cleveragents-core/docs/specification.md

CleverAgents Documentation (Detailed Spec)

Key Themes

Key themes include:

• A four-phase plan lifecycle (Action → Strategize → Execute → Apply).
• Actors as a unifying abstraction (an LLM/agent or a whole graph).
• A sandbox + diff review workflow and CLI-first interaction model.
• A scalable context/memory architecture (hot/warm/cold tiers, per-actor views).
• Invariants as first-class constraints (global, project, action, and plan scoped) that flow into the decision tree, with precedence-based conflict resolution via the Invariant Reconciliation Actor.
• A future-facing correction model where the user can "edit the decision tree" and only recompute affected subtrees.

Big Picture: What CleverAgents is

CleverAgents is your command center for AI agents—a unified platform for orchestrating any task you want agents to accomplish, from developing large software projects to writing comprehensive technical papers, administering databases, managing cloud infrastructure, or any complex multi-step workflow. The core value proposition is enabling long-running, complex, large-scale tasks to execute autonomously with minimal human intervention, making it ideal for building entire software systems, producing extensive documentation, or managing sophisticated operations largely hands-off.

In server mode, CleverAgents becomes a collaborative hub where teams can share resources—prompts, actors, actions, and projects—while executing plans in the cloud. This enables a consistent experience across all your devices: start a complex task on your laptop, check progress from your phone, and review results from any machine.

While CleverAgents leverages LangGraph and LangChain for the underlying LLM runtime primitives (tool calling, graphs, routing), its value lies in what it builds on top:

CleverAgents provides:

• A first-class plan lifecycle (Action/Strategize/Execute/Apply) for breaking down and tracking complex work,
• A project + resource model for grounding tasks in real codebases, databases, documents, and infrastructure,
• A consistent actor abstraction for defining and composing intelligent agents,
• An independently registered resource abstraction for representing anything that can be read, written, or queried,
• An independently registered tool abstraction for reusable, callable operations with resource bindings,
• A consistent skill abstraction for organizing tools into composable capability collections,
• A sandbox + checkpoint safety model for safe, reversible execution,
• A CLI/TUI/Web UX for controlling and monitoring large multi-step autonomous work.

Glossary (Terms Used Precisely)

• Plan: A tracked lifecycle for a single unit-of-work (which may spawn child plans). Plans follow the same namespace rules as actors.
• Action: A reusable plan template not tied to any project yet. Created via CLI commands with a required --config YAML configuration file that fully defines the action. CLI options provided alongside the config act as optional overrides. Actions follow the same namespace rules as actors.
• Strategize: Read-only planning phase that produces a strategy and child plan blueprint. All decisions are made during this phase.
• Execute: Phase that performs work in a sandbox; spawns child plans (based on decisions made in Strategize); produces artifacts/diffs.
• Apply: Phase that commits sandbox results into the real project (and records an "applied" plan state).
• Project: A collection of linked resources + configuration that define "where work happens" and "what can be touched." Created via CLI commands. Projects link to independently registered resources from the Resource Registry (they do not define resources inline). A resource can be linked to multiple projects. Can be local (contains local-only resources) or remote (all resources remotely accessible).
• Resource: A namespaced, independently registered entity representing anything that can be read, written, or queried. Resources are managed through the agents resource CLI commands and stored in the Resource Registry. Each resource has a resource type that determines its properties, allowed parent/child relationships, sandbox strategy, and handler. Resources form a directed acyclic graph (DAG) with parent/child relationships. Resources are either physical (a specific, concrete manifestation — THIS file at THIS path, THIS git repository at THIS URL, THIS commit in THIS repo) or virtual (an abstract identity linking equivalent physical resources — "these two fs-file resources and this git-tree-entry resource all represent the same file with the same content, name, and permissions"). Two physical resources share a virtual parent when they are equivalent by the virtual type's criteria; when equivalence breaks, the virtual relationship is updated. Resources follow the same <namespace>/<name> naming convention as other entities. Extends the MCP resource concept to support both read and write operations.
• Resource Type: A schema-level definition that constrains a category of resources. A resource type defines: what CLI arguments agents resource add <type> accepts, whether instances are physical or virtual, what child/parent resource types are allowed, what child resources are auto-discovered when an instance is created, the sandbox strategy, and the resource handler implementation. Built-in physical types span three layers: git metadata (git, git-remote, git-branch, git-tag, git-commit, git-tree, git-tree-entry, git-stash, git-submodule), git checkout (git-checkout), and filesystem (fs-mount, fs-directory, fs-file, fs-symlink, fs-hardlink). Built-in virtual types (file, directory, symlink, commit, branch, tag, remote, submodule, tree) link equivalent physical resources through content/identity matching. Custom types are defined in YAML configuration files and registered via agents resource type add. Resource types also specify whether instances can be created directly by users (user_addable: true) or are only auto-generated as children of other resources.
• Physical Resource: A resource representing a specific, concrete manifestation — a particular file at a particular path, a particular git repository at a particular URL, a particular commit in a particular repo. Physical resources exist somewhere (locally on disk, or remotely on a server) and can be directly read and written by tools. Every physical resource is a distinct instance, even if its content is identical to another physical resource. A physical resource's parents can be either physical or virtual resources.
• Virtual Resource: A resource representing an abstract identity that links equivalent physical resources. Virtual resources have no location of their own — they serve as shared parents of physical resources that are "the same" by some equivalence criterion (same content, same name, same permissions, same hash, etc.). Not all physical resources have a virtual parent. The children of a virtual resource can be physical resources, other virtual resources, or both. Built-in virtual types use simple names that mirror their physical counterparts: file (links fs-file + git-tree-entry with same content, filename, and permissions), directory (links fs-directory + git-tree with same recursive content), symlink (links fs-symlink + git-tree-entry mode 120000 with same target), commit (links git-commit across repos with same commit hash), branch (links git-branch across repos with same name and HEAD), tag (links git-tag across repos with same name and target), remote (links git-remote across repos with same URL), submodule (links git-submodule across repos with same URL and path), tree (links git-tree across repos with same tree hash).
• Resource Binding: The declared association between a tool and the resources it operates on. A tool declares resource slots — typed placeholders specifying what resource types the tool requires (e.g., "I need a git-checkout resource with read_write access"). Slots are resolved to actual resources at activation time (contextual binding — from the plan's project), at registration time (static binding — hardcoded to a specific resource), or at invocation time (parameter binding — passed as a tool argument).
• Resource Registry: A persistent catalog of all independently registered resources and their DAG relationships (parent/child links). Managed via agents resource add/remove/list/show CLI commands. The Resource Registry works alongside the Tool Registry and Skill Registry.
• Skill: A namespaced, reusable collection of tools registered in the system via its own YAML configuration file and managed through the agents skill CLI commands. A skill assembles tools by referencing named tools from the Tool Registry, defining anonymous inline tools, and/or including other skills (whose tools are merged in). Skills can also expose tools from MCP servers, Agent Skills Standard folders, and built-in tool groups. When referencing named tools or including sub-skills, metadata can optionally be overridden. Actors reference skills by fully-qualified name. Extends the MCP standard and Agent Skills standard. Skills follow the same namespace rules as actors, tools, actions, etc.
• Tool: A namespaced, independently registered, callable operation. A tool is the atomic unit of execution — it has a name, a JSON Schema for inputs/outputs, capability metadata (read-only, writes, checkpointable, etc.), and a uniform lifecycle (discover, activate, execute, deactivate). Tools are defined in their own YAML configuration files and managed through the agents tool CLI commands, following the same <namespace>/<name> naming convention as other entities. Tools originate from one of four sources: MCP servers (exposed via JSON-RPC tools/call), Agent Skills Standard folders (instruction-driven SKILL.md bundles), built-in operations (first-party file, git, search, and directory operations), or custom code (Python). Tools have a dual role: they can be included in skills (by reference) to form reusable capability collections, and they can be used directly as tool nodes in actor graphs. When referencing a named tool in a skill or actor, its metadata can optionally be overridden at the point of use.
• Anonymous Tool: An inline tool definition (Python code block) that appears directly inside a skill YAML or an actor graph node. Anonymous tools use the same format as a named tool's YAML definition but lack a namespaced name — they are not registered in the Tool Registry, cannot be reused across skills or actors, and exist only within the scope where they are defined. Anonymous tools are useful for one-off, context-specific operations.
• Actor: Anything conversational; may be a single agent/LLM or an entire graph of actors/tools. Defined via YAML configuration files (LangGraph definitions). Always named using <namespace>/<name> format.
• Session: A user interaction context and conversation thread that can span multiple plans.
• Server: Optional shared service for multi-user storage, permissions, and orchestration. Plans on remote projects can execute on the server.
• Namespace: Scoping mechanism for actors, tools, skills, resources, resource types, actions, plans, automation profiles, etc. local/ is reserved for local-only items. User namespaces (<username>/) and organization namespaces (<orgname>/) are stored on the server. Built-in LLM actors use provider namespaces (e.g., openai/, anthropic/). Built-in resource types use no namespace prefix (e.g., git, git-checkout, fs-mount, fs-directory, file, commit).
• Decision: A recorded choice point made during Strategize that affects downstream work. Decisions form a tree structure that enables correction and replay.
• Invariant: A named constraint or rule that applies to plan execution. Invariants can be attached at four scopes: global (applies to all plans), project (applies to all plans targeting that project), plan (applies to a specific plan and its child plans), or action (carried forward as plan-level invariants when the action is used). Managed via the unified agents invariant add/list/remove command, or attached at creation time via --invariant flags on agents project create, agents action create, and agents plan use. Precedence: plan-level invariants override project-level, which override global-level, when conflicts exist. An Invariant Reconciliation Actor (set via --invariant-actor on actions, plans, and projects, or globally via agents config set invariant-actor) resolves conflicts and computes the effective invariant view when a plan enters Strategize. During Strategize, the effective invariants are recorded as invariant_enforced decisions in the decision tree. When a top-level plan spawns child plans, the parent's effective invariant view is passed down to each child plan.
• Automation Profile: A named collection of boolean flags that controls which tasks are automated vs. require human approval during plan execution. Built-in profiles (locked-down, manual, supervised, trusted, autonomous, full-auto) cover the spectrum from maximum human control to full automation. Custom profiles are defined in YAML configuration files, registered via agents automation-profile add, and follow the same <namespace>/<name> naming convention as other entities. Profiles control phase transitions, decision automation, validation self-fix behavior, strategy revision, child plan spawning, and safety requirements.
• ULID: Universally Unique Lexicographically Sortable Identifier. Preferred over UUID for plan and decision IDs due to time-sortability.

CLI Commands

Command Synopsis

agents|cleveragents [--data-dir <DATA_PATH>] [--config-path <CONFIG_PATH>]
                     [--format (rich|color|table|plain|json|yaml)]
                     [--help|-h] [--version]
                     [--install-completion [<INST_SHELL>]] [--show-completion [<SHOW_SHELL>]]
                     [-v...] <COMMAND> [<ARGS>...]
agents version
agents info
agents diagnostics
agents init [--yes|-y]
agents session create [--actor <ACTOR>]
agents session list
agents session show <SESSION_ID>
agents session delete [--yes|-y] <SESSION_ID>
agents session export [(--output|-o) <FILE>] <SESSION_ID>
agents session import (--input|-i) <FILE>
agents session tell --session <SESSION_ID> [--actor <ACTOR>] [--stream] <PROMPT>
agents project create [(--description|-d) <DESC>] [--resource <RESOURCE>]...
[--invariant <INVARIANT>]... [--invariant-actor <ACTOR>] <NAME>
agents project link-resource [--read-only] <PROJECT> <RESOURCE>
agents project unlink-resource [--yes|-y] <PROJECT> <RESOURCE>
agents project list [(--namespace|-n) NS] [<REGEX>]
agents project show <PROJECT>
agents project validation add [(--description|-d) <DESC>] [--required|--informational]
[--timeout <SECONDS>] [--resource <RESOURCE>] <PROJECT> <COMMAND>
agents project validation remove [--yes|-y] <PROJECT> <VALIDATION_ID>
agents project validation list <PROJECT>
agents project delete [--force|-f] [--yes|-y] <NAME>
agents project context set[--view (strategize|execute|apply|default)]
[--include-resource <INCLUDE_RESOURCE>]...
[--exclude-resource <EXCLUDE_RESOURCE>]...
[--include-path <INCLUDE_GLOB>]...
[--exclude-path <EXCLUDE_GLOB>]...
[--hot-max-tokens <N>]
[--warm-max-decisions <N_WARM_MAX>]
[--cold-max-decisions <N_COLD_MAX>]
[--query-limit <N>]
[--max-file-size <MAX_FILE_BYTES>]
[--max-total-size <MAX_TOTAL_BYTES>]
[--summarize|--no-summarize]
[--summary-max-tokens <N>]
[--clear] <PROJECT>
agents project context show [--view (strategize|execute|apply|default)]
<PROJECT>
agents actor run [(--output|-o) <OUTPUT_FILE>]
[-v...] [--unsafe|-u] [--context <CONTEXT_NAME>]
[--context-dir <CONTEXT_PATH>] [--load-context <LOAD_CONTEXT_NAME>]
[(--temperature|-t) <TEMP>] [--allow-rxpy-in-run-mode]
[--skill <SKILL>]... <NAME> <PROMPT>
agents actor add (--config|-c) <FILE> [--unsafe] [(--option|-o) <key>=<value>]...
[--type (graph|agent)] [--update]
[--skill <SKILL>]... [(--description|-d) <DESC>] [<NAME>]
agents actor remove <NAME>
agents actor list
agents actor show <NAME>
agents actor context rm [--yes|-y] (--all|-a|<NAME>)
agents actor context list [<REGEX>]
agents actor context show <NAME>
agents actor context export (--output|-o) <FILE> <NAME>
agents actor context import [--update] (--input|-i) <FILE> [<NAME>]
agents actor context delete [--yes|-y] (--all|-a|<NAME>)
agents actor context clear [--yes|-y] (--all|-a|<NAME>)
agents skill add (--config|-c) <FILE> [(--description|-d) <DESC>] [--update]
[--tool <TOOL>]... [--include-skill <INCLUDE_SKILL>]... [--mcp-server <SPEC>]... [<NAME>]
agents skill remove [--yes|-y] <NAME>
agents skill list [(--namespace|-n) <NS>] [--source <SOURCE>]
agents skill show  <NAME>
agents skill tools <NAME>
agents tool add (--config|-c) <FILE> [(--description|-d) <DESC>] [--update]
[--source <SOURCE>] [--input-schema <JSON>] [--code <CODE>]
[--writes|--no-writes] [--checkpointable|--no-checkpointable] [<NAME>]
agents tool remove [--yes|-y] <NAME>
agents tool list [(--namespace|-n) <NS>] [--source <SOURCE>] [<REGEX>]
agents tool show <NAME>
agents resource type add (--config|-c) <FILE> [--update]
[--physical|--virtual] [--user-addable|--no-user-addable]
[--sandbox-strategy <STRATEGY>] [--handler <NAME>]
[--child-type spec]... [--cli-arg spec]... [<NAME>]
agents resource type remove [--yes|-y] <NAME>
agents resource type list [<REGEX>]
agents resource type show <NAME>
agents resource add [(--description|-d) <DESC>] [--update] <TYPE> <NAME> [type-specific-flags...]
agents resource remove [--yes|-y] <NAME>
agents resource list [(--namespace|-n) <NS>] [(--type|-t) <TYPE>] [<REGEX>]
agents resource show <NAME>
agents resource tree [(--depth|-d) <N>] [(--type|-t) <TYPE>] <NAME>
agents resource link-child <PARENT> <CHILD>
agents resource unlink-child [--yes|-y] <PARENT> <CHILD>
agents plan list [--phase <PHASE>] [--state <STATE>] [--project <PROJECT>]
[--action <ACTION>] [<REGEX>]
agents plan use [--automation-profile <PROFILE>]
[--invariant <INVARIANT>]...
[--strategy-actor <STRATEGY_ACTOR>]
[--execution-actor <EXEC_ACTOR>]
[--estimation-actor <EST_ACTOR>]
[--invariant-actor <INV_ACTOR>]
[--arg/-a name=value]...
<ACTION> <PROJECT>...
agents plan execute <PLAN_ID>
agents plan apply [--yes|-y] <PLAN_ID>
agents plan status <PLAN_ID>
agents plan cancel [(--reason|-r) <REASON>] <PLAN_ID>
agents plan tree [--show-superseded] <PLAN_ID>
agents plan explain [--show-context] [--show-reasoning] <DECISION_ID>
agents plan correct --mode (revert|append) (--guidance|-g) <GUIDANCE>
[--dry-run] [--yes|-y] <DECISION_ID>
agents plan diff (--correction <CORRECTION_ATTEMPT_ID>|<PLAN_ID>)
agents plan artifacts <PLAN_ID>
agents plan prompt <PLAN_ID> <GUIDANCE>
agents plan rollback [--yes|-y] <PLAN_ID> <CHECKPOINT_ID>
agents action create (--config|-c) <CFG_FILE>
[--strategy-actor <STRATEGY_ACTOR>]
[--execution-actor <EXEC_ACTOR>]
[--definition-of-done <DOD>]
[(--description|-d) <DESC>]
[--long-description <LONG_DESC>]
[--reusable|--no-reusable]
[--read-only]
[--available]
[--estimation-actor <EST_ACTOR>]
[--invariant-actor <INV_ACTOR>]
[--automation-profile <PROFILE>]
[--invariant <INVARIANT>]...
[--arg <ARG_SPEC>]...
[<NAME>]
agents action list [(--namespace|-n) <NS>] [(--state|-s) <STATE>] [--available] [<REGEX>]
agents action show <ACTION_NAME>
agents action available <ACTION_NAME>
agents action archive <ACTION_NAME>
agents automation-profile add (--config|-c) <FILE> [--update] [<NAME>]
agents automation-profile remove [--yes|-y] <NAME>
agents automation-profile list [<REGEX>]
agents automation-profile show <NAME>
agents config set <key> <value>
agents config get <key>
agents config list [--filter-values <REGEX>] [<REGEX>]
agents invariant add [--global] [(--project|-p) PROJECT] [--plan PLAN_ID]...
[--action ACTION]... <INVARIANT_TEXT>
agents invariant list [--global] [(--project|-p) PROJECT] [--plan PLAN_ID] [--action ACTION]
[--effective] [<REGEX>]
agents invariant remove [--yes|-y] <INVARIANT_ID>

Command Reference

Global Options

Purpose Configure global state locations and shell integration for every command.

Arguments

• --data-dir PATH: Overrides the global data directory (database, caches, sessions, logs). When omitted, the default data location is used.
• --config-path PATH: Overrides the global configuration file path. When omitted, the default config path is used.
• --format rich|color|table|plain|json|yaml: Set the output rendering format for all subcommands. When omitted, the value is read from the global config key format. If not set in config either, defaults to rich. See Output Rendering Framework for full details on each format. The available formats are:
  • rich (default): Uses modern rich CLI elements with dynamic effects, animated spinners, progress bars, and generous color. Best for interactive terminal use.
  • color: Plain scrolling text with ANSI color codes. Good for terminals that support color but not advanced rendering.
  • table: ASCII box-drawing characters to create structured tables and panels with color. Similar to the examples shown throughout this document.
  • plain: Plain text with no color codes or non-ASCII characters. Suitable for piping into files, logs, or non-terminal consumers.
  • json: Structured JSON output for programmatic consumption. No color except within verbatim text values.
  • yaml: Structured YAML output for programmatic consumption. No color except within verbatim text values.
• --help, -h: Print help for the current command.
• --version, -V: Print the version and exit.
• --install-completion [SHELL]: Install shell completion for the given shell.
• --show-completion [SHELL]: Show the completion script for the given shell.

Examples

$ agents --data-dir /srv/cleveragents --config-path /srv/cleveragents/config.toml info
╭─ System Snapshot ─────────────────╮
│ CleverAgents 1.0.0                │
│ Mode: local                       │
│ Automation: review                │
│ Status: ready                     │
╰───────────────────────────────────╯
╭─ Paths ───────────────────────────────╮
│ Data Dir: /srv/cleveragents           │
│ Config: /srv/cleveragents/config.toml │
│ Logs: /srv/cleveragents/logs          │
│ Cache: /srv/cleveragents/cache        │
│ Database: /srv/cleveragents/agents.db │
╰───────────────────────────────────────╯
╭─ Runtime ──────────╮
│ PID: 4127          │
│ Uptime: 00:14:32   │
│ Python: 3.13.1     │
│ Host: devbox.local │
│ Platform: linux    │
╰────────────────────╯
╭─ Projects & Sessions ─╮
│ Projects: 2          │
│ Sessions: 1 active  │
│ Active Plans: 0     │
│ Actors: 3           │
╰───────────────────────╯
✓ OK Environment loaded

agents version

Purpose Print the current CLI version.

Arguments

None.

Examples

$ agents version
╭─ CLI Version ────╮
│ CleverAgents CLI │
│ Version: 1.0.0   │
│ Channel: stable  │
│ Python: 3.13     │
╰──────────────────╯
╭─ Build ────────────────╮
│ Build Date: 2026-02-08 │
│ Commit: a17c3f9        │
│ Schema: v3             │
│ Platform: linux-x86_64 │
╰────────────────────────╯
╭─ Dependencies ─────────────────╮
│ LangGraph: 0.2.60           │
│ LangChain: 0.3.18          │
│ MCP SDK: 1.4.0             │
│ Pydantic: 2.10.4           │
╰────────────────────────────────╯
✓ OK Version reported

agents info

Purpose Show configuration and runtime information useful for debugging or support.

Arguments

None.

Examples

$ agents info
╭─ Environment ───────────────────────────────────────────────╮
│ Data Dir: /home/alex/.cleveragents                          │
│ Config: /home/alex/.cleveragents/config.toml                │
│ Database: sqlite:///home/alex/.cleveragents/cleveragents.db │
│ Server Mode: disabled                                       │
│ Platform: Linux 6.8.0 (x86_64)                              │
╰─────────────────────────────────────────────────────────────╯
╭─ Runtime ─────────────────────────╮
│ Automation: review                │
│ Providers: 3 configured           │
│ Sessions: 2 active                │
│ Active Plans: 1                   │
╰───────────────────────────────────╯
╭─ Storage ─────╮
│ Cache: 118 MB │
│ Logs: 42 MB   │
│ Backups: 3    │
│ DB Size: 8 MB │
╰───────────────╯
╭─ Indexing ─────────────╮
│ Text Index: ready     │
│ Vector Index: ready   │
│ Graph Store: disabled │
│ Indexed Files: 1,247  │
╰────────────────────────╯
✓ OK Environment details ready

agents diagnostics

Purpose Run health checks for configuration, providers, and filesystem permissions.

Arguments

None.

Examples

$ agents diagnostics
╭─ Checks ────────────────────────────────╮
│ Check            Status  Details        │
│ ───────────────  ──────  ────────────── │
│ Config file      OK      readable       │
│ Database         OK      writable       │
│ OPENAI_API_KEY   WARN    missing        │
│ Anthropic key    OK      configured     │
│ Disk space       OK      2.1 GB free    │
│ Text index       OK      tantivy 0.22   │
│ Vector index     OK      faiss (CPU)    │
│ Graph store      WARN    not configured │
│ File permissions OK      data dir r/w   │
│ Git              OK      git 2.43.0     │
╰────────────────────────────────────────╯
╭─ Summary ────────╮
│ Checks: 10 total │
│ Warnings: 2      │
│ Errors: 0         │
│ Duration: 0.6s   │
╰──────────────────╯
╭─ Recommendations ─────────────────────────────────────────────╮
│ - Set OPENAI_API_KEY to enable OpenAI models                  │
│ - Configure a graph store backend for structural code queries │
│ - Verify provider credentials via config                      │
╰───────────────────────────────────────────────────────────────╯
⚠ WARN 2 warnings require attention

agents init

Purpose Initialize or reset the global CleverAgents environment. This wipes any existing data and re-creates the global config and database.

Arguments

• --yes: Skip the confirmation prompt and proceed with the wipe.

Examples

$ agents init
Warning: This will remove all data in /home/alex/.cleveragents
Continue? [y/N]: y
╭─ Environment Reset ────────────────────────────────╮
│ Config: /home/alex/.cleveragents/config.toml       │
│ Database: /home/alex/.cleveragents/cleveragents.db │
│ Backup: /home/alex/.cleveragents.backup-2026-02-08 │
│ Status: ready                                      │
╰────────────────────────────────────────────────────╯
╭─ Defaults ──────────────────────────────────╮
│ Automation Profile: supervised              │
│ Built-in Profiles: 6 loaded                 │
╰─────────────────────────────────────────────╯
╭─ Created ─────────────────╮
│ Config: config.toml       │
│ Database: cleveragents.db │
│ Logs: logs/               │
│ Cache: cache/             │
│ Backups: backups/         │
╰───────────────────────────╯
╭─ Schema ───────────────────────╮
│ Version: v3                   │
│ Tables: 12 created            │
│ Migrations: up to date        │
╰────────────────────────────────╯
✓ OK Environment initialized

agents session

Purpose Manage interactive sessions that hold a conversation history and orchestrator state.

agents session create

Purpose Create a new session for interactive work.

Arguments

• --actor ACTOR: Orchestrator actor to use for session tell.

Examples

$ agents session create --actor local/orchestrator
╭─ Session ──────────────────────╮
│ ID: 01HXM2A6K1P2E9Q9D4GQ7J4S7Z │
│ Actor: local/orchestrator      │
│ Created: 2026-02-08 12:44      │
│ Namespace: local                │
╰────────────────────────────────╯
╭─ Settings ─────────────╮
│ Automation: review     │
│ Streaming: off         │
│ Context: default       │
│ Memory: enabled        │
│ Max History: 50 turns │
╰────────────────────────╯
╭─ Actor Details ───────────────────╮
│ Provider: anthropic              │
│ Model: claude-3.5                │
│ Temperature: 0.7                 │
│ Context Window: 200K tokens      │
╰───────────────────────────────────╯
✓ OK Session created

agents session list

Purpose List sessions available on the local machine.

Arguments

None.

Examples

$ agents --format table session list
╭─ Sessions ───────────────────────────────────────────────────────────────────╮
│ ID        Name             Actor               Messages  Updated          │
│ ────────  ───────────────  ──────────────────  ────────  ──────────────── │
│ 01HXM2A6  weekly-planning  local/orchestrator  6         2026-02-08 12:44 │
│ 01HXM1F2  refactor-sprint  local/orchestrator  14        2026-02-07 18:11 │
╰──────────────────────────────────────────────────────────────────────────────╯
╭─ Summary ────────────────────╮
│ Total: 2                     │
│ Most Recent: weekly-planning │
│ Oldest: refactor-sprint      │
│ Total Messages: 20           │
│ Storage: 42 KB               │
╰──────────────────────────────╯
✓ OK 2 sessions listed

agents session show

Purpose Show details and recent messages for a session.

Arguments

• <SESSION_ID>: The session identifier.

Examples

$ agents session show 01HXM2A6K1P2E9Q9D4GQ7J4S7Z
╭─ Session Summary ──────────────╮
│ ID: 01HXM2A6K1P2E9Q9D4GQ7J4S7Z │
│ Actor: local/orchestrator      │
│ Messages: 6                    │
│ Created: 2026-02-08 12:30      │
│ Updated: 2026-02-08 12:44      │
│ Automation: review              │
╰────────────────────────────────╯
╭─ Recent Messages ──────────────────────────────────╮
│ user  Create an action to refresh dependency locks │
│ assistant  Plan created, running commands...       │
│ assistant  Completed 2 commands                    │
╰────────────────────────────────────────────────────╯
╭─ Linked Plans ──────────────────────────────╮
│ Plan ID                     Phase   State    │
│ ──────────────────────────  ──────  ──────── │
│ 01HXM8C2ZK4Q7C2B3F2R4VYV6J  execute  complete │
╰─────────────────────────────────────────────╯
╭─ Token Usage ──────────────╮
│ Input Tokens: 3,420       │
│ Output Tokens: 1,185     │
│ Estimated Cost: $0.0184  │
╰────────────────────────────╯
✓ OK Session details loaded

agents session delete

Purpose Delete a session and its stored conversation history.

Arguments

• <SESSION_ID>: The session identifier.
• --yes, -y: Skip the confirmation prompt.

Examples

$ agents session delete 01HXM2A6K1P2E9Q9D4GQ7J4S7Z
Delete session 01HXM2A6K1P2E9Q9D4GQ7J4S7Z? [y/N]: y
╭─ Deletion Summary ─────────────╮
│ Session: 01HXM2A6K1P2E9Q9D4GQ7J4S7Z │
│ ID: 01HXM2A6K1P2E9Q9D4GQ7J4S7Z │
│ Messages: 6 removed            │
│ Storage: 18 KB freed           │
│ Plans Orphaned: 0              │
╰────────────────────────────────╯
╭─ Cleanup ───────────╮
│ Backups: none       │
│ Logs: preserved     │
│ Context: cleared    │
│ Checkpoints: none  │
╰─────────────────────╯
✓ OK Session deleted

agents session export

Purpose Export a session as a portable JSON file.

Arguments

• <SESSION_ID>: The session identifier.
• --output/-o FILE: Output file path (optional).

Examples

$ agents session export --output /tmp/weekly-planning.json 01HXM2A6K1P2E9Q9D4GQ7J4S7Z
╭─ Session Export ──────────────────╮
│ Session: 01HXM2A6K1P2E9Q9D4GQ7J4S7Z │
│ Output: /tmp/weekly-planning.json │
│ Messages: 6                       │
│ Size: 24 KB                       │
│ Format: JSON                      │
╰───────────────────────────────────╯
╭─ Contents ─────────────────╮
│ Messages: 6               │
│ Plan References: 1       │
│ Metadata Keys: 2         │
│ Actor Config: included   │
│ Schema Version: v3       │
╰────────────────────────────╯
╭─ Integrity ──────────────────╮
│ Checksum: sha256:7a9b...42c1 │
│ Encrypted: no                │
╰──────────────────────────────╯
✓ OK Export completed

agents session import

Purpose Import a session JSON file.

Arguments

• --input/-i FILE: Input JSON file.

Examples

$ agents session import --input /tmp/weekly-planning.json
╭─ Session Import ───────────────────────╮
│ Input: /tmp/weekly-planning.json       │
│ Session ID: 01HXM3D3B2W4CQYQ3P4ZB8A5T1 │
│ Messages: 6                            │
│ Schema: v3                              │
╰────────────────────────────────────────╯
╭─ Validation ────────────╮
│ Checksum: verified     │
│ Schema: compatible     │
│ Actor Ref: resolved    │
╰─────────────────────────╯
╭─ Merge ──────────────╮
│ Existing: none       │
│ Strategy: create new │
╰──────────────────────╯
✓ OK Import completed

agents session tell

Purpose Send a natural-language request to the orchestrator. The orchestrator can create actions, plans, or project changes by issuing the necessary CleverAgents commands under the hood.

Arguments

• <PROMPT>: Instruction text (positional argument).
• --session SESSION_ID: Session to use (required).
• --actor ACTOR: Override the session actor for this request.
• --stream: Stream progress as the orchestrator works.

Examples

$ agents session tell "Create an action to refresh dependency locks and add it to the platform project" \
  --session 01HXM2A6K1P2E9Q9D4GQ7J4S7Z
╭─ Plan Request ──────────────────────────────────────────╮
│ Actor: local/orchestrator                               │
│ Session: 01HXM2A6K1P2E9Q9D4GQ7J4S7Z                     │
│ Automation: review                                      │
│ Prompt: Create an action to refresh dependency locks... │
╰─────────────────────────────────────────────────────────╯
╭─ Commands Executed ────────────────────────────────────────────────────────────────────────────────────────╮
│ - agents action create --config ./actions/refresh-locks.yaml local/refresh-locks │
│ - agents resource add git-checkout local/platform-repo --path /repos/platform                               │
│ - agents project link-resource local/platform local/platform-repo                     │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Result ────────────────────────────╮
│ Action: local/refresh-locks (draft) │
│ Project: local/platform             │
│ Resource: repo                      │
╰─────────────────────────────────────╯
╭─ Usage ─────────────────────╮
│ Input Tokens: 1,842       │
│ Output Tokens: 624       │
│ Cost: $0.0094             │
│ Duration: 3.2s             │
│ Tool Calls: 3             │
╰─────────────────────────────╯
✓ OK Orchestrator completed 3 commands

agents project

Purpose Manage projects and their resources.

agents project create

Purpose Create a new project record.

Arguments

• <NAME>: Namespaced project name (positional argument).
• --description/-d TEXT: Optional description.
• --resource RESOURCE: Resource to link to the project at creation time (repeatable).
• --invariant TEXT: Invariant to attach to this project (repeatable). These invariants apply to all plans targeting this project.
• --invariant-actor ACTOR: Invariant Reconciliation Actor for this project. Used to reconcile project-level invariants against global invariants for all plans targeting this project (unless the plan overrides it).

Examples

$ agents project create --description "Backend API" local/api-service
╭─ Project ──────────────────────╮
│ Name: local/api-service        │
│ ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1 │
│ Description: Backend API       │
│ Type: local                    │
│ Created: 2026-02-08 12:46      │
╰────────────────────────────────╯
╭─ Paths ────────────────────────────────────╮
│ Root: /repos/api-service                   │
│ Data Dir: /repos/api-service/.cleveragents │
╰────────────────────────────────────────────╯
╭─ Defaults ──────────────────────────────╮
│ Sandbox: git_worktree                   │
│ Validations: 0                         │
│ Context Filters: none                   │
│ Automation Profile: (inherits global)  │
╰─────────────────────────────────────────╯
╭─ Resources ──────╮
│ Total: 0        │
│ Indexed: 0      │
│ Sandboxable: 0 │
╰──────────────────╯
✓ OK Project created

agents project link-resource

Purpose Link a registered resource to a project. The resource must already be registered via agents resource add. A resource can be linked to multiple projects. Optionally, the resource can be marked as read-only within this project context, and given an alias for convenience.

Arguments

• <PROJECT>: Project name (positional argument).
• <RESOURCE>: Registered resource name (positional argument).
• --read-only: Mark the resource as read-only within this project (even if the resource itself is writable).

Examples

$ agents resource add git-checkout local/api-repo --path /repos/api --branch main
$ agents project link-resource local/api-service local/api-repo
╭─ Resource Linked ──────────────────────╮
│ Project: local/api-service            │
│ Resource: local/api-repo               │
│ Type: git-checkout                     │
│ Read-Only: no                           │
╰────────────────────────────────────────╯
╭─ Permissions ────────────╮
│ Read: allowed            │
│ Write: allowed           │
│ Apply: requires approval │
╰──────────────────────────╯
╭─ Indexing ─────────────────────╮
│ Status: indexing...          │
│ Files Found: 347             │
│ Language: Python (primary)   │
│ Estimated Time: ~20 seconds  │
╰────────────────────────────────╯
✓ OK Resource linked to project

agents project unlink-resource

Purpose Unlink a resource from a project. The resource itself remains registered in the Resource Registry and can still be linked to other projects. Active plans using this resource in this project context will be warned.

Arguments

• <PROJECT>: Project name (positional argument).
• <RESOURCE>: Resource name (positional argument).
• --yes, -y: Skip confirmation prompt.

Examples

$ agents project unlink-resource local/api-service local/api-repo
Unlink local/api-repo from local/api-service? [y/N]: y
╭─ Resource Unlinked ────────────────────╮
│ Project: local/api-service            │
│ Resource: local/api-repo               │
│ Type: git-checkout                     │
╰────────────────────────────────────────╯
╭─ Index Cleanup ──────────╮
│ Text Index: 347 removed │
│ Vectors: 892 removed    │
│ Graph Triples: cleared  │
│ Duration: 0.3s          │
╰──────────────────────────╯
╭─ Project Summary ──────────────╮
│ Linked Resources: 1 remaining  │
│ Last Updated: 2026-02-09 10:30 │
│ Active Plans: 0                │
╰────────────────────────────────╯
✓ OK Resource unlinked from project

agents project list

Purpose List projects with optional filters.

Arguments

• --namespace/-n NS: Filter by namespace.

Examples

$ agents --format table project list
╭─ Projects ────────────────────────────────────────────────────────╮
│ ID        Name               Resources  Remote  Active Plans │
│ ────────  ─────────────────  ─────────  ──────  ──────────── │
│ 01HXM4T0  local/api-service  2          No      1            │
│ 01HXM4B9  local/docs         1          No      0            │
╰───────────────────────────────────────────────────────────────────╯
╭─ Summary ──────────────────╮
│ Total: 2                   │
│ With Resources: 2          │
│ Remote: 0                  │
│ Total Resources: 3         │
│ Indexed Files: 1,247       │
│ Active Plans: 1            │
╰────────────────────────────╯
✓ OK 2 projects listed

agents project show

Purpose Show full project details.

Arguments

• <NAME>: Project name.

Examples

$ agents project show local/api-service
╭─ Project Details ──────────────╮
│ Name: local/api-service        │
│ ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1 │
│ Description: Backend API       │
│ Resources: 2                   │
│ Remote: no                     │
│ Created: 2026-02-08 12:46      │
╰────────────────────────────────╯
╭─ Linked Resources ──────────────────────────────────────────────────────╮
│ Resource          Type            Sandbox              Read-Only       │
│ ────────────────  ──────────────  ────────────────────  ─────────       │
│ local/api-repo   git-checkout    git_worktree         no              │
│ local/staging-db local/database  transaction_rollback yes             │
╰─────────────────────────────────────────────────────────────────────────╯
╭─ Validations (3) ───────────────────────────────────────────╮
│ val_01HXM5A  pytest --cov=src --cov-fail-under=80  required │
│ val_01HXM5B  ruff check .                          required │
│ val_01HXM5C  node scripts/check-bundle-size.js     info     │
╰─────────────────────────────────────────────────────────────╯
╭─ Context ───────────────────╮
│ Include: repo               │
│ Exclude: /node_modules/ │
│ Max File Size: 1 MB        │
╰─────────────────────────────╯
╭─ Indexing Status ──────────╮
│ Text Index: ready         │
│ Vector Index: ready       │
│ Graph Store: disabled     │
│ Indexed Files: 347        │
│ Last Indexed: 12:48       │
╰────────────────────────────╯
╭─ Active Plans ──────────────────────────╮
│ Plan ID   Action               Phase    │
│ ────────  ───────────────────  ─────── │
│ 01HXM7A9  local/code-coverage  execute │
╰─────────────────────────────────────────╯
✓ OK Project loaded

agents project validation

Purpose Manage validations for a project. Validations are commands that run during the Execute phase to verify the correctness of changes. Each validation has an optional description, a command to execute, and a mode (required or informational). Required validations must pass for execution to proceed; informational validations are included in the summary but do not block execution.

agents project validation add

Purpose Add a validation to a project.

Arguments

• <PROJECT>: Project name (positional argument).
• <COMMAND>: The shell command to run (positional argument, quoted string).
• --description/-d TEXT: Optional description of what this validation checks.
• --required/--informational: Whether the validation must pass (default: --required). Required validations block execution on failure; informational validations report results without blocking.
• --timeout SECONDS: Timeout for this validation command (default: 300).
• --resource RESOURCE: Scope this validation to a specific linked resource.

Examples

$ agents project validation add --description "Run unit tests with coverage" \
  --required --timeout 600 --resource repo local/api-service "pytest --cov=src --cov-fail-under=80"
╭─ Validation Added ──────────────────────────────────────────╮
│ ID: val_01HXM5A                                            │
│ Project: local/api-service                                │
│ Command: pytest --cov=src --cov-fail-under=80             │
│ Description: Run unit tests with coverage                  │
│ Mode: required                                            │
│ Resource: repo                                            │
│ Timeout: 600s                                             │
╰─────────────────────────────────────────────────────────────╯
✓ OK Validation added

$ agents project validation add --description "Lint check" \
  --required local/api-service "ruff check ."
╭─ Validation Added ───────────────────╮
│ ID: val_01HXM5B                     │
│ Project: local/api-service          │
│ Command: ruff check .               │
│ Description: Lint check              │
│ Mode: required                      │
│ Timeout: 300s                       │
╰──────────────────────────────────────╯
✓ OK Validation added

$ agents project validation add --description "Check bundle size (advisory)" \
  --informational local/api-service "node scripts/check-bundle-size.js"
╭─ Validation Added ──────────────────────────────╮
│ ID: val_01HXM5C                                  │
│ Project: local/api-service                      │
│ Command: node scripts/check-bundle-size.js       │
│ Description: Check bundle size (advisory)        │
│ Mode: informational                              │
│ Timeout: 300s                                    │
╰─────────────────────────────────────────────────╯
✓ OK Validation added

agents project validation remove

Purpose Remove a validation from a project.

Arguments

• <PROJECT>: Project name (positional argument).
• <VALIDATION_ID>: Validation identifier (positional argument).
• --yes, -y: Skip confirmation prompt.

Examples

$ agents project validation remove local/api-service val_01HXM5C
Remove validation val_01HXM5C from local/api-service? [y/N]: y
╭─ Validation Removed ─────────────────────────────╮
│ ID: val_01HXM5C                                   │
│ Command: node scripts/check-bundle-size.js        │
│ Description: Check bundle size (advisory)         │
╰──────────────────────────────────────────────────╯
✓ OK Validation removed

agents project validation list

Purpose List all validations for a project.

Arguments

• <PROJECT>: Project name (positional argument).

Examples

$ agents project validation list local/api-service
╭─ Validations ─────────────────────────────────────────────────────────────────────────╮
│ ID           Command                              Mode          Timeout  Resource │
│ ───────────  ───────────────────────────────────  ────────────  ───────  ──────── │
│ val_01HXM5A  pytest --cov=src --cov-fail-under=80  required      600s     repo     │
│ val_01HXM5B  ruff check .                          required      300s     (all)    │
│ val_01HXM5C  node scripts/check-bundle-size.js     informational 300s     (all)    │
╰───────────────────────────────────────────────────────────────────────────────────────╯
╭─ Summary ──────────────╮
│ Total: 3               │
│ Required: 2            │
│ Informational: 1      │
╰────────────────────────╯
✓ OK 3 validations listed

agents project delete

Purpose Delete a project and all associated resources.

Arguments

• <NAME>: Project name.
• --force/-f: Delete even if active plans exist.
• --yes: Skip confirmation prompt.

Examples

$ agents project delete local/docs
Delete project local/docs? This cannot be undone. [y/N]: y
╭─ Deletion Summary ──────────────────╮
│ Project: local/docs                 │
│ ID: 01HXM4B9F2C1V8X2N6Q7K9L0M1      │
│ Resources: 1 removed                │
│ Data Dir: /repos/docs/.cleveragents │
╰─────────────────────────────────────╯
╭─ Index Cleanup ────────╮
│ Text Index: cleared   │
│ Vectors: 240 removed  │
│ Graph Triples: none  │
│ Storage Freed: 12 MB │
╰────────────────────────╯
╭─ Backups ────────────────────────────────────╮
│ Snapshot: /backups/local-docs-2026-02-08.tgz │
│ Retention: 7 days                            │
╰──────────────────────────────────────────────╯
✓ OK Project deleted

agents project context

Purpose Manage context policies for the hot/warm/cold tiers and per-view context selection.

agents project context set

Purpose Set the context policy for a project and (optionally) a specific view.

Arguments

• <PROJECT>: Project name (positional argument, at end of command).
• --view strategize|execute|apply|default: Which view this policy applies to.
• --include-resource NAME: Resource allowlist (repeatable).
• --exclude-resource NAME: Resource denylist (repeatable).
• --include-path GLOB: Path allowlist (repeatable).
• --exclude-path GLOB: Path denylist (repeatable).
• --hot-max-tokens N: Maximum token budget for hot context. This is a soft cap and may be null. The actor/LLM hard limit can be lower; the effective hot context is the lesser of the two.
• --warm-max-decisions N: Maximum number of decisions kept in warm context.
• --cold-max-decisions N: Maximum number of decisions kept in cold context.
• --query-limit N: Max number of retrieval results per query.
• --max-file-size BYTES: Max file size included in context.
• --max-total-size BYTES: Max total size across included files.
• --summarize/--no-summarize: Enable or disable summarization for large context segments.
• --summary-max-tokens N: Token limit for generated summaries.
• --clear: Clear the policy for the selected view.

Examples

$ agents project context set --view strategize \
  --include-resource repo --exclude-path "**/node_modules/**" \
  --hot-max-tokens 12000 --warm-max-decisions 50 --cold-max-decisions 200 \
  --summarize --summary-max-tokens 800 local/api-service
╭─ Context Policy ────────────╮
│ Project: local/api-service  │
│ View: strategize            │
│ Include: repo               │
│ Exclude: /node_modules/ │
╰─────────────────────────────╯
╭─ Limits ─────────────────────╮
│ Hot Tokens: 12000 (soft cap) │
│ Warm Decisions: 50           │
│ Cold Decisions: 200          │
│ Query Limit: 20              │
│ Max File Size: 1 MB          │
│ Max Total Size: 50 MB        │
╰──────────────────────────────╯
╭─ Summarization ─╮
│ Enabled: yes    │
│ Max Tokens: 800 │
╰─────────────────╯
╭─ Other Views ────────╮
│ execute: (default) │
│ apply: (default)   │
│ default: (unset)   │
╰──────────────────────╯
✓ OK Context policy updated

agents project context show

Purpose Show the active context policy for a project.

Arguments

• <PROJECT>: Project name (positional argument, at end of command).
• --view strategize|execute|apply|default: View to display.

Examples

$ agents project context show --view strategize local/api-service
╭─ Context Policy ────────────╮
│ Project: local/api-service  │
│ View: strategize            │
│ Include: repo               │
│ Exclude: /node_modules/ │
╰─────────────────────────────╯
╭─ Limits ─────────────────────╮
│ Hot Tokens: 12000 (soft cap) │
│ Warm Decisions: 50           │
│ Cold Decisions: 200          │
│ Query Limit: 20              │
│ Max File Size: 1 MB          │
│ Max Total Size: 50 MB        │
╰──────────────────────────────╯
╭─ Summarization ─╮
│ Enabled: yes    │
│ Max Tokens: 800 │
╰─────────────────╯
╭─ Current Usage ──────────────╮
│ Hot Context: 8,420 / 12,000  │
│ Warm Entries: 12 / 50        │
│ Cold Entries: 47 / 200       │
│ Indexed Resources: 1        │
╰──────────────────────────────╯
✓ OK Context policy loaded

agents actor

Purpose Manage actors and run actor configurations directly.

agents actor run

Purpose Run a named actor in isolation with simple, manual context.

Arguments

• <NAME>: The name of the actor to run (required).
• <PROMPT>: Prompt text (positional argument).
• --output/-o FILE: Output file path.
• --verbose/-v: Increase verbosity (repeatable).
• --unsafe/-u: Allow unsafe configs.
• --context NAME: Named actor context to attach.
• --context-dir PATH: Context storage location.
• --load-context FILE: Load context from JSON.
• --temperature/-t FLOAT: Override temperature.
• --skill NAME: Skill to attach (repeatable).
• --allow-rxpy-in-run-mode: Allow RxPy routes in run mode.

Examples

$ agents actor run --context docs local/code_reader "Summarize the README"
╭─ Run Summary ────────────────╮
│ Actor: local/code_reader     │
│ Context: docs                │
│ Temperature: 0.2             │
│ Provider: anthropic          │
│ Model: claude-3.5             │
╰──────────────────────────────╯
╭─ Inputs ─────────────────────╮
│ Prompt: Summarize the README │
│ Context Files: 3             │
│ Context Size: 12.4 KB       │
╰──────────────────────────────╯
╭─ Result Metrics ──────╮
│ Output: stdout         │
│ Input Tokens: 1,524   │
│ Output Tokens: 842    │
│ Duration: 1.8s         │
│ Cost: $0.0021          │
│ Tool Calls: 0          │
╰───────────────────────╯
✓ OK Summary generated

agents actor add

Purpose Add a new actor configuration, or replace an existing one with --update. An actor can be defined via a YAML configuration file, entirely from CLI options, or a combination of both. When both a --config file and CLI options are provided, the CLI options act as overrides for values defined in the configuration file. If a local actor with the same name already exists, the command fails unless the --update flag is provided, which replaces the existing actor.

Arguments

• [<NAME>]: Actor name (optional). When provided via a config file, the name can be omitted from the command line.
• --config/-c FILE: Actor config file. When provided, values from the file serve as defaults. When omitted, the actor is defined entirely from CLI options.
• --type graph|agent: Actor type.
• --skill NAME: Skill to attach to the actor (repeatable).
• --description/-d TEXT: Actor description.
• --unsafe: Mark actor as unsafe.
• --option/-o key=value: Option override (repeatable).
• --update: Replace an existing local actor with the same name. Without this flag, attempting to add an actor whose name is already registered will fail.

When --config is provided alongside CLI options, the CLI options override the corresponding values from the config file.

Examples

$ agents actor add --config ./actors/reviewer.yaml local/reviewer
╭─ Actor Added ────────╮
│ Name: local/reviewer │
│ Provider: openai     │
│ Model: gpt-4         │
│ Default: yes         │
│ Unsafe: no           │
│ Type: graph          │
╰──────────────────────╯
╭─ Config ─────────────────────╮
│ Path: ./actors/reviewer.yaml │
│ Hash: 8b3f3d2                │
│ Options: 4                   │
│ Nodes: 3                     │
│ Edges: 4                     │
╰──────────────────────────────╯
╭─ Capabilities ───────╮
│ - code review        │
│ - diff summarization │
│ - lint guidance      │
╰──────────────────────╯
╭─ Tools ────────────────────────╮
│ Tool          Read-Only  Safe │
│ ────────────  ─────────  ──── │
│ read_file     yes        yes  │
│ search_files  yes        yes  │
│ git_diff      yes        yes  │
╰────────────────────────────────╯
✓ OK Actor added

agents actor remove

Purpose Remove a custom actor.

Arguments

• <NAME>: Actor name.

Examples

$ agents actor remove local/reviewer
╭─ Actor Removed ──────╮
│ Name: local/reviewer │
│ Provider: openai     │
│ Model: gpt-4         │
╰──────────────────────╯
╭─ Impact ───────────────────────────────────╮
│ Sessions: 0 affected                       │
│ Active Plans: 0 affected                   │
│ Actions Referencing: 0                     │
╰────────────────────────────────────────────╯
╭─ Cleanup ──────────────╮
│ Config: kept on disk  │
│ Contexts: 1 orphaned  │
╰────────────────────────╯
✓ OK Actor removed

agents actor list

Purpose List all actors.

Arguments

None.

Examples

$ agents actor list
╭─ Actors ──────────────────────────────────────────────────────────────╮
│ Name            Provider   Model       Default  Built-in  Unsafe │
│ ──────────────  ─────────  ──────────  ───────  ────────  ────── │
│ local/reviewer  openai     gpt-4       ✓                  no     │
│ openai/gpt-4    openai     gpt-4                ✓         no     │
│ anthropic/3.5   anthropic  claude-3.5           ✓         no     │
╰──────────────────────────────────────────────────────────────────────╯
╭─ Summary ──────────────╮
│ Total: 3               │
│ Built-in: 2            │
│ Custom: 1              │
│ Unsafe: 0              │
│ Providers Used: 2     │
╰────────────────────────╯
✓ OK 3 actors listed

agents actor show

Purpose Show details for a single actor.

Arguments

• <NAME>: Actor name.

Examples

$ agents actor show local/reviewer
╭─ Actor Details ────────────────────╮
│ Name: local/reviewer               │
│ Provider: openai                   │
│ Model: gpt-4                       │
│ Default: yes                       │
│ Built-in: no                       │
│ Unsafe: no                         │
│ Type: graph                        │
│ Created: 2026-02-08 12:35          │
│ Updated: 2026-02-08 12:40          │
│ Config: ./actors/reviewer.yaml     │
│ Config Hash: 9c4e2a1              │
╰────────────────────────────────────╯
╭─ Options ──────────╮
│ - temperature: 0.2 │
│ - max_tokens: 2048 │
│ - top_p: 1.0       │
╰────────────────────╯
╭─ Graph Structure ─╮
│ Nodes: 3          │
│ Edges: 4          │
│ Entry: analyze    │
│ Exit: report      │
╰───────────────────╯
╭─ Tools ────────────────────────╮
│ Tool          Read-Only  Safe │
│ ────────────  ─────────  ──── │
│ read_file     yes        yes  │
│ search_files  yes        yes  │
│ git_diff      yes        yes  │
╰────────────────────────────────╯
╭─ Permissions ───────╮
│ Unsafe: no          │
│ Filesystem: allowed │
│ Network: restricted │
╰─────────────────────╯
╭─ Usage ─────────────────────────────────────────╮
│ Referenced by Actions: 1 (local/code-coverage) │
│ Active in Sessions: 0                          │
│ Total Runs: 14                                 │
│ Avg Cost/Run: $0.0032                          │
╰─────────────────────────────────────────────────╯
✓ OK Actor loaded

agents actor context

Purpose Manage manual context for actor runs. These commands mirror the legacy context behavior but are scoped to an actor context name.

agents actor context rm

Purpose Remove files or directories from an actor context.

Arguments

• <NAME>: Context name (positional argument). Use --all/-a to target all contexts.
• --all, -a: Target all contexts instead of a named one.
• --yes, -y: Skip confirmation prompt.

Examples

$ agents actor context rm docs
╭─ Context Removed ───────────╮
│ Context: docs              │
│ Status: removed            │
╰─────────────────────────────╯
╭─ Stats ───────────────────╮
│ Remaining Size: 48 KB     │
│ Updated: 2026-02-08 13:06 │
╰───────────────────────────╯
✓ OK Context updated

agents actor context list

Purpose List files stored in an actor context.

Arguments

• [REGEX]: Optional regex filter for context names.

Examples

$ agents actor context list docs
╭─ Context Files ──────────────────────────────╮
│ Name              Type  Size     Added      │
│ ────────────────  ────  ───────  ────────── │
│ README.md         file  4.2 KB   02-08 12:10 │
│ docs/overview.md  file  12.8 KB  02-08 12:10 │
│ docs/cli.md       file  9.5 KB   02-08 12:10 │
╰──────────────────────────────────────────────╯
╭─ Stats ───────────────────────╮
│ Total Files: 3               │
│ Total Size: 26.5 KB          │
│ Estimated Tokens: ~6,600    │
│ Languages: Markdown          │
╰───────────────────────────────╯
✓ OK 3 files listed

agents actor context show

Purpose Show content of a file in an actor context.

Arguments

• <NAME>: Context name (positional argument).

Examples

$ agents actor context show docs
╭─ Context Summary ───────────╮
│ Context: docs               │
│ Files: 3                    │
│ Total Size: 26.5 KB        │
│ Estimated Tokens: ~6,600  │
│ Created: 2026-02-08 12:10  │
╰─────────────────────────────╯
✓ OK Context displayed

agents actor context export

Purpose Export a context as JSON.

Arguments

• <NAME>: Context name (positional argument).
• --output/-o FILE: Output file path (required).

Examples

$ agents actor context export --output /tmp/docs-context.json docs
╭─ Context Export ───────────────╮
│ Context: docs                  │
│ Output: /tmp/docs-context.json │
│ Items: 12                      │
│ Size: 48 KB                    │
╰────────────────────────────────╯
╭─ Integrity ──────────────────╮
│ Checksum: sha256:19b2...a7d0 │
│ Compressed: no               │
╰──────────────────────────────╯
✓ OK Export completed

agents actor context import

Purpose Import a context from JSON.

Arguments

• [NAME]: Context name (optional, inferred from file if omitted).
• --input/-i FILE: Input JSON file (required).
• --update: Replace an existing context with the same name.

Examples

$ agents actor context import --input /tmp/docs-context.json docs
╭─ Context Import ──────────────╮
│ Context: docs                 │
│ Input: /tmp/docs-context.json │
│ Items: 12                     │
╰───────────────────────────────╯
╭─ Merge ───────────╮
│ Strategy: replace │
│ Conflicts: 0      │
╰───────────────────╯
✓ OK Import completed

agents actor context delete

Purpose Delete an entire context.

Arguments

• <NAME>: Context name (positional argument). Use --all/-a to target all contexts.
• --all, -a: Target all contexts instead of a named one.
• --yes, -y: Skip confirmation.

Examples

$ agents actor context delete docs
Delete context docs? [y/N]: y
╭─ Context Deleted ────╮
│ Context: docs        │
│ Items: 12 removed    │
│ Storage: 48 KB freed │
╰──────────────────────╯
╭─ Cleanup ───────╮
│ Backups: none   │
│ Logs: preserved │
╰─────────────────╯
��� OK Context deleted

agents actor context clear

Purpose Clear all files from a context but keep the context itself.

Arguments

• <NAME>: Context name (positional argument). Use --all/-a to target all contexts.
• --all, -a: Target all contexts instead of a named one.
• --yes, -y: Skip confirmation.

Examples

$ agents actor context clear docs
Clear context docs? [y/N]: y
╭─ Context Cleared ────╮
│ Context: docs        │
│ Items: 12 removed    │
│ Storage: 48 KB freed │
╰──────────────────────╯
╭─ Retention ────────╮
│ Context: preserved │
│ Files: removed     │
╰────────────────────╯
✓ OK Context cleared

agents skill

Purpose Manage skills — reusable, namespaced collections of tools. Skills are defined in their own YAML configuration files and registered in the system through these commands. Once registered, skills can be referenced by actors.

agents skill add

Purpose Register a new skill. A skill can be defined via a YAML configuration file, entirely from CLI options, or a combination of both. When both a --config file and CLI options are provided, the CLI options act as overrides for values defined in the configuration file. If a skill with the same name already exists, the command fails unless the --update flag is provided, which allows overwriting the existing registration with the new configuration.

Arguments

• [<NAME>]: Namespaced skill name (optional). When provided via a config file, the name can be omitted from the command line.
• --config/-c FILE: Path to the skill YAML configuration file. When provided, values from the file serve as defaults. When omitted, the skill is defined entirely from CLI options.
• --description/-d TEXT: Optional description (overrides what's in the YAML when both are provided).
• --tool NAME: Named tool to include in the skill (repeatable). References tools from the Tool Registry.
• --include-skill NAME: Include another skill's tools (repeatable). References skills by fully-qualified name.
• --mcp-server spec: MCP server specification to expose tools from (repeatable).
• --update: Allow overwriting an existing skill registration. Without this flag, attempting to add a skill whose name is already registered will fail.

When --config is provided alongside CLI options (--tool, --include-skill, --mcp-server, --description), the CLI options override or extend the corresponding values from the config file.

Examples

Registering a new skill:

$ agents skill add --config ./skills/devops-toolkit.yaml local/devops-toolkit
╭─ Skill Registered ──────────────────────╮
│ Name: local/devops-toolkit              │
│ ID: 01HXMC4T08Y0N5R9VZ4QX4BPTZ1        │
│ Description: Full-stack development tools │
│ Config: ./skills/devops-toolkit.yaml      │
│ Created: 2026-02-08 13:10                 │
╰─────────────────────────────────────────╯
╭─ Includes ────────────────────╮
│ local/file-ops (registered) │
│ local/git-ops (registered)  │
│ local/github (registered)   │
╰───────────────────────────────╯
╭─ Tool Sources ─────────────────────────────╮
│ Source         Count  Details              │
│ ─────────────  ─────  ───────────────────  │
│ builtin        14     file, dir, git, shell   │
│ mcp            6      github (4), linear (2) │
│ agent_skill    2      deploy, code-review    │
│ custom         1      run_migrations         │
│ ─────────────  ─────  ───────────────────  │
│ Total:         23                            │
╰────────────────────────────────────────────╯
╭─ MCP Servers ────────────────────╮
│ linear: validated (2 tools)    │
╰──────────────────────────────────╯
✓ OK Skill registered with 23 tools

Attempting to add a skill that already exists (without --update):

$ agents skill add --config ./skills/devops-toolkit-v2.yaml local/devops-toolkit
✗ Error: Skill 'local/devops-toolkit' is already registered.
To overwrite the existing configuration, re-run with --update:
agents skill add --config ./skills/devops-toolkit-v2.yaml --update local/devops-toolkit

Updating an existing skill with --update:

$ agents skill add --config ./skills/devops-toolkit-v2.yaml --update local/devops-toolkit
╭─ Skill Updated ──────────────────────────╮
│ Name: local/devops-toolkit              │
│ Description: Full-stack development tools │
│ Updated: 2026-02-08 14:22               │
╰─────────────────────────────────────────╯
╭─ Changes ─────────────────────╮
│ Tools Added: 2              │
│ Tools Removed: 0           │
│ Tools Modified: 1          │
│ Includes Changed: no      │
│ MCP Servers Changed: no   │
╰───────────────────────────────╯
╭─ Affected Actors ────────────────────────────╮
│ Warning: 2 actors reference this skill:     │
│ - local/code-assistant                        │
│ - local/full-stack-assistant                  │
│ These actors will pick up changes on next use │
╰──────────────────────────────────────────────╯
✓ OK Skill updated (23 → 25 tools)

agents skill remove

Purpose Remove a registered skill.

Arguments

• <NAME>: Skill name to remove.
• --yes: Skip confirmation prompt.

Examples

$ agents skill remove local/devops-toolkit
Remove skill local/devops-toolkit? [y/N]: y
╭─ Skill Removed ──────────────────────╮
│ Name: local/devops-toolkit           │
│ Tools: 23 removed from registry      │
│ MCP Servers: 1 connection closed     │
╰──────────────────────────────────────╯
╭─ Dependency Check ───────────────────────────────╮
│ Warning: 1 skill includes this skill:             │
│ - local/full-stack-dev (will lose devops tools)   │
│ Warning: 2 actors reference this skill:            │
│ - local/code-assistant                             │
│ - local/full-stack-assistant                       │
╰──────────────────────────────────────────────────╯
✓ OK Skill removed

agents skill list

Purpose List registered skills with optional filters.

Arguments

• --namespace/-n NS: Filter by namespace.
• --source SOURCE: Filter by tool source type (mcp, agent_skill, builtin, custom).

Examples

$ agents skill list
╭─ Skills ────────────────────────────────────────────────────────────╮
│ Name                    Tools  Includes  Sources                │
│ ──────────────────────  ─────  ────────  ────────────────────── │
│ local/file-ops          9      0         builtin                │
│ local/git-ops           4      0         builtin                │
│ local/github            4      0         mcp                    │
│ local/devops-toolkit    23     3         builtin, mcp, custom   │
│ local/full-stack-dev    25     4         builtin, mcp, custom   │
╰─────────────────────────────────────────────────────────────────────╯
╭─ Summary ─────────╮
│ Total: 5          │
│ Local: 5          │
│ Server: 0         │
│ Total Tools: 28  │
╰───────────────────╯
✓ OK 5 skills listed

agents skill show

Purpose Show full details for a registered skill, including its includes, tool sources, and capability summary.

Arguments

• <NAME>: Skill name.

Examples

$ agents skill show local/devops-toolkit
╭─ Skill Details ─────────────────────────────╮
│ Name: local/devops-toolkit                 │
│ ID: 01HXMC4T08Y0N5R9VZ4QX4BPTZ1           │
│ Description: Full-stack development tools    │
│ Config: ./skills/devops-toolkit.yaml         │
│ Created: 2026-02-08 13:10                    │
│ Updated: 2026-02-08 14:22                    │
╰─────────────────────────────────────────────╯
╭─ Includes (3) ────────────────────────────╮
│ local/file-ops  → 9 tools (builtin)     │
│ local/git-ops   → 4 tools (builtin)     │
│ local/github    → 4 tools (mcp)          │
╰───────────────────────────────────────────╯
╭─ Direct Tools (6) ────────────────────────────────────────╮
│ Name              Source       Writes  Checkpoint │
│ ────────────────  ───────────  ──────  ────────── │
│ create_issue      mcp:linear   yes     no         │
│ list_issues       mcp:linear   no      —          │
│ deploy-to-staging agent_skill  yes     composite  │
│ code-review       agent_skill  no      —          │
│ run_migrations    custom       yes     transaction │
│ shell_execute     builtin      yes     snapshot   │
╰───────────────────────────────────────────────────────────╯
╭─ MCP Servers (1) ───────────────────╮
│ linear: stdio, 2 tools, connected │
╰─────────────────────────────────────╯
╭─ Capability Summary ──────────╮
│ Total Tools: 23              │
│ Read-Only: 10               │
│ Writes: 13                  │
│ Checkpointable: 10         │
│ Has Side Effects: 3        │
│ Requires Approval: 1       │
╰───────────────────────────────╯
╭─ Referenced By ───────────────────╮
│ Actors: local/code-assistant    │
│ Skills: local/full-stack-dev    │
╰───────────────────────────────────╯
✓ OK Skill loaded

agents skill tools

Purpose List all tools provided by a skill, including those inherited from included skills (the flattened tool set).

Arguments

• <NAME>: Skill name.

Examples

$ agents skill tools local/devops-toolkit
╭─ Tools for local/devops-toolkit ─────────────────────────────────────────────────────────╮
│ Tool               Source       From Skill      Read-Only  Writes  Checkpoint │
│ ─────────────────  ───────────  ──────────────  ─────────  ──────  ────────── │
│ read_file          builtin      local/file-ops  ✓          —       —          │
│ write_file         builtin      local/file-ops  —          ✓       file       │
│ edit_file          builtin      local/file-ops  —          ✓       file       │
│ delete_file        builtin      local/file-ops  —          ✓       file       │
│ move_file          builtin      local/file-ops  —          ✓       file       │
│ copy_file          builtin      local/file-ops  —          ✓       file       │
│ create_directory   builtin      local/file-ops  —          ✓       file       │
│ list_directory     builtin      local/file-ops  ✓          —       —          │
│ delete_directory   builtin      local/file-ops  —          ✓       file       │
│ git_status         builtin      local/git-ops   ✓          —       —          │
│ git_diff           builtin      local/git-ops   ✓          —       —          │
│ git_log            builtin      local/git-ops   ✓          —       —          │
│ git_blame          builtin      local/git-ops   ✓          —       —          │
│ create_issue       mcp:github   local/github    —          ✓       no         │
│ create_pr          mcp:github   local/github    —          ✓       no         │
│ list_repos         mcp:github   local/github    ✓          —       —          │
│ get_file_contents  mcp:github   local/github    ✓          —       —          │
│ create_issue       mcp:linear   (direct)        —          ✓       no         │
│ list_issues        mcp:linear   (direct)        ✓          —       —          │
│ run_migrations     custom       (direct)        —          ✓       transaction │
│ deploy-to-staging  agent_skill  (direct)        —          ✓       composite  │
│ code-review        agent_skill  (direct)        ✓          —       —          │
│ shell_execute      builtin      (direct)        —          ✓       snapshot   │
╰──────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Summary ──────────────────╮
│ Total: 23                 │
│ From Includes: 17        │
│ Direct: 6                │
│ Read-Only: 10            │
│ Writes: 13               │
│ Checkpointable: 10      │
╰────────────────────────────╯
✓ OK 23 tools listed

agents tool

Purpose Manage tools — namespaced, independently registered, callable operations. Tools are defined in their own YAML configuration files and registered in the system through these commands. Once registered, tools can be referenced by name in skills (as part of a tool collection) and in actor graphs (as tool nodes).

agents tool add

Purpose Register a new tool. A tool can be defined via a YAML configuration file, entirely from CLI options, or a combination of both. When both a --config file and CLI options are provided, the CLI options act as overrides for values defined in the configuration file. If a tool with the same name already exists, the command fails unless the --update flag is provided, which allows overwriting the existing registration with the new configuration.

Arguments

• <NAME>: Namespaced tool name (appears at the end of the command).
• --config/-c FILE: Path to the tool YAML configuration file. When provided, values from the file serve as defaults. When omitted, the tool is defined entirely from CLI options.
• --description/-d TEXT: Optional description (overrides what's in the YAML when both are provided).
• --source SOURCE: Tool source type (custom, mcp, agent_skill, builtin).
• --input-schema JSON: JSON Schema for tool inputs.
• --code TEXT: Inline Python code for custom tools.
• --writes/--no-writes: Whether the tool performs write operations.
• --checkpointable/--no-checkpointable: Whether the tool supports checkpointing.
• --update: Allow overwriting an existing tool registration. Without this flag, attempting to add a tool whose name is already registered will fail.

When --config is provided alongside CLI options, the CLI options override the corresponding values from the config file.

Examples

Registering a new tool:

$ agents tool add --config ./tools/run-migrations.yaml local/run-migrations
╭─ Tool Registered ───────────────────────────╮
│ Name: local/run-migrations                  │
│ ID: 01HXMC5T09Z1N6S0WZ5RY5CQUZ2          │
│ Description: Run database migrations         │
│ Source: custom                               │
│ Config: ./tools/run-migrations.yaml          │
│ Created: 2026-02-09 10:15                   │
╰─────────────────────────────────────────────╯
╭─ Capability ──────────────────╮
│ Writes: true                 │
│ Write Scope: database:migrations │
│ Checkpointable: true        │
│ Checkpoint Scope: transaction │
│ Side Effects: schema_mutation  │
╰───────────────────────────────╯
✓ OK Tool registered

Attempting to add a tool that already exists (without --update):

$ agents tool add --config ./tools/run-migrations-v2.yaml local/run-migrations
✗ Error: Tool 'local/run-migrations' is already registered.
To overwrite the existing configuration, re-run with --update:
agents tool add --config ./tools/run-migrations-v2.yaml --update local/run-migrations

agents tool remove

Purpose Remove a registered tool.

Arguments

• <NAME>: Tool name to remove.
• --yes: Skip confirmation prompt.

Examples

$ agents tool remove local/run-migrations
Remove tool local/run-migrations? [y/N]: y
╭─ Tool Removed ────────────────────╮
│ Name: local/run-migrations       │
│ Source: custom                    │
╰───────────────────────────────────╯
╭─ References ──────────────────────────────────────╮
│ Warning: This tool is referenced by:              │
│ - Skill: local/devops-toolkit                     │
│ - Actor graph node: code_executor.run_db_migrate  │
│ These references will break until resolved.       │
╰───────────────────────────────────────────────────╯
✓ OK Tool removed

agents tool list

Purpose List registered tools with optional filters.

Arguments

• --namespace/-n NS: Filter by namespace.
• --source SOURCE: Filter by tool source type (mcp, agent_skill, builtin, custom).

Examples

$ agents tool list --namespace local
╭─ Tools ──────────────────────────────────────────────────╮
│ Name                       Source   Read-Only  Writes │
│ ─────────────────────────  ───────  ─────────  ────── │
│ local/run-migrations       custom   —          ✓      │
│ local/validate-api-compat  custom   —          ✓      │
│ local/create-subplan       custom   —          ✓      │
│ local/deploy-staging       agent    —          ✓      │
╰──────────────────────────────────────────────────────────╯
╭─ Summary ────────╮
│ Total: 4        │
│ Read-Only: 0    │
│ Writes: 4       │
╰──────────────────╯
✓ OK 4 tools listed

agents tool show

Purpose Show full details for a registered tool, including its schema, capability metadata, and where it is referenced.

Arguments

• <NAME>: Tool name.

Examples

$ agents tool show local/run-migrations
╭─ Tool Details ──────────────────────────────╮
│ Name: local/run-migrations                  │
│ Description: Run database migrations         │
│ Source: custom                               │
│ Config: ./tools/run-migrations.yaml          │
│ Registered: 2026-02-09 10:15                 │
╰─────────────────────────────────────────────╯
╭─ Input Schema ────────────────────────────────╮
│ direction: string (required) enum: [up, down] │
│ count: integer (default: 1)                    │
╰───────────────────────────────────────────────╯
╭─ Capability ──────────────────────╮
│ Read-Only: false                  │
│ Writes: true                     │
│ Write Scope: database:migrations  │
│ Checkpointable: true             │
│ Checkpoint Scope: transaction    │
│ Side Effects: schema_mutation     │
│ Idempotent: false                │
╰───────────────────────────────────╯
╭─ Referenced By ───────────────────────╮
│ Skills:                             │
│   - local/devops-toolkit              │
│ Actor Graph Nodes:                  │
│   - code_executor.run_db_migrate      │
╰───────────────────────────────────────╯
✓ OK Tool details loaded

agents resource type

Purpose Manage resource type definitions. Resource types are schema-level definitions that constrain categories of resources — they define what CLI arguments agents resource add <type> accepts, whether instances are physical or virtual, allowed parent/child types, auto-discovery behavior, sandbox strategy, and handler implementation. Built-in types (git-checkout, git, fs-mount, fs-directory, and their child types) are hardcoded. Custom types are defined in YAML configuration files and extend the system with new agents resource add subcommands.

agents resource type add

Purpose Register a new custom resource type. A resource type can be defined via a YAML configuration file, entirely from CLI options, or a combination of both. When both a --config file and CLI options are provided, the CLI options act as overrides for values defined in the configuration file.

Arguments

• [<NAME>]: Namespaced resource type name (e.g., local/svn, cleverthis/s3-bucket). Optional when provided via a config file.
• --config/-c FILE: Path to the resource type YAML configuration file. When provided, values from the file serve as defaults. When omitted, the resource type is defined entirely from CLI options.
• --physical/--virtual: Whether instances of this type are physical or virtual.
• --user-addable/--no-user-addable: Whether instances can be created directly by users.
• --sandbox-strategy NAME: Sandbox strategy for instances of this type (e.g., copy_on_write, transaction_rollback, none).
• --handler NAME: Resource handler implementation.
• --child-type spec: Allowed child resource type specification (repeatable).
• --cli-arg spec: CLI argument definition for agents resource add <type> (repeatable).
• --update: If the type name already exists, overwrite it. Without this flag, adding a duplicate name fails with an error.

When --config is provided alongside CLI options, the CLI options override the corresponding values from the config file.

Examples

$ agents resource type add --config ./resource-types/svn.yaml local/svn
╭─ Resource Type ────────────────────╮
│ Name: local/svn                    │
│ Physical/Virtual: physical         │
│ User Addable: yes                  │
│ Registered: 2026-02-09 10:15       │
╰────────────────────────────────────╯
╭─ CLI Arguments ────────────────────────╮
│ Argument      Required  Description    │
│ ────────────  ────────  ───────────── │
│ --url         yes       Repository URL │
│ --checkout    no        Local checkout │
╰────────────────────────────────────────╯
╭─ Child Types ──────────────────────────╮
│ Auto-discover: svn-revision, svn-file │
│ Manual link: fs-mount                 │
╰────────────────────────────────────────╯
╭─ Sandbox ──────────────────────╮
│ Strategy: copy_on_write        │
│ Handler: SVNHandler (custom)   │
╰────────────────────────────────╯
✓ OK Resource type registered
ℹ New subcommand available: agents resource add local/svn

agents resource type remove

Purpose Remove a custom resource type. Built-in types cannot be removed. Fails if any registered resources use this type.

Arguments

• <NAME>: Resource type name.
• --yes: Skip confirmation prompt.

Examples

$ agents resource type remove local/svn
Remove resource type local/svn? [y/N]: y
╭─ Resource Type Removed ───────╮
│ Name: local/svn               │
│ Resources Using: 0           │
│ Subcommand Removed: yes      │
╰───────────────────────────────╯
✓ OK Resource type removed

agents resource type list

Purpose List all registered resource types (built-in and custom).

Arguments

None (use the global --format option to control output format).

Examples

$ agents resource type list
╭─ Resource Types ──────────────────────────────────────────────────────────────────────────────────╮
│ Name                 Source    Phys/Virt  Addable  Auto-children                          │
│ ───────────────────  ────────  ─────────  ───────  ────────────────────────────────────── │
│ git-checkout        built-in  physical   yes      git, fs-directory                       │
│ git                 built-in  physical   yes      git-remote, git-branch, git-tag,       │
│                                                    git-commit, git-stash, git-submodule   │
│ git-remote          built-in  physical   no       (none)                                 │
│ git-branch          built-in  physical   no       git-commit                             │
│ git-tag             built-in  physical   no       (none)                                 │
│ git-commit          built-in  physical   no       git-tree                               │
│ git-tree            built-in  physical   no       git-tree-entry                         │
│ git-tree-entry      built-in  physical   no       (none)                                 │
│ git-stash           built-in  physical   no       (none)                                 │
│ git-submodule       built-in  physical   no       (none)                                 │
│ fs-mount            built-in  physical   yes      fs-directory                            │
│ fs-directory        built-in  physical   yes      fs-file, fs-directory,                  │
│                                                    fs-symlink, fs-hardlink                │
│ fs-file             built-in  physical   no       (none)                                 │
│ fs-symlink          built-in  physical   no       (none)                                 │
│ fs-hardlink         built-in  physical   no       (none)                                 │
│ file                built-in  virtual    no       (none)                                 │
│ directory           built-in  virtual    no       (none)                                 │
│ symlink             built-in  virtual    no       (none)                                 │
│ commit              built-in  virtual    no       (none)                                 │
│ branch              built-in  virtual    no       (none)                                 │
│ tag                 built-in  virtual    no       (none)                                 │
│ remote              built-in  virtual    no       (none)                                 │
│ submodule           built-in  virtual    no       (none)                                 │
│ tree                built-in  virtual    no       (none)                                 │
│ local/svn           custom    physical   yes      svn-revision, svn-file                 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Summary ─────────────────╮
│ Built-in: 24            │
│ Custom: 1               │
│ User Addable: 4        │
╰───────────────────────────╯
✓ OK 25 resource types listed

agents resource type show

Purpose Show detailed information about a resource type, including its full schema.

Arguments

• <NAME>: Resource type name.

Examples

$ agents resource type show git-checkout
╭─ Resource Type ──────────────────────────────────────────────────────╮
│ Name: git-checkout                                                  │
│ Source: built-in                                                    │
│ Description: A locally checked-out git repository with worktree      │
│ Physical/Virtual: physical                                          │
│ User Addable: yes                                                  │
╰──────────────────────────────────────────────────────────────────────╯
╭─ CLI Arguments (agents resource add git-checkout) ─────────────────╮
│ Argument    Required  Type    Description                      │
│ ──────────  ────────  ──────  ──────────────────────────────  │
│ --path      yes       path    Local checkout directory             │
│ --branch    no        string  Default branch (default: main)       │
╰────────────────────────────────────────────────────────────────────╯
╭─ Parent Types ───────────────╮
│ Allowed: (any, top-level OK) │
╰──────────────────────────────╯
╭─ Child Types ────────────────────────────────────────────────────╮
│ Type           Auto  Manual Link  Description              │
│ ─────────────  ────  ───────────  ──────────────────────────  │
│ git            yes   no           Repo object DB and history     │
│ fs-directory   yes   no           Worktree root directory        │
╰──────────────────────────────────────────────────────────────────╯
╭─ Sandbox ──────────────────────╮
│ Strategy: git_worktree         │
│ Checkpointable: yes           │
│ Handler: GitCheckoutHandler   │
╰────────────────────────────────╯
✓ OK Resource type details loaded

agents resource

Purpose Manage independently registered resources. Resources represent anything that can be read, written, or queried — git repositories, filesystems, databases, APIs, and more. Resources are registered independently of projects and linked to one or more projects via agents project link-resource. Resources form a directed acyclic graph (DAG) with parent/child relationships, and child resources are often auto-discovered when a parent resource is registered.

agents resource add

Purpose Register a new resource. The add command uses type-specific subcommands — each registered resource type (built-in or custom) provides its own subcommand with type-appropriate arguments.

Arguments

• --description/-d TEXT: Optional resource description.
• --update: If the resource name already exists, replace it. Without this flag, adding a duplicate name fails with an error.
• <TYPE>: Resource type name (e.g., git-checkout, git, fs-mount, fs-directory, local/svn). Determines type-specific flags.
• <NAME>: Namespaced resource name (e.g., local/api-repo, cleverthis/staging-db).

Type-specific flags depend on the resource type. See agents resource type show <TYPE> for available arguments.

Examples

$ agents resource add git-checkout local/api-repo --path /home/user/projects/api-service --branch main
╭─ Resource ────────────────────────────╮
│ Name: local/api-repo                 │
│ Type: git-checkout                    │
│ Physical/Virtual: physical            │
│ Path: /home/user/projects/api-service  │
│ Branch: main                          │
│ Created: 2026-02-09 10:20             │
╰───────────────────────────────────────╯
╭─ Auto-discovered Children ───────────────────────────────────────╮
│ Name                         Type            Status             │
│ ───────────────────────────  ──────────────  ───────────────── │
│ local/api-repo:repo        git             created            │
│ local/api-repo:repo:origin git-remote      created            │
│ local/api-repo:repo:main   git-branch      created            │
│ local/api-repo:repo:dev    git-branch      created            │
│ local/api-repo:worktree    fs-directory    created            │
│   + 47 git-commit resources                                    │
│   + 312 git-tree-entry resources                               │
│   + 3 fs-directory + 28 fs-file                                │
╰──────────────────────────────────────────────────────────────────╯
╭─ Capabilities ─────────────────╮
│ Readable: yes                 │
│ Writable: yes                 │
│ Sandboxable: yes              │
│ Checkpointable: yes           │
│ Sandbox Strategy: git_worktree │
╰────────────────────────────────╯
✓ OK Resource registered (395 child resources discovered)

$ agents resource add fs-mount local/docs --mount-path /docs/api-reference
╭─ Resource ──────────────────────────╮
│ Name: local/docs                    │
│ Type: fs-mount                      │
│ Physical/Virtual: physical          │
│ Mount Path: /docs/api-reference      │
│ Created: 2026-02-09 10:22           │
╰─────────────────────────────────────╯
╭─ Auto-discovered Children ────────────────────╮
│   + 1 fs-directory (root)                     │
│   + 3 fs-directory resources                  │
│   + 28 fs-file resources                      │
╰───────────────────────────────────────────────╯
╭─ Capabilities ──────────────────────╮
│ Readable: yes                      │
│ Writable: yes                      │
│ Sandboxable: yes                   │
│ Checkpointable: yes                │
│ Sandbox Strategy: copy_on_write     │
╰─────────────────────────────────────╯
✓ OK Resource registered (31 child resources discovered)

Duplicate name error:

$ agents resource add git-checkout local/api-repo --path /repos/api-service
✗ Error: Resource local/api-repo already exists.
Use --update to replace: agents resource add --update git-checkout local/api-repo --path /repos/api-service

agents resource remove

Purpose Remove a registered resource and all its auto-discovered child resources. Fails if the resource is linked to any project (unlink first).

Arguments

• <NAME>: Resource name.
• --yes: Skip confirmation prompt.

Examples

$ agents resource remove local/api-repo
Remove resource local/api-repo and 395 child resources? [y/N]: y
╭─ Resource Removed ────────────────╮
│ Name: local/api-repo              │
│ Type: git-checkout                  │
│ Children Removed: 395             │
│ Projects Unlinked: 0              │
╰───────────────────────────────────╯
✓ OK Resource removed

agents resource list

Purpose List registered resources with optional filters.

Arguments

• --namespace/-n NS: Filter by namespace.
• --type/-t TYPE: Filter by resource type.

Examples

$ agents resource list
╭─ Resources ──────────────────────────────────────────────────────────────────╮
│ Name                Type        Phys/Virt  Children  Projects       │
│ ──────────────────  ──────────  ─────────  ────────  ──────────────── │
│ local/api-repo    git-checkout physical   395       local/api-service │
│ local/docs        fs-mount     physical   32        local/api-service │
│ local/staging-db  database    physical   12        local/api-service,│
│                                                     local/staging    │
╰──────────────────────────────────────────────────────────────────────────────╯
╭─ Summary ───────────╮
│ Total: 3            │
│ Physical: 3        │
│ Virtual: 0         │
│ Total Children: 405 │
╰─────────────────────╯
✓ OK 3 resources listed

agents resource show

Purpose Show detailed information about a registered resource including its type, capabilities, parent/child relationships, and linked projects.

Arguments

• <NAME>: Resource name.

Examples

$ agents resource show local/api-repo
╭─ Resource ──────────────────────────────╮
│ Name: local/api-repo                   │
│ Type: git-checkout                      │
│ Physical/Virtual: physical              │
│ Path: /home/user/projects/api-service    │
│ Branch: main                            │
│ Created: 2026-02-09 10:20               │
╰─────────────────────────────────────────╯
╭─ Capabilities ──────────────────╮
│ Readable: yes                    │
│ Writable: yes                    │
│ Sandboxable: yes                 │
│ Checkpointable: yes              │
│ Sandbox Strategy: git_worktree    │
╰─────────────────────────────────╯
╭─ Parents ────╮
│ (top-level)  │
╰──────────────╯
╭─ Direct Children ────────────────────────────────────────────╮
│ Name                       Type          Auto  Children  │
│ ─────────────────────────  ────────────  ────  ────────  │
│ local/api-repo:repo      git           yes   363       │
│ local/api-repo:worktree  fs-directory  yes   32        │
╰──────────────────────────────────────────────────────────────╯
╭─ Linked Projects ─────────────╮
│ - local/api-service (r/w)     │
╰───────────────────────────────╯
╭─ Tool Bindings ────────────────────────────────╮
│ Tool                      Slot   Access       │
│ ────────────────────────  ─────  ───────────  │
│ (builtin) git_status     repo   read_only     │
│ (builtin) git_diff       repo   read_only     │
│ (builtin) git_log        repo   read_only     │
│ local/run-migrations     db     (not bound)   │
╰────────────────────────────────────────────────╯
✓ OK Resource details loaded

agents resource tree

Purpose Display the resource DAG as a tree starting from a given resource, showing parent/child relationships.

Arguments

• <NAME>: Root resource name.
• --depth/-d N: Maximum depth to display (default: 3).
• --type/-t TYPE: Filter to only show children of a specific type.

Examples

$ agents resource tree local/api-repo --depth 2
╭─ Resource Tree: local/api-repo ──────────────────────────────╮
│                                                               │
│ local/api-repo (git-checkout, physical)                      │
│ ├── local/api-repo:repo (git, physical)                       │
│ │   ├── local/api-repo:repo:origin (git-remote)               │
│ │   ├── local/api-repo:repo:main (git-branch)                 │
│ │   │   ├── ...repo:main:a1b2c3d (git-commit)                 │
│ │   │   └── ... 22 more git-commit resources                 │
│ │   └── local/api-repo:repo:dev (git-branch)                  │
│ │       └── ... 26 git-commit resources                       │
│ └── local/api-repo:worktree (fs-directory, physical)          │
│     ├── ...worktree:src/ (fs-directory)                         │
│     ├── ...worktree:tests/ (fs-directory)                       │
│     └── ... 28 more fs-file resources                          │
│                                                               │
╰───────────────────────────────────────────────────────────────╯
╭─ Summary ──────────────╮
│ Total shown: 14       │
│ Total in subtree: 395 │
│ Max depth: 2          │
╰────────────────────────╯
✓ OK Resource tree displayed

agents resource link-child

Purpose Manually link one resource as a child of another. The child resource must already be registered. The resource types must be compatible (the parent type must allow the child type). A resource can have multiple parents.

Arguments

• --parent/-p RESOURCE: Parent resource name.
• --child/-c RESOURCE: Child resource name.

Examples

$ agents resource link-child --parent local/api-repo --child local/docs
╭─ Child Linked ────────────────────────╮
│ Parent: local/api-repo              │
│ Child: local/docs                    │
│ Child Type: fs-mount                 │
│ Status: linked                       │
╰───────────────────────────────────────╯
✓ OK Child resource linked

agents resource unlink-child

Purpose Remove a manual parent/child link between two resources. Auto-discovered links cannot be manually unlinked.

Arguments

• --parent/-p RESOURCE: Parent resource name.
• --child/-c RESOURCE: Child resource name.
• --yes: Skip confirmation prompt.

Examples

$ agents resource unlink-child --parent local/api-repo --child local/docs
Unlink local/docs from parent local/api-repo? [y/N]: y
╭─ Child Unlinked ──────────────────────╮
│ Parent: local/api-repo              │
│ Child: local/docs                    │
│ Status: unlinked                     │
╰───────────────────────────────────────╯
✓ OK Child resource unlinked

agents plan

Purpose Manage plans through the Action -> Strategize -> Execute -> Apply lifecycle.

agents plan list

Purpose List plans with optional filtering.

Arguments

• --phase PHASE: Filter by phase.
• --state STATE: Filter by processing state.
• --project PROJECT: Filter by project.
• --action ACTION: Filter by action name.

Examples

$ agents --format table plan list --phase execute
╭─ Plans ──────────────────────────────────────────────────────────────────────────────╮
│ ID        Phase    State       Action               Project            Elapsed  │
│ ────────  ───────  ──────────  ───────────────────  ─────────────────  ───────── │
│ 01HXM7A9  execute  processing  local/code-coverage  local/api-service  00:01:12  │
╰──────────────────────────────────────────────────────────────────────────────────────╯
╭─ Filters ──────╮
│ Phase: execute │
│ State: (any)   │
│ Project: (any) │
│ Action: (any)  │
╰────────────────╯
╭─ Summary ─────────╮
│ Total: 1          │
│ Processing: 1    │
│ Completed: 0     │
│ Errored: 0        │
╰───────────────────╯
✓ OK 1 plan listed

agents plan use

Purpose Apply an action to one or more projects and start the Strategize phase.

Arguments

• <ACTION_NAME>: Action name.
• <PROJECT>: One or more project names (positional arguments, repeatable).
• --arg/-a name=value: Action argument (repeatable).
• --automation-profile PROFILE: Automation profile name (e.g., trusted, autonomous, local/careful-auto). Overrides the profile inherited from the action, project, or global config.
• --strategy-actor ACTOR: Override the action's strategy actor for this plan.
• --execution-actor ACTOR: Override the action's execution actor for this plan.
• --estimation-actor ACTOR: Override the action's estimation actor for this plan.
• --invariant-actor ACTOR: Override the action's Invariant Reconciliation Actor for this plan.
• --invariant TEXT: Invariant to attach to the created plan (repeatable). These are added as plan-level invariants in addition to any invariants inherited from the action, project, or global scope.

All actor arguments (--strategy-actor, --execution-actor, --estimation-actor, --invariant-actor) are optional overrides. When provided, they replace whatever was set when creating the action. When omitted, the action's configured actors are used.

Examples

$ agents plan use local/code-coverage local/api-service \
  --arg target_coverage_percent=85 --automation-profile trusted
╭─ Plan Created ──────────────────────╮
│ Plan ID: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: strategize                   │
│ Action: local/code-coverage         │
│ Project: local/api-service          │
│ Automation: review                  │
│ Attempt: 1                          │
╰─────────────────────────────────────╯
╭─ Inputs ─────────────────────╮
│ - target_coverage_percent=85 │
│ - automation_profile=trusted  │
╰──────────────────────────────╯
╭─ Actors ────────────────────────╮
│ Strategy: local/strategist      │
│ Execution: local/executor       │
│ Estimation: (none)              │
╰─────────────────────────────────╯
╭─ Automation ─────────────────────────╮
│ Profile: supervised                  │
│ Source: project default              │
│ Read-Only: no                        │
╰──────────────────────────────────────╯
╭─ Context ───────────────────────╮
│ Resources: 2 (repo, db)        │
│ Indexed Files: 347             │
│ View: strategize               │
│ Hot Token Budget: 12,000       │
╰─────────────────────────────────╯
╭─ Next Steps ─────────────────────────────────────╮
│ - agents plan execute 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ - agents plan status 01HXM8C2ZK4Q7C2B3F2R4VYV6J  │
│ - agents plan tree 01HXM8C2ZK4Q7C2B3F2R4VYV6J    │
╰──────────────────────────────────────────────────╯
✓ OK Plan created

agents plan execute

Purpose Start or resume execution for a plan.

Arguments

• <PLAN_ID>: Plan ID (required).

Examples

$ agents plan execute 01HXM8C2ZK4Q7C2B3F2R4VYV6J
╭─ Execution ──────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: execute                   │
│ Sandbox: git_worktree            │
│ Worker: local/executor           │
│ Started: 12:58:10                │
│ Attempt: 1                       │
╰──────────────────────────────────╯
╭─ Sandbox ──────────────────────────────────╮
│ Strategy: git_worktree                   │
│ Path: /repos/api/.worktrees/plan-01HXM8 │
│ Branch: cleveragents/plan-01HXM8C2      │
│ Status: active                           │
╰────────────────────────────────────────────╯
╭─ Strategy Summary ─────────────────────╮
│ Decisions: 8                          │
│ Invariants: 2                         │
│ Planned Child Plans: 2+               │
│ Estimated Files: ~12                  │
│ Risk: low                              │
╰────────────────────────────────────────╯
╭─ Progress ────────╮
│ ⏳ Collect context │
│ • Run tools       │
│ • Build changeset │
│ • Validate        │
╰───────────────────╯
✓ OK Execution started

agents plan apply

Purpose Apply sandboxed changes to real resources.

Arguments

• <PLAN_ID>: Plan ID.
• --yes: Skip confirmation.

Examples

$ agents plan apply 01HXM8C2ZK4Q7C2B3F2R4VYV6J
Apply changes for plan 01HXM8C2ZK4Q7C2B3F2R4VYV6J? [y/N]: y
╭─ Apply Summary ─────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J    │
│ Artifacts: 6 files updated          │
│ Changes: 42 insertions, 9 deletions │
│ Project: local/api-service          │
│ Applied At: 2026-02-08 13:04        │
╰─────────────────────────────────────╯
╭─ Validation ───────────────────╮
│ Tests: passed (24/24)          │
│ Lint: passed (0 warnings)      │
│ Type Check: passed (0 errors)  │
│ Duration: 12.4s                │
╰────────────────────────────────╯
╭─ Sandbox Cleanup ─────────╮
│ Worktree: removed        │
│ Branch: merged to main   │
│ Checkpoint: archived     │
╰───────────────────────────╯
╭─ Plan Lifecycle ────────────────────────╮
│ Phase: applied                         │
│ State: complete                        │
│ Total Duration: 00:06:14              │
│ Total Cost: $0.0847                    │
│ Decisions Made: 8                     │
│ Child Plans: 2 (completed)              │
╰─────────────────────────────────────────╯
╭─ Next Steps ──────╮
│ - Review git diff │
│ - Commit changes  │
╰───────────────────╯
✓ OK Changes applied

agents plan status

Purpose Show detailed status for a plan.

Arguments

• <PLAN_ID>: Plan ID (required).

Examples

$ agents plan status 01HXM8C2ZK4Q7C2B3F2R4VYV6J
╭─ Plan Status ────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: execute                   │
│ State: processing                │
│ Action: local/code-coverage      │
│ Project: local/api-service       │
│ Automation: review               │
│ Attempt: 1                       │
╰──────────────────────────────────╯
╭─ Progress ───────╮
│ ✓ Strategize     │
│ ⏳ Execute        │
│ • Apply (queued) │
╰──────────────────╯
╭─ Timing ──────────╮
│ Started: 12:57:01 │
│ Elapsed: 00:01:12 │
│ ETA: 00:03:45     │
╰───────────────────╯
╭─ Execution Detail ──────────╮
│ Sandbox: git_worktree       │
│ Tool Calls: 8              │
│ Files Modified: 3          │
│ Child Plans: 1/2 complete  │
│ Checkpoints: 2 created     │
╰─────────────────────────────╯
╭─ Cost ───────────────╮
│ Tokens Used: 12,420 │
│ Cost So Far: $0.041 │
│ Estimated: $0.085   │
╰──────────────────────╯
✓ OK Status refreshed

agents plan cancel

Purpose Cancel a plan that is not terminal.

Arguments

• <PLAN_ID>: Plan ID.
• --reason/-r TEXT: Optional reason.

Examples

$ agents plan cancel 01HXM8C2ZK4Q7C2B3F2R4VYV6J --reason "blocked on credentials"
╭─ Plan Cancelled ─────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: execute                   │
│ Reason: blocked on credentials   │
│ State: cancelled                 │
│ Cancelled At: 13:02:15          │
╰──────────────────────────────────╯
╭─ Sandbox ────────────────╮
│ Status: preserved        │
│ Files Modified: 3       │
│ Checkpoints: 2          │
╰──────────────────────────╯
╭─ Child Plans ─────────────╮
│ Completed: 1             │
│ Cancelled: 1             │
│ Artifacts Preserved: yes │
╰───────────────────────────╯
╭─ Recovery ────────────────────────────────────────╮
│ - Resolve credentials                             │
│ - Run agents plan execute 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
╰───────────────────────────────────────────────────╯
✓ OK Plan cancelled

agents plan tree

Purpose Render the decision tree for a plan.

Arguments

• <PLAN_ID>: Plan ID (required).
• --show-superseded: Include superseded decisions.

Examples

$ agents plan tree 01HXM8C2ZK4Q7C2B3F2R4VYV6J
╭─ Decision Tree ──────────────────────────────────────────────────────────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J                                                       │
│ ├─ [prompt_definition] "Increase test coverage to 85%"                                │
│ ├─ [invariant_enforced] "Prioritize financial transaction and user mgmt code"       │
│ ├─ [invariant_enforced] "All API calls over TCP must be mocked"                     │
│ ├─ [strategy_choice] "Prioritize auth and payments" (confidence: 0.82)           │
│ ├─ [subplan_parallel_spawn] "Implement auth and payment modules, in parallel"        │
│ │  ├─ [subplan_spawn] "Write auth tests" → Plan: 01HXM9F1A                          │
│ │  └─ [subplan_spawn] "Write payment tests" → Plan: 01HXM9F2B                       │
│ └─ [subplan_parallel_spawn] "Write tests for remaining modules"                      │
│    └─ ...                                                                                │
╰──────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Tree Summary ─────────────╮
│ Nodes: 9                  │
│ Depth: 3                  │
│ Child Plans: 2+           │
│ Invariants: 2            │
│ Superseded: 0 (hidden)     │
╰────────────────────────────╯
╭─ Child Plans ──────────────────────────────────────╮
│ ID          Name           Phase    State     │
│ ──────────  ─────────────  ───────  ───────── │
│ 01HXM9F1A   auth-tests     execute  processing │
│ 01HXM9F2B   payment-tests  execute  queued     │
╰────────────────────────────────────────────────────╯
╭─ Decision IDs (for correction) ──────────────────╮
│ Root: 01HXM9A0B1Q2W3R5G8Z0P4Q1X8             │
│ Invariant 1: 01HXM9A0C1R3X4S6G9Z1P5Q2Y9    │
│ Invariant 2: 01HXM9A0D2S4Y5T7H0Z2P6Q3Z0    │
│ Strategy: 01HXM9A1C2Q7W3R5G8Z0P4Q1X9         │
│ Parallel 1: 01HXM9A1D3R8X5S7H1Z2P5Q2Y0     │
│ Spawn Auth: 01HXM9A2D3Q8W4R6H9Z1P5Q2X0      │
│ Spawn Payment: 01HXM9A3E4Q9W5R7I0Z2P6Q3X1   │
│ Parallel 2: 01HXM9A4F5Q0W6R8J1Z3P7Q4X2     │
╰──────────────────────────────────────────────────╯
✓ OK Decision tree rendered

agents plan explain

Purpose Show a detailed explanation for a decision.

Arguments

• <DECISION_ID>: Decision ID.
• --show-context: Include the context snapshot.
• --show-reasoning: Include raw model reasoning if available.

Examples

$ agents plan explain 01HXM9A1C2Q7W3R5G8Z0P4Q1X9 --show-context
╭─ Decision ─────────────────────────────────────╮
│ ID: 01HXM9A1C2Q7W3R5G8Z0P4Q1X9                 │
│ Type: strategy_choice                          │
│ Question: Which modules should be prioritized? │
│ Chosen: Auth and payments                      │
│ Confidence: 0.82                               │
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J              │
│ Sequence: 2 of 5                               │
│ Created: 2026-02-08 12:58                     │
╰────────────────────────────────────────────────╯
╭─ Alternatives Considered ──────────────────────╮
│ 1. Auth and payments (chosen)                 │
│ 2. User module first (coverage 71%, med risk) │
│ 3. All modules equally (spread thin)          │
╰────────────────────────────────────────────────╯
╭─ Impact ──────────────────────╮
│ Downstream Decisions: 3     │
│ Downstream Child Plans: 2  │
│ Artifacts Produced: 5      │
│ Correction Impact: medium  │
╰───────────────────────────────╯
╭─ Context Snapshot ───────────────╮
│ - Coverage < 70% in auth         │
│ - Payments failures last release │
│ - Auth: 12 files, 45% coverage   │
│ - Payments: 8 files, 52% cover.  │
│ Hot Context Hash: sha256:4b2e... │
╰──────────────────────────────────╯
╭─ Rationale ───────────────────────────────────────╮
│ Auth and payment modules have the lowest coverage │
│ and highest business risk. Auth handles security  │
│ tokens, payments handles money. Both had bugs in  │
│ the last release traceable to missing tests.      │
╰───────────────────────────────────────────────────╯
╭─ Correction ──────────────────────────────────────────────╮
│ agents plan correct 01HXM9A1C2Q7W3R5G8Z0P4Q1X9           │
│   --mode revert --guidance "Prioritize payments first..." │
╰───────────────────────────────────────────────────────────╯
✓ OK Decision explained

agents plan correct

Purpose Correct a decision either by reverting and re-executing or by appending a fix.

Arguments

• <DECISION_ID>: Decision ID.
• --mode revert|append: Correction mode.
• --guidance/-g TEXT: Guidance text.
• --dry-run: Show impact without executing.
• --yes: Skip confirmation for revert mode.

Examples

$ agents plan correct 01HXM9A1C2Q7W3R5G8Z0P4Q1X9 --mode revert \
  --guidance "Prioritize payments first" --yes
╭─ Correction ─────────────────────────────────────╮
│ Mode: revert                                     │
│ Impact: 3 decisions, 2 child plans, 5 artifacts     │
│ New Decision: 01HXM9B7Z3Q1Q8K2E9H7K3W2M8         │
│ Corrects: 01HXM9A1C2Q7W3R5G8Z0P4Q1X9            │
│ Attempt: 2                                       │
╰──────────────────────────────────────────────────╯
╭─ Affected Subtree ──────────────╮
│ Decisions Invalidated: 3      │
│ Child Plans Rolled Back: 2   │
│ Artifacts Archived: 5        │
│ Unaffected Decisions: 2      │
╰─────────────────────────────────╯
╭─ Sandbox Rollback ─────────────╮
│ Checkpoint: cp_01HXM8C2       │
│ Files Reverted: 5             │
│ Status: restored              │
╰────────────────────────────────╯
╭─ Recompute ──────────────╮
│ Queued: 2 child plans   │
│ ETA: 4m                  │
╰──────────────────────────╯
╭─ History ──────────────────────────────────────────╮
│ - Original decision superseded                      │
│ - Prior artifacts archived for comparison           │
│ - agents plan diff --correction 01HXM9B7Z3Q1Q8K2.. │
╰─────────────────────────────────────────────────────╯
✓ OK Correction applied

agents plan diff

Purpose Show diffs for a plan or a correction attempt.

Arguments

• <PLAN_ID>: Show diff for a plan (positional argument). Mutually exclusive with --correction.
• --correction CORRECTION_ATTEMPT_ID: Show diff for a correction attempt instead.

Examples

$ agents plan diff 01HXM8C2ZK4Q7C2B3F2R4VYV6J
╭─ Diff Summary ─────────────────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J            │
│ Project: local/api-service                  │
│ Files Changed: 2                             │
│ Insertions: 12                               │
│ Deletions: 4                                 │
│ Net Change: +8 lines                         │
╰────────────────────────────────────────────────╯
╭─ Files ───────────────────────────────╮
│ Path                 Change  Status   │
│ ───────────────────  ──────  ──────── │
│ src/auth/session.py  +8 -2   modified │
│ src/auth/tokens.py   +4 -2   modified │
╰───────────────────────────────────────╯
╭─ Patch Preview ─────────────────────────╮
│ --- a/src/auth/session.py              │
│ +++ b/src/auth/session.py              │
│ @@ -12,4 +12,10 @@                      │
│ - import jwt                            │
│ + import sessionlib                     │
│ - def validate_token(...)               │
│ + def validate_session(...)             │
│ --- a/src/auth/tokens.py               │
│ +++ b/src/auth/tokens.py               │
│ @@ -5,3 +5,7 @@                         │
│ - TOKEN_EXPIRY = 3600                    │
│ + TOKEN_EXPIRY = 7200                    │
╰─────────────────────────────────────────╯
╭─ Risk Assessment ────────────────╮
│ API Compatibility: preserved    │
│ Test Coverage: maintained       │
│ Breaking Changes: none detected │
╰──────────────────────────────────╯
✓ OK Diff generated

agents plan artifacts

Purpose List artifacts produced by a plan.

Arguments

• <PLAN_ID>: Plan ID.

Examples

$ agents plan artifacts 01HXM8C2ZK4Q7C2B3F2R4VYV6J
╭─ Artifacts ──────────────────────────────────────────────╮
│ Path                   Type   Size    Change     Child Plan │
│ ─────────────────────  ─────  ──────  ─────────  ─────── │
│ src/auth/session.py    write  2.1 KB  +8 -2      root    │
│ tests/test_session.py  write  4.7 KB  +47 -0     root    │
│ src/auth/tokens.py     edit   1.8 KB  +4 -2      root    │
│ tests/test_tokens.py   write  3.2 KB  +32 -0     auth    │
╰──────────────────────────────────────────────────────────╯
╭─ Summary ──────────╮
│ Total: 4           │
│ Writes: 2 (new)    │
│ Edits: 2 (modified) │
│ Deletes: 0         │
│ Total Size: 11.8 KB │
╰────────────────────╯
╭─ By Plan ─────────────────╮
│ Root Plan: 3 artifacts    │
│ auth-tests: 1 artifact   │
│ payment-tests: (pending) │
╰───────────────────────────╯
✓ OK 4 artifacts listed

agents plan prompt

Purpose Provide additional guidance to a plan, typically when it is errored or awaiting input.

Arguments

• <PLAN_ID>: Plan ID.
• "<GUIDANCE>": Guidance text.

Examples

$ agents plan prompt 01HXM8C2ZK4Q7C2B3F2R4VYV6J "Use mocks for database tests"
╭─ Guidance Added ───────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J       │
│ Guidance: Use mocks for database tests │
│ Scope: next execution step             │
│ Phase: execute                          │
│ State: errored → processing            │
╰────────────────────────────────────────╯
╭─ Decision Created ────────────────────────╮
│ Type: user_intervention                 │
│ ID: 01HXM9C5G7R2X8S3K4Z5Q8R6Y3        │
│ Parent: 01HXM9A1C2Q7W3R5G8Z0P4Q1X9     │
╰───────────────────────────────────────────╯
╭─ Queue ────╮
│ Pending: 1 │
│ Applied: 0 │
╰────────────╯
✓ OK Guidance queued

agents plan rollback

Purpose Rollback a plan sandbox to a checkpoint.

Arguments

• <PLAN_ID>: Plan ID.
• <CHECKPOINT_ID>: Checkpoint ID.
• --yes: Skip confirmation.

Examples

$ agents plan rollback 01HXM8C2ZK4Q7C2B3F2R4VYV6J cp_01HXM8C2
Rollback plan 01HXM8C2ZK4Q7C2B3F2R4VYV6J to cp_01HXM8C2? [y/N]: y
╭─ Rollback Summary ───────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Checkpoint: cp_01HXM8C2          │
│ Label: before auth refactor      │
│ Files: 6 reverted                │
╰──────────────────────────────────╯
╭─ Changes Reverted ──────────────────╮
│ File                    Action     │
│ ──────────────────────  ────────── │
│ src/auth/session.py     restored   │
│ src/auth/tokens.py      restored   │
│ tests/test_session.py   removed    │
│ tests/test_tokens.py    removed    │
│ src/auth/fixtures.py    restored   │
│ src/auth/__init__.py    restored   │
╰─────────────────────────────────────╯
╭─ Impact ──────────────────────────────╮
│ Child Plans Invalidated: 2          │
│ Sandbox: restored to cp_01HXM8C2   │
│ Decisions After CP: 2 discarded     │
│ Tool Calls After CP: 5 undone      │
╰───────────────────────────────────────╯
╭─ Post-Rollback State ─────────╮
│ Phase: execute                │
│ State: queued (awaiting input) │
│ Checkpoints Remaining: 2     │
╰───────────────────────────────╯
✓ OK Rollback complete

agents action

Purpose Manage reusable actions.

agents action create

Purpose Create a new action template from a YAML configuration file. The --config file is required and must fully define the action. CLI options provided alongside --config act as optional overrides for values defined in the configuration file.

Arguments

• [<NAME>]: Namespaced action name (optional when provided in config file).
• --config/-c FILE: YAML configuration file defining the action (required). The file must fully define the action. Any CLI options provided alongside --config override the corresponding values in the file.
• --strategy-actor ACTOR: Override the Strategize actor from config.
• --execution-actor ACTOR: Override the Execution actor from config.
• --definition-of-done TEXT: Override the completion criteria from config.
• --description TEXT: Short description.
• --long-description TEXT: Long description.
• --arg/-a spec: Argument definition (repeatable).
• --reusable/--no-reusable: Keep action after use.
• --read-only: Read-only action.
• --available: Make action available immediately.
• --estimation-actor ACTOR: Optional estimation actor.
• --invariant-actor ACTOR: Invariant Reconciliation Actor for this action. Carried forward to plans created from this action (can be overridden via agents plan use --invariant-actor).
• --automation-profile PROFILE: Default automation profile for plans created from this action.
• --invariant TEXT: Invariant to attach to this action (repeatable). These invariants are carried forward as plan-level invariants when the action is used.

The --config file must define strategy-actor, execution-actor, and definition-of-done. Any of these can be overridden via CLI flags.

Examples

$ agents action create --config ./actions/code-coverage.yaml \
  --available local/code-coverage
╭─ Action Created ──────────────────────╮
│ Name: local/code-coverage             │
│ ID: 01HXMAY3D1JQ0C3G1H0Q7B2W7M        │
│ State: available                      │
│ Strategy Actor: local/strategist      │
│ Execution Actor: local/executor       │
│ Reusable: yes                         │
│ Read Only: no                         │
│ Created: 2026-02-08 12:20             │
╰───────────────────────────────────────╯
╭─ Definition of Done ─╮
│ Coverage reaches 85% │
╰──────────────────────╯
╭─ Arguments ──────────────────────────────────────────────────────╮
│ Name                     Type    Required  Description          │
│ ───────────────────────  ──────  ────────  ───────────────────── │
│ target_coverage_percent  int     yes       Target coverage %    │
│ test_command             string  no        Test framework to use │
╰──────────────────────────────────────────────────────────────────╯
╭─ Automation ─────────────────────────╮
│ Profile: supervised                  │
│ Source: default                       │
╰──────────────────────────────────────╯
╭─ Usage ─────────────────────────────────────────────────────────────────╮
│ agents plan use local/code-coverage local/api-service                    │
│   --arg target_coverage_percent=85                                      │
╰─────────────────────────────────────────────────────────────────────────╯
✓ OK Action created

Creating an action entirely from a YAML configuration file:

$ agents action create local/code-coverage --config ./actions/code-coverage.yaml
╭─ Action Created ──────────────────────╮
│ Name: local/code-coverage             │
│ ID: 01HXMAY3D1JQ0C3G1H0Q7B2W7M        │
│ State: draft                          │
│ Strategy Actor: local/strategist      │
│ Execution Actor: local/executor       │
│ Reusable: yes                         │
│ Read Only: no                         │
│ Config: ./actions/code-coverage.yaml    │
│ Created: 2026-02-08 12:20             │
╰───────────────────────────────────────╯
╭─ Definition of Done ─╮
│ Coverage reaches 85% │
╰──────────────────────╯
╭─ Arguments ──────────────────────────────────────────────────────╮
│ Name                     Type    Required  Description          │
│ ───────────────────────  ──────  ────────  ───────────────────── │
│ target_coverage_percent  int     yes       Target coverage %    │
│ test_command             string  no        Test framework to use │
╰──────────────────────────────────────────────────────────────────╯
╭─ Automation ─────────────────────────╮
│ Profile: supervised                  │
│ Source: default                       │
╰──────────────────────────────────────╯
✓ OK Action created

Creating an action from a config file with CLI overrides (the --execution-actor overrides the value from the YAML, and --available is added):

$ agents action create local/code-coverage \
  --config ./actions/code-coverage.yaml \
  --execution-actor local/fast-executor \
  --available
╭─ Action Created ──────────────────────╮
│ Name: local/code-coverage             │
│ ID: 01HXMAY3D1JQ0C3G1H0Q7B2W7M        │
│ State: available                      │
│ Strategy Actor: local/strategist      │
│ Execution Actor: local/fast-executor  │
│ Reusable: yes                         │
│ Read Only: no                         │
│ Config: ./actions/code-coverage.yaml    │
│ Created: 2026-02-08 12:20             │
╰───────────────────────────────────────╯
╭─ Overrides Applied ──────────────────────────────────────╮
│ execution_actor: local/executor → local/fast-executor  │
│ available: false → true                                │
╰──────────────────────────────────────────────────────────╯
╭─ Definition of Done ─╮
│ Coverage reaches 85% │
╰──────────────────────╯
✓ OK Action created

agents action list

Purpose List actions with optional filters.

Arguments

• --namespace/-n NS: Filter by namespace.
• --state/-s STATE: Filter by state.
• --available: Show only available actions.

Examples

$ agents action list --available
╭─ Actions ──────────────────────────────────────────────────────────────────────────────────╮
│ Name                 State      Strategy Actor    Execution Actor  Reusable  Plans │
│ ───────────────────  ─────────  ────────────────  ───────────────  ────────  ───── │
│ local/code-coverage  available  local/strategist  local/executor   ✓         3     │
╰──────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Filters ────────╮
│ State: available │
│ Namespace: (any) │
╰──────────────────╯
╭─ Summary ────────────╮
│ Total: 1             │
│ Available: 1         │
│ Draft: 0              │
│ Archived: 0          │
│ Total Plans Created: 3 │
╰──────────────────────╯
✓ OK 1 action listed

agents action show

Purpose Show details for an action.

Arguments

• <ACTION_NAME>: Action name.

Examples

$ agents action show local/code-coverage
╭─ Action Details ──────────────────────╮
│ Name: local/code-coverage             │
│ ID: 01HXMAY3D1JQ0C3G1H0Q7B2W7M        │
│ State: available                      │
│ Strategy Actor: local/strategist      │
│ Execution Actor: local/executor       │
│ Reusable: yes                         │
│ Read Only: no                         │
│ Created: 2026-02-08 12:20             │
╰───────────────────────────────────────╯
╭─ Definition of Done ─╮
│ Coverage reaches 85% │
╰──────────────────────╯
╭─ Arguments ──────────────────────────────────────────────────────╮
│ Name                     Type    Required  Description          │
│ ───────────────────────  ──────  ────────  ───────────────────── │
│ target_coverage_percent  int     yes       Target coverage %    │
│ test_command             string  no        Test framework to use │
╰──────────────────────────────────────────────────────────────────╯
╭─ Automation ─────────────────────────╮
│ Profile: supervised                  │
│ Source: default                       │
╰──────────────────────────────────────╯
╭─ History ────────────────────╮
│ Plans Created: 3           │
│ Plans Completed: 2        │
│ Plans Failed: 0            │
│ Avg Duration: 00:04:30    │
│ Avg Cost: $0.072           │
╰──────────────────────────────╯
╭─ Usage ───────────────────────────────────────────────────────────╮
│ - agents plan use local/code-coverage local/api-service │
│     --arg target_coverage_percent=85                              │
╰───────────────────────────────────────────────────────────────────╯
✓ OK Action loaded

agents action available

Purpose Mark a draft action as available.

Arguments

• <ACTION_NAME>: Action name.

Examples

$ agents action available 01HXMAY3D1JQ0C3G1H0Q7B2W7M
╭─ Action Available ─────────────╮
│ ID: 01HXMAY3D1JQ0C3G1H0Q7B2W7M │
│ State: draft → available       │
│ Name: local/code-coverage      │
╰────────────────────────────────╯
╭─ Visibility ─────╮
│ Namespace: local │
│ Listed: yes      │
│ Usable: yes      │
╰──────────────────╯
╭─ Validation ────────────────────────╮
│ Strategy Actor: resolved           │
│ Execution Actor: resolved          │
│ Definition of Done: present        │
│ Arguments: valid schema            │
╰─────────────────────────────────────╯
✓ OK Action marked available

agents action archive

Purpose Archive an action.

Arguments

• <ACTION_NAME>: Action name.

Examples

$ agents action archive local/old-action
╭─ Action Archived ──────────╮
│ Name: local/old-action     │
│ State: available → archived │
│ Archived: 2026-02-08 12:22 │
╰────────────────────────────╯
╭─ Impact ───────────────────────╮
│ Availability: hidden from list │
│ Existing Plans: unchanged      │
│ Active Plans: 0 affected       │
╰────────────────────────────────╯
╭─ History ─────────────────╮
│ Total Plans: 5          │
│ Completed: 4            │
│ Failed: 1               │
│ Last Used: 2026-02-06  │
╰───────────────────────────╯
✓ OK Action archived

agents automation-profile

Purpose Manage automation profiles — named collections of boolean flags that control which tasks are automated vs. require human approval. Built-in profiles (locked-down, manual, supervised, trusted, autonomous, full-auto) are always available. Custom profiles follow the same <namespace>/<name> naming convention as other entities.

agents automation-profile add

Purpose Register a new custom automation profile from a YAML configuration file. If a profile with the same name already exists, the command fails unless the --update flag is provided.

Arguments

• [<NAME>]: Profile name (optional when provided in config file).
• --config/-c FILE: YAML configuration file defining the profile (required).
• --update: Replace an existing profile with the same name.

Examples

$ agents automation-profile add --config ./profiles/careful-auto.yaml
╭─ Profile Registered ─────────────────────────────────────────────╮
│ Name: local/careful-auto                                       │
│ Description: Autonomous execution with mandatory sandbox       │
│              and manual apply                                    │
│ Created: 2026-02-08 14:30                                      │
╰──────────────────────────────────────────────────────────────────╯
╭─ Flags ────────────────────────────────╮
│ auto_strategize: true               │
│ auto_execute: true                  │
│ auto_apply: false                    │
│ auto_decisions_strategize: true     │
│ auto_decisions_execute: true        │
│ auto_validation_fix: true           │
│ auto_strategy_revision: false        │
│ auto_child_plans: true              │
│ auto_retry_transient: true          │
│ auto_checkpoint_restore: true       │
│ require_sandbox: true               │
│ require_checkpoints: true           │
│ allow_unsafe_tools: false            │
╰────────────────────────────────────────╯
✓ OK Profile registered

agents automation-profile remove

Purpose Remove a custom automation profile. Built-in profiles cannot be removed.

Arguments

• <NAME>: Profile name.
• --yes, -y: Skip confirmation prompt.

Examples

$ agents automation-profile remove local/careful-auto
Remove automation profile local/careful-auto? [y/N]: y
╭─ Profile Removed ──────────╮
│ Name: local/careful-auto │
╰────────────────────────────╯
✓ OK Profile removed

agents automation-profile list

Purpose List all available automation profiles (built-in and custom).

Arguments

• [REGEX]: Optional filter pattern.

Examples

$ agents automation-profile list
╭─ Automation Profiles ─────────────────────────────────────────────────────────╮
│ Name                Source    Auto-Apply  Sandbox  Description              │
│ ──────────────────  ────────  ──────────  ───────  ──────────────────────── │
│ locked-down        built-in  no          yes      Maximum human control    │
│ manual             built-in  no          yes      Human-driven (default)   │
│ supervised         built-in  no          yes      Auto-plan, manual exec   │
│ trusted            built-in  no          yes      Auto-exec, manual apply  │
│ autonomous         built-in  no          yes      Full auto except apply   │
│ full-auto          built-in  yes         no       Complete automation      │
│ local/careful-auto custom    no          yes      Custom careful profile   │
╰───────────────────────────────────────────────────────────────────────────────╯
╭─ Summary ───────────╮
│ Built-in: 6        │
│ Custom: 1          │
│ Total: 7           │
╰─────────────────────╯
✓ OK 7 profiles listed

agents automation-profile show

Purpose Show full details for an automation profile, including all flag values.

Arguments

• <NAME>: Profile name.

Examples

$ agents automation-profile show trusted
╭─ Automation Profile ───────────────────────────────────────────╮
│ Name: trusted                                                 │
│ Source: built-in                                              │
│ Description: Auto-exec, manual apply. Day-to-day development │
╰────────────────────────────────────────────────────────────────╯
╭─ Phase Transitions ──────────────────╮
│ auto_strategize: true              │
│ auto_execute: true                 │
│ auto_apply: false                   │
╰──────────────────────────────────────╯
╭─ Decision Automation ────────────────╮
│ auto_decisions_strategize: true    │
│ auto_decisions_execute: true       │
╰──────────────────────────────────────╯
╭─ Self-Repair ────────────────────────╮
│ auto_validation_fix: true          │
│ auto_strategy_revision: false       │
│ auto_retry_transient: true         │
│ auto_checkpoint_restore: false      │
╰──────────────────────────────────────╯
╭─ Execution Controls ────────────────╮
│ auto_child_plans: true             │
│ require_sandbox: true              │
│ require_checkpoints: true          │
│ allow_unsafe_tools: false           │
╰──────────────────────────────────────╯
✓ OK Profile loaded

agents config

Purpose Manage global configuration values.

agents config set

Purpose Set a configuration key.

Arguments

• <key>: automation-profile, log-level, invariant-actor, format.
• <value>: Value to set.

The invariant-actor key sets the default Invariant Reconciliation Actor used globally. This actor is used to reconcile invariant conflicts when neither the plan nor the project specifies one.

The format key sets the default output rendering format used by all commands. Accepted values are rich, color, table, plain, json, yaml. When set, this value is used unless overridden by the --format CLI flag. See Output Rendering Framework for details.

Examples

$ agents config set automation-profile trusted
╭─ Config Updated ──────────────╮
│ Key: automation-profile        │
│ Value: trusted                │
│ Previous: manual              │
│ Source: config                │
│ Scope: global                 │
╰──────────────────────────────╯
╭─ Effective ────────────────────────╮
│ Sessions: new sessions           │
│ Plans: future plans (unless set) │
│ Existing: unchanged              │
╰────────────────────────────────────╯
╭─ Saved To ──────────────────────────╮
│ File: ~/.cleveragents/config.toml │
│ Line: 8                           │
╰─────────────────────────────────────╯
✓ OK Config updated

agents config get

Purpose Get a configuration value.

Arguments

• <key>: Key to read.

Examples

$ agents config get automation-profile
╭─ Config ──────────────────╮
│ Key: automation-profile    │
│ Value: trusted            │
│ Source: config            │
│ Overridden: no            │
│ Type: string              │
╰───────────────────────────╯
╭─ Origin ──────────────────────────╮
│ File: ~/.cleveragents/config.toml │
│ Line: 8                           │
│ Default: full                     │
╰───────────────────────────────────╯
╭─ Resolution Chain ──────────────╮
│ 1. CLI flag: (not set)          │
│ 2. Env var: (not set)           │
│ 3. Config file: review          │
│ 4. Default: full                │
│ Winner: config file (level 3)   │
╰─────────────────────────────────╯
✓ OK Config read

agents config list

Purpose List all configuration values.

Arguments

None.

Examples

$ agents config list
╭─ Config ───────────────────────────────────────────────╮
│ Key               Value               Source   Modified  │
│ ────────────────  ──────────────────  ───────  ──────── │
│ automation-profile trusted             config   yes       │
│ invariant-actor   local/reconciler    config   yes       │
│ log-level         INFO                default  no        │
╰────────────────────────────────────────────────────────╯
╭─ Overrides ─────╮
│ Env: none       │
│ CLI Flags: none │
╰─────────────────╯
╭─ Config File ───────────────────────╮
│ Path: ~/.cleveragents/config.toml │
│ Size: 284 bytes                   │
│ Valid: yes                        │
╰─────────────────────────────────────╯
✓ OK 6 settings listed

agents invariant

Purpose Manage invariants — named constraints that guide and constrain plan execution. Invariants can be attached to any scope: global (all plans), project (all plans targeting that project), plan (a specific plan and its child plans), or action (carried forward when the action is used). Exactly one scope flag is required for add and list.

agents invariant add

Purpose Add an invariant at the specified scope.

Arguments

• <INVARIANT_TEXT>: The invariant text (positional argument at end of command).
• --global: Attach as a global invariant (applies to all plans).
• --project/-p PROJECT: Attach to a project (applies to all plans targeting this project).
• --plan PLAN_ID: Attach to a plan (plan-level invariant). Repeatable.
• --action ACTION: Attach to an action (action-level invariant). Repeatable.

At least one scope flag (--global, --project, --plan, or --action) must be provided. --plan and --action can be repeated to attach the same invariant to multiple plans or actions.

Examples

$ agents invariant add --global "All public APIs must maintain backward compatibility"
╭─ Invariant Added ──────────────────────────────────────────────────────╮
│ Invariant: All public APIs must maintain backward compatibility      │
│ Scope: global                                                       │
│ ID: inv_01HXM9A1B                                                  │
╰────────────────────────────────────────────────────────────────────────╯
✓ OK Invariant added
$ agents invariant add --project local/api-service "All endpoints must validate auth tokens"
╭─ Invariant Added ──────────────────────────────────────────────╮
│ Project: local/api-service                                   │
│ Invariant: All endpoints must validate auth tokens           │
│ Scope: project                                              │
│ ID: inv_01HXM9A2C                                           │
╰────────────────────────────────────────────────────────────────╯
✓ OK Invariant added
$ agents invariant add --plan 01HXM8C2ZK4Q7C2B3F2R4VYV6J "All database queries must use parameterized statements"
╭─ Invariant Added ─────────────────────────────────────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J                                    │
│ Invariant: All database queries must use parameterized statements    │
│ Scope: plan                                                        │
│ ID: inv_01HXM9G3A                                                  │
╰───────────────────────────────────────────────────────────────────────╯
✓ OK Invariant added
$ agents invariant add --action local/code-coverage "Test files must not import production secrets"
╭─ Invariant Added ──────────────────────────────────────────────────────╮
│ Action: local/code-coverage                                        │
│ Invariant: Test files must not import production secrets             │
│ Scope: action                                                      │
│ ID: inv_01HXM9H4B                                                  │
╰────────────────────────────────────────────────────────────────────────╯
✓ OK Invariant added

agents invariant list

Purpose List invariants at a given scope. Use --effective with --plan to show the final reconciled view of invariants (after precedence resolution) rather than just the invariants directly attached at that scope.

Arguments

• --global: List global invariants.
• --project/-p PROJECT: List invariants attached to a project.
• --plan PLAN_ID: List invariants attached to a plan.
• --action ACTION: List invariants attached to an action.
• --effective: (Only with --plan) Show the reconciled invariant view after precedence resolution across all scopes.

Examples

$ agents invariant list --global
╭─ Global Invariants ──────────────────────────────────────────────────────╮
│ ID              Text                                                  │
│ ──────────────  ────────────────────────────────────────────────────   │
│ inv_01HXM9A1B  All public APIs must maintain backward compatibility     │
│ inv_01HXM9A1C  Payment processing must be idempotent                    │
╰──────────────────────────────────────────────────────────────────────────╯
✓ OK 2 invariants
$ agents invariant list --plan 01HXM8C2ZK4Q7C2B3F2R4VYV6J --effective
╭─ Effective Invariants (Plan 01HXM8C2ZK4Q7C2B3F2R4VYV6J) ─────────────────────────────────╮
│ ID              Source   Text                                                   │
│ ──────────────  ───────  ──────────────────────────────────────────────────────   │
│ inv_01HXM9A1B  global   All public APIs must maintain backward compatibility      │
│ inv_01HXM9A2C  project  All endpoints must validate auth tokens                   │
│ inv_01HXM9G3A  plan     All database queries must use parameterized statements    │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
│ Conflicts Resolved: 1                                                              │
│   Global "Use shared DB pool" overridden by plan "All database queries must use    │
│   parameterized statements"                                                         │
✓ OK 3 effective invariants (1 global, 1 project, 1 plan; 1 conflict resolved)

agents invariant remove

Purpose Remove an invariant by ID. The invariant is removed from whichever scope it was originally attached to.

Arguments

• <INVARIANT_ID>: Invariant ID to remove.
• --yes: Skip confirmation.

Examples

$ agents invariant remove inv_01HXM9A1C
Remove invariant inv_01HXM9A1C ("Payment processing must be idempotent", scope: global)? [y/N]: y
╭─ Invariant Removed ──────────────────────────────────────────────╮
│ Removed: Payment processing must be idempotent                 │
│ Scope: global                                                  │
│ ID: inv_01HXM9A1C                                             │
╰──────────────────────────────────────────────────────────────────╯
✓ OK Invariant removed

Output Rendering Framework

Overview

CleverAgents uses a unified Output Rendering Framework to decouple command output data from its visual presentation. Every CLI command produces structured output through a common abstraction layer, and the active format determines how that output is rendered to the terminal (or piped to external consumers). The format is set via the global --format flag, the format config key, or defaults to rich.

The framework is reactive-first: commands do not build a static data structure and hand it to a renderer. Instead, commands open an output session, create element handles for each piece of output (a panel, a table, a progress indicator), and write data to those handles — potentially from multiple concurrent producers. The session coordinates with a materialization strategy selected by the active format, which decides when and how each element's content reaches the terminal. A rich session renders updates in-place as they arrive; a plain session buffers each element and flushes sequentially; a json session accumulates everything and serializes once at the end. Producer code is format-agnostic — it writes to handles without knowing which format is active.

This architecture is designed for modularity, extensibility, and future-proofing — the same session-based output can be consumed by the CLI, a future TUI, a web frontend, or programmatic integrations. The design uses a pipeline of composable stages: session lifecycle management, typed element handles, event-driven materialization, and format-specific element rendering.

Architecture

Rendering Pipeline

All CLI output flows through a five-stage reactive pipeline:

Command Logic ──► OutputSession ──► ElementHandles ──► MaterializationStrategy ──► Terminal/Pipe
                  (lifecycle)       (typed producers)   (format-driven policy)      (stdout/stderr)

1. Command Logic opens an OutputSession and creates typed element handles — PanelHandle, TableHandle, ProgressHandle, etc. — for each piece of output the command will produce. Handles are created in declaration order, which determines the canonical order in which elements appear in sequential formats.

2. OutputSession is the central coordinator. It owns the set of active handles, tracks their lifecycle (open → writing → closed), emits ElementEvent objects to the active materialization strategy, and provides a snapshot() method that returns a static StructuredOutput representing the accumulated state at any point in time.

3. ElementHandles are the producer-facing API. Each handle is typed for a specific element kind (panel, table, tree, etc.) and exposes write methods appropriate to that kind (add_row(), set_entry(), set_step_status(), etc.). Handles are thread-safe — multiple concurrent coroutines or threads can write to different handles simultaneously. Handles are format-agnostic — the producer never knows or cares which format is active.

4. MaterializationStrategy is a polymorphic observer selected by the active format. It receives ElementEvent notifications from the session and decides when and how to render content. Each strategy delegates the actual visual rendering of an element's accumulated state to a paired ElementRenderer.

5. Terminal/Pipe receives the final byte stream. The framework auto-detects whether stdout is a TTY and degrades gracefully (e.g., rich falls back to table when piped to a non-TTY unless --format rich was explicitly set).

OutputSession

The OutputSession is the core abstraction that replaces direct construction of static output objects. Commands receive a session (typically injected by the CLI framework) and interact with it throughout their execution:

class OutputSession:
    """A live output document that coordinates element production and materialization.
    
    The session manages the lifecycle of all output elements for a single command
    invocation. It is the bridge between format-agnostic producer code and the
    format-specific materialization strategy.
    
    Thread Safety:
        The session is thread-safe. Multiple producers may create and write to
        handles concurrently. The session serializes event delivery to the
        materialization strategy using an internal event queue.
    
    Lifecycle:
        session = OutputSession.open(command, strategy)
        handle_a = session.panel("Title")       # create handles
        handle_b = session.table("Results", ...) 
        handle_a.set_entry(...)                  # write to handles (concurrent OK)
        handle_b.add_row(...)
        handle_a.close()                         # close handles when done
        handle_b.close()
        session.close()                          # finalize the session
    """
<span style="opacity: 0.7;"># --- Session lifecycle ---</span>

command: <span style="color: cyan;">str</span>                                  <span style="opacity: 0.7;"># The command that owns this session</span>
session_id: <span style="color: cyan;">str</span>                               <span style="opacity: 0.7;"># Unique session identifier (ULID)</span>
created_at: <span style="color: cyan;">datetime</span>                          <span style="opacity: 0.7;"># Session creation timestamp</span>

_strategy: MaterializationStrategy            <span style="opacity: 0.7;"># The active materialization strategy</span>
_handles: <span style="color: cyan;">OrderedDict</span>[<span style="color: cyan;">str</span>, ElementHandle]     <span style="opacity: 0.7;"># handle_id → handle, in declaration order</span>
_event_queue: asyncio.Queue[ElementEvent]     <span style="opacity: 0.7;"># Internal event queue for serialization</span>
_state: SessionState                          <span style="opacity: 0.7;"># &quot;open&quot; | &quot;closing&quot; | &quot;closed&quot;</span>
_lock: threading.Lock                         <span style="opacity: 0.7;"># Protects handle creation/removal</span>

<span style="color: yellow;">@classmethod</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">open</span>(cls, command: <span style="color: cyan;">str</span>, strategy: MaterializationStrategy,
         metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;OutputSession&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Open a new output session.</span>

        
        Called by the CLI framework before command execution. The strategy is
        selected based on the resolved format (see Format Resolution).
        
        Args:
            command: The command string (e.g., "project show").
            strategy: The materialization strategy for the active format.
            metadata: Optional command metadata (user, timestamp, etc.).
        
        Returns:
            A new OutputSession ready for element creation.
        """
...
<span style="opacity: 0.7;"># --- Element handle factories ---</span>
<span style="opacity: 0.7;"># Each factory creates a typed handle, registers it with the session in</span>
<span style="opacity: 0.7;"># declaration order, and emits an ElementCreated event to the strategy.</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">panel</span>(self, title: <span style="color: cyan;">str</span>, *,
          border_style: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;rounded&quot;</span>,
          priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
          collapse_hint: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;auto&quot;</span>,
          metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;PanelHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a panel element handle for key-value pair output.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">table</span>(self, title: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span>, *,
          columns: <span style="color: cyan;">list</span>[<span style="color: #66cc66;">&quot;ColumnDef&quot;</span>],
          summary: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
          max_rows_hint: <span style="color: cyan;">int</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
          sort_key: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
          priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
          collapse_hint: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;auto&quot;</span>,
          metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;TableHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a table element handle for tabular data.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">tree</span>(self, root_label: <span style="color: cyan;">str</span>, *,
         root_style: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
         max_depth_hint: <span style="color: cyan;">int</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
         show_guides: <span style="color: cyan;">bool</span> = <span style="color: magenta; font-weight: 600;">True</span>,
         priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
         collapse_hint: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;auto&quot;</span>,
         metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;TreeHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a tree element handle for hierarchical data.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">progress</span>(self, label: <span style="color: cyan;">str</span>, *,
             total: <span style="color: cyan;">int</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
             indeterminate: <span style="color: cyan;">bool</span> = <span style="color: magenta; font-weight: 600;">False</span>,
             steps: <span style="color: cyan;">list</span>[<span style="color: cyan;">str</span>] | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
             priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
             metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;ProgressHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a progress indicator handle.</span>

        
        Args:
            label: Display label for the progress indicator.
            total: Total units of work (None for indeterminate).
            indeterminate: If True, show a spinner instead of a progress bar.
            steps: Named steps to track (creates ProgressStep objects with
                   initial status "pending").
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">status</span>(self, message: <span style="color: cyan;">str</span>, *,
           level: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;info&quot;</span>,
           detail: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
           priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
           metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;StatusHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a status message handle.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">text</span>(self, content: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;&quot;</span>, *,
         wrap: <span style="color: cyan;">bool</span> = <span style="color: magenta; font-weight: 600;">True</span>,
         indent: <span style="color: cyan;">int</span> = 0,
         priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
         metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;TextHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a text block handle.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">code</span>(self, content: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;&quot;</span>, *,
         language: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
         line_numbers: <span style="color: cyan;">bool</span> = <span style="color: magenta; font-weight: 600;">False</span>,
         highlight_lines: <span style="color: cyan;">list</span>[<span style="color: cyan;">int</span>] | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
         priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
         metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;CodeHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a code block handle.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">diff</span>(self, *,
         file_a: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
         file_b: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
         priority: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;normal&quot;</span>,
         metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;DiffHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a diff block handle.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">separator</span>(self, style: <span style="color: cyan;">str</span> = <span style="color: #66cc66;">&quot;line&quot;</span>) -&gt; <span style="color: #66cc66;">&quot;SeparatorHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a visual separator. Separators are auto-closed on creation.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">action_hint</span>(self, commands: <span style="color: cyan;">list</span>[<span style="color: cyan;">str</span>],
                description: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: #66cc66;">&quot;ActionHintHandle&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create an action hint. Action hints are auto-closed on creation.&quot;&quot;&quot;</span>
    ...

<span style="opacity: 0.7;"># --- Session operations ---</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">snapshot</span>(self) -&gt; <span style="color: #66cc66;">&quot;StructuredOutput&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Return a static snapshot of all elements accumulated so far.</span>

        
        The snapshot captures the current state of every handle (open or closed)
        as a StructuredOutput object. This is used by accumulate-mode strategies
        (json/yaml) at session end, and is available at any time for logging,
        debugging, or programmatic inspection.
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">close</span>(self, *, exit_code: <span style="color: cyan;">int</span> = 0) -&gt; <span style="color: #66cc66;">&quot;StructuredOutput&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Close the session and finalize all output.</span>

        
        Any handles still open are force-closed (with a warning logged).
        Emits a SessionEnd event to the strategy. Returns the final
        StructuredOutput snapshot.
        
        Args:
            exit_code: The command's exit code (included in the snapshot).
        
        Returns:
            The final StructuredOutput snapshot.
        """
...
<span style="opacity: 0.7;"># --- Context manager support ---</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">__enter__</span>(self) -&gt; <span style="color: #66cc66;">&quot;OutputSession&quot;</span>:
    <span style="color: magenta; font-weight: 600;">return</span> self

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">__exit__</span>(self, exc_type, exc_val, exc_tb) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Auto-close session on context exit.</span>

        
        If exiting due to an exception, sets exit_code to 1 and emits an
        error status element before closing.
        """
...

Element Handles

Element handles are the producer-facing API. Each handle type wraps a specific element kind and provides methods appropriate to that kind. All handles share a common base:

class ElementHandle(Generic[E]):
    """Base class for all element handles.
    
    An element handle is a write-only view of an output element. Producers use
    handles to incrementally build element content without knowledge of the
    active format or materialization strategy.
    
    Type Parameter:
        E: The OutputElement subclass this handle wraps (e.g., Panel, Table).
    
    Thread Safety:
        Individual handle methods are thread-safe. Multiple threads may call
        methods on *different* handles concurrently. Concurrent writes to the
        *same* handle are serialized via an internal lock.
    
    Lifecycle:
        handle = session.table(...)    # Created by session factory
        handle.add_row(...)            # Write operations (zero or more)
        handle.close()                 # Finalize (required unless auto-closed)
    
    Closed Handle Behavior:
        Calling any write method on a closed handle raises ElementClosedError.
    """
handle_id: <span style="color: cyan;">str</span>                       <span style="opacity: 0.7;"># Unique handle identifier (ULID)</span>
element_type: <span style="color: cyan;">str</span>                    <span style="opacity: 0.7;"># Semantic type (&quot;panel&quot;, &quot;table&quot;, etc.)</span>
declaration_index: <span style="color: cyan;">int</span>               <span style="opacity: 0.7;"># Position in session&#x27;s declaration order</span>

_session: OutputSession              <span style="opacity: 0.7;"># Owning session (for event emission)</span>
_element: E                          <span style="opacity: 0.7;"># The accumulated element state</span>
_state: HandleState                  <span style="opacity: 0.7;"># &quot;open&quot; | &quot;closed&quot;</span>
_lock: threading.Lock                <span style="opacity: 0.7;"># Serializes writes to this handle</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">close</span>(self) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Close this handle, signaling that no more data will be written.</span>

        
        Emits an ElementClosed event to the materialization strategy.
        For buffered strategies, this triggers rendering of the element.
        """
...
<span style="color: yellow;">@property</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">is_open</span>(self) -&gt; <span style="color: cyan;">bool</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Whether this handle is still accepting writes.&quot;&quot;&quot;</span>
    ...

<span style="color: yellow;">@property</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">element</span>(self) -&gt; E:
    <span style="color: #66cc66;">&quot;&quot;&quot;The accumulated element state (read-only snapshot).&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">__enter__</span>(self) -&gt; <span style="color: cyan;">Self</span>:
    <span style="color: magenta; font-weight: 600;">return</span> self

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">__exit__</span>(self, exc_type, exc_val, exc_tb) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Auto-close handle on context exit.&quot;&quot;&quot;</span>
    <span style="color: magenta; font-weight: 600;">if</span> self.is_open:
        self.close()

class PanelHandle(ElementHandle[Panel]):
"""Handle for building a Panel element incrementally.
    
    Panels are titled groups of key-value pairs. Entries can be added,
    updated, or removed after creation.
    """
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_entry</span>(self, key: <span style="color: cyan;">str</span>, value: <span style="color: cyan;">str</span>, *,
              style_hint: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
              icon: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set or update a key-value entry in the panel.</span>

        
        If an entry with the given key already exists, it is updated.
        Otherwise, a new entry is appended.
        
        Emits an ElementUpdated event.
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_entries</span>(self, entries: <span style="color: cyan;">dict</span>[<span style="color: cyan;">str</span>, <span style="color: cyan;">str</span>], *,
                style_hints: <span style="color: cyan;">dict</span>[<span style="color: cyan;">str</span>, <span style="color: cyan;">str</span>] | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set multiple entries at once (batch update). Emits a single event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">remove_entry</span>(self, key: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Remove an entry by key. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

class TableHandle(ElementHandle[Table]):
"""Handle for building a Table element incrementally.
    
    Tables are the primary element for streamed data. Rows can be added
    one at a time or in batches as data becomes available from queries,
    API calls, or concurrent operations.
    """
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">add_row</span>(self, row: <span style="color: cyan;">dict</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Append a single row to the table.</span>

        
        The row dict maps column names to cell values. Missing columns
        are filled with None. Extra columns not in the schema are ignored.
        
        Emits an ElementUpdated event (type=row_added).
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">add_rows</span>(self, rows: <span style="color: cyan;">list</span>[<span style="color: cyan;">dict</span>]) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Append multiple rows in a batch. Emits a single ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_summary</span>(self, summary: <span style="color: cyan;">dict</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set or update the summary/aggregation row. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_sort_key</span>(self, column: <span style="color: cyan;">str</span>, *, descending: <span style="color: cyan;">bool</span> = <span style="color: magenta; font-weight: 600;">False</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Change the sort key. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

class TreeHandle(ElementHandle[Tree]):
"""Handle for building a Tree element incrementally.
    
    Trees are built by adding child nodes to existing nodes. The root node
    is created with the handle. Subtrees can be constructed incrementally
    as hierarchical data is discovered.
    """
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">add_child</span>(self, parent_path: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span>, label: <span style="color: cyan;">str</span>, *,
              style_hint: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>,
              collapsed: <span style="color: cyan;">bool</span> = <span style="color: magenta; font-weight: 600;">False</span>,
              metadata: <span style="color: cyan;">dict</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: cyan;">str</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Add a child node to the tree.</span>

        
        Args:
            parent_path: Slash-separated path to the parent node (None = root).
            label: Display label for the new node.
            style_hint: Optional color/style hint.
            collapsed: Whether this node starts collapsed in interactive renderers.
            metadata: Arbitrary data attached to the node.
        
        Returns:
            The full path to the newly created node (for use as parent_path
            in subsequent add_child calls).
        
        Emits an ElementUpdated event.
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_node_style</span>(self, path: <span style="color: cyan;">str</span>, style_hint: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Update the style of an existing node. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

class ProgressHandle(ElementHandle[ProgressIndicator]):
"""Handle for updating a ProgressIndicator element.
    
    Progress handles are unique in that they are expected to receive many
    rapid updates. The materialization strategy may throttle update events
    to avoid overwhelming the terminal (e.g., limiting redraws to 10/sec).
    """
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_progress</span>(self, current: <span style="color: cyan;">int</span>, total: <span style="color: cyan;">int</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Update the progress counter.</span>

        
        Args:
            current: Current progress value.
            total: Total value (can change, e.g., when total is discovered late).
        
        Emits an ElementUpdated event (may be throttled by the strategy).
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_step_status</span>(self, step_label: <span style="color: cyan;">str</span>, status: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Update the status of a named step.</span>

        
        Args:
            step_label: The label of the step to update.
            status: New status — "pending" | "active" | "done" | "error" | "skipped".
        
        Emits an ElementUpdated event.
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_label</span>(self, label: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Update the progress label text. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">increment</span>(self, delta: <span style="color: cyan;">int</span> = 1) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Increment progress by delta. Convenience wrapper around set_progress.&quot;&quot;&quot;</span>
    ...

class StatusHandle(ElementHandle[StatusMessage]):
"""Handle for a status message.
    
    Status handles are typically created and immediately closed (fire-and-forget
    messages). However, they can be kept open for messages that may be revised
    (e.g., a "Working..." status that becomes "Done" or "Failed").
    """
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_message</span>(self, message: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Update the status message text. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_level</span>(self, level: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Change the status level. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_detail</span>(self, detail: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set or clear the detail text. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

class TextHandle(ElementHandle[TextBlock]):
"""Handle for a text block. Supports appending text incrementally."""
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">append</span>(self, text: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Append text to the block. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_content</span>(self, content: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Replace the entire content. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

class CodeHandle(ElementHandle[CodeBlock]):
"""Handle for a code block."""
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_content</span>(self, content: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set the code content. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_language</span>(self, language: <span style="color: cyan;">str</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set the language for syntax highlighting. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_highlight_lines</span>(self, lines: <span style="color: cyan;">list</span>[<span style="color: cyan;">int</span>]) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set lines to highlight. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

class DiffHandle(ElementHandle[DiffBlock]):
"""Handle for a diff block. Hunks can be added incrementally."""
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">add_hunk</span>(self, header: <span style="color: cyan;">str</span>, lines: <span style="color: cyan;">list</span>[<span style="color: #66cc66;">&quot;DiffLine&quot;</span>]) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Add a diff hunk. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">set_stats</span>(self, insertions: <span style="color: cyan;">int</span>, deletions: <span style="color: cyan;">int</span>, **extra: <span style="color: cyan;">int</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Set diff statistics. Emits an ElementUpdated event.&quot;&quot;&quot;</span>
    ...

Element Events

Element handles communicate with the materialization strategy through a typed event system. Events are the sole interface between production (handles) and consumption (strategy) — this indirection is what enables format-agnostic producer code:

class ElementEvent:
    """Base class for all events emitted by element handles."""
    event_type: str                      # "created" | "updated" | "closed"
    handle_id: str                       # The handle that emitted this event
    element_type: str                    # The element kind ("panel", "table", etc.)
    timestamp: datetime                  # When the event occurred
    session_id: str                      # The owning session
class ElementCreated(ElementEvent):
"""Emitted when a new element handle is created via a session factory."""
event_type = "created"
declaration_index: int               # Position in session declaration order
initial_state: OutputElement         # The element's initial state
class ElementUpdated(ElementEvent):
"""Emitted when data is written to an element handle."""
event_type = "updated"
update_type: str                     # Kind-specific: "entry_set", "row_added",
#   "progress_changed", "step_status_changed", etc.
delta: dict                          # The change payload (what was added/modified)
element_snapshot: OutputElement      # The full element state after this update
class ElementClosed(ElementEvent):
"""Emitted when an element handle is closed (no more data will arrive)."""
event_type = "closed"
final_state: OutputElement           # The element's final accumulated state
class SessionEnd(ElementEvent):
"""Emitted when the session itself is closed."""
event_type = "session_end"
exit_code: int
snapshot: "StructuredOutput"         # The complete accumulated output

Element Data Model (Snapshot Types)

Each element handle accumulates state into a typed snapshot object. These are the data classes that represent a fully-built element — they are what the ElementRenderer receives when it is time to paint. They are also the building blocks of the StructuredOutput returned by session.snapshot():

class OutputElement:
    """Base class for all output element snapshot types."""
    element_type: str                    # Semantic type identifier
    metadata: dict                       # Arbitrary metadata (timestamps, IDs, etc.)
    priority: str = "normal"             # "critical" | "normal" | "supplementary"
    collapse_hint: str = "auto"          # "always" | "auto" | "never" — guidance for renderers
                                         #   on whether this element can be collapsed/hidden
class Panel(OutputElement):
"""A titled group of key-value pairs."""
element_type = "panel"
title: str
entries: list[PanelEntry]            # Each entry: key, value, style_hint (color, icon, etc.)
border_style: str = "rounded"        # "rounded" | "square" | "heavy" | "none"
class PanelEntry:
"""A single key-value pair within a Panel."""
key: str
value: str
style_hint: str | None = None        # Color/style for the value (e.g., "success", "warning")
icon: str | None = None              # Optional icon/prefix character
class Table(OutputElement):
"""A tabular data set with typed columns."""
element_type = "table"
title: str | None
columns: list[ColumnDef]             # name, type, alignment, width_hint, sortable
rows: list[dict]                     # Column name → cell value
summary: dict | None                 # Optional aggregation row (totals, counts)
max_rows_hint: int | None            # Suggest truncation for large datasets
sort_key: str | None                 # Default sort column
class ColumnDef:
"""Schema for a single table column."""
name: str                            # Column display name
type: str = "string"                 # "string" | "number" | "boolean" | "datetime" | "id"
alignment: str = "left"              # "left" | "right" | "center"
width_hint: int | None = None        # Suggested character width (None = auto)
sortable: bool = False               # Whether this column can be sorted
style_hint: str | None = None        # Default style for cells in this column
class Tree(OutputElement):
"""A hierarchical tree structure."""
element_type = "tree"
root: TreeNode                       # Recursive node structure
max_depth_hint: int | None           # Suggest depth truncation
show_guides: bool = True             # Whether to show tree guide lines
class TreeNode:
"""A node in a tree structure."""
label: str
style_hint: str | None               # Color/style for this node
children: list["TreeNode"]
collapsed: bool = False              # Hint: start collapsed in interactive renderers
metadata: dict                       # Arbitrary data attached to the node
class StatusMessage(OutputElement):
"""A status line (success, warning, error, info)."""
element_type = "status"
level: str                           # "ok" | "warn" | "error" | "info"
message: str
detail: str | None                   # Optional detail text
class ProgressIndicator(OutputElement):
"""A progress bar or spinner for long-running operations."""
element_type = "progress"
label: str
current: int | None
total: int | None
indeterminate: bool = False          # Spinner mode vs. progress bar mode
steps: list[ProgressStep] | None     # Named steps with status (pending/active/done)
class ProgressStep:
"""A named step within a progress indicator."""
label: str
status: str                          # "pending" | "active" | "done" | "error" | "skipped"
class CodeBlock(OutputElement):
"""A block of source code with optional syntax highlighting."""
element_type = "code"
content: str
language: str | None                 # For syntax highlighting
line_numbers: bool = False
highlight_lines: list[int] | None    # Lines to emphasize
class DiffBlock(OutputElement):
"""A unified diff display."""
element_type = "diff"
hunks: list[DiffHunk]
file_a: str | None
file_b: str | None
stats: dict | None                   # insertions, deletions, etc.
class DiffHunk:
"""A single hunk within a diff."""
header: str                          # @@ line range @@
lines: list[DiffLine]
class DiffLine:
"""A single line in a diff hunk."""
type: str                            # "context" | "add" | "remove"
content: str
line_number_old: int | None
line_number_new: int | None
class TextBlock(OutputElement):
"""A free-form text block (descriptions, rationale, etc.)."""
element_type = "text"
content: str
wrap: bool = True
indent: int = 0
class Separator(OutputElement):
"""A visual separator between logical groups."""
element_type = "separator"
style: str = "line"                  # "line" | "blank" | "double"
class ActionHint(OutputElement):
"""A suggested next-step action for the user."""
element_type = "action_hint"
commands: list[str]                  # Suggested CLI commands
description: str | None
class StructuredOutput:
"""Static snapshot of a complete command output.
    
    This is the accumulated state of all elements at a point in time.
    It is produced by OutputSession.snapshot() and OutputSession.close().
    
    Uses:
    - Final serialization for json/yaml formats
    - Logging and audit trails
    - Programmatic inspection and testing
    - TUI widget data binding (initial state)
    """
command: str                         # The command that produced this output
session_id: str                      # The session that produced this output
elements: list[OutputElement]        # Ordered list of element snapshots
exit_code: int = 0
timing: dict | None                  # start_time, end_time, duration
metadata: dict                       # command-specific metadata

Materialization Strategies

The materialization strategy is the format-side counterpart to the output session. It receives element events and decides when and how to render content. Each strategy is paired with an ElementRenderer that handles the actual visual formatting of individual elements.

The strategy pattern creates a clean separation between timing/ordering policy (when to render) and visual formatting (how to render). This means the same PlainElementRenderer can be used whether elements arrive all-at-once or are streamed concurrently — the strategy handles the coordination.

class MaterializationStrategy(Protocol):
    """Interface for format-driven output materialization.
    
    A materialization strategy receives element lifecycle events from the
    OutputSession and decides when to render element content to the output
    stream. Strategies do not render elements themselves — they delegate
    to a paired ElementRenderer at the appropriate time.
    
    The strategy is the mechanism by which format-agnostic producer code
    produces correct output regardless of format. The producer writes to
    handles; the strategy decides what reaches the terminal and when.
    """
strategy_name: <span style="color: cyan;">str</span>                   <span style="opacity: 0.7;"># &quot;live&quot; | &quot;sequential_buffer&quot; | &quot;accumulate&quot;</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">bind</span>(self, renderer: <span style="color: #66cc66;">&quot;ElementRenderer&quot;</span>,
         terminal_caps: <span style="color: #66cc66;">&quot;TerminalCapabilities&quot;</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Bind this strategy to a renderer and terminal capabilities.</span>

        
        Called once during format resolution, before the session opens.
        The strategy retains a reference to the renderer for use during
        event handling. The output stream is provided separately via
        on_session_begin, since the session owns the stream.
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_session_begin</span>(self, session: OutputSession, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Called when the session opens. The strategy receives the output</span>

        stream and may write preamble (e.g., opening JSON bracket)."""
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_element_created</span>(self, event: ElementCreated) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Called when a new element handle is created.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_element_updated</span>(self, event: ElementUpdated) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Called when data is written to an element handle.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_element_closed</span>(self, event: ElementClosed) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Called when an element handle is closed.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_session_end</span>(self, event: SessionEnd) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Called when the session closes. The strategy may write epilogue.&quot;&quot;&quot;</span>
    ...

class LiveMaterializer(MaterializationStrategy):
"""Materialization strategy for the rich format.
    
    Renders element updates in real-time using terminal cursor movement.
    Multiple elements can be visually active and updating simultaneously.
    The terminal display is a live document that is rewritten in place.
    
    Behavior:
    - on_element_created: Allocates screen region for the element, renders
      initial (possibly empty) visual state.
    - on_element_updated: Re-renders the element in place using cursor
      movement. For progress indicators, updates may be throttled to a
      maximum refresh rate (default: 15 fps) to avoid terminal flooding.
    - on_element_closed: Renders the final state and freezes the screen
      region (no further updates). May apply a visual transition (e.g.,
      spinner resolves to a checkmark).
    - on_session_end: Finalizes the display, moves cursor to end, and
      restores normal terminal scrolling.
    
    Screen Layout:
    Elements are arranged vertically in declaration order. Each element
    occupies a contiguous block of terminal lines. The materializer tracks
    the line offset and height of each element's region. When an element's
    height changes (e.g., a table gains rows), subsequent elements are
    shifted down.
    
    Concurrent Updates:
    Updates from multiple handles are coalesced into a single frame refresh
    at the target frame rate. The materializer maintains a dirty-element set
    and redraws all dirty elements in a single pass per frame.
    """
strategy_name = "live"
_frame_rate: <span style="color: cyan;">float</span> = 15.0            <span style="opacity: 0.7;"># Maximum redraws per second</span>
_element_regions: <span style="color: cyan;">OrderedDict</span>[<span style="color: cyan;">str</span>, ScreenRegion]  <span style="opacity: 0.7;"># handle_id → screen region</span>
_dirty_set: <span style="color: cyan;">set</span>[<span style="color: cyan;">str</span>]                 <span style="opacity: 0.7;"># handle_ids that need redraw</span>
_frame_timer: asyncio.TimerHandle    <span style="opacity: 0.7;"># Coalescing timer for frame redraws</span>

class SequentialBufferMaterializer(MaterializationStrategy):
"""Materialization strategy for plain, color, and table formats.
    
    Buffers element content and renders elements sequentially in declaration
    order. An element's content is rendered to the output stream only when
    its handle is closed. If handles are closed out of declaration order,
    the out-of-order element's rendered content is held in a buffer until
    all preceding elements have been rendered.
    
    This strategy ensures that static, scrolling output formats produce
    coherent sequential output even when producers write to handles
    concurrently and close them in arbitrary order.
    
    Behavior:
    - on_element_created: Records the element's declaration index. No output.
    - on_element_updated: Buffers the update internally. No output.
    - on_element_closed: If this element is the next in declaration order,
      renders it immediately (and any buffered subsequent elements that are
      also closed). Otherwise, buffers the rendered content.
    - on_session_end: Force-renders any remaining buffered elements (handles
      that were never closed, in declaration order).
    
    Example (two tables populated concurrently):
    1. Handle A (index 0) created — table "Resources"
    2. Handle B (index 1) created — table "Validations"
    3. Handle B receives rows, Handle A receives rows (interleaved)
    4. Handle B closes (index 1) — rendered content buffered (waiting for A)
    5. Handle A closes (index 0) — A is rendered to stream, then buffered B
       is rendered to stream
    
    Result: Output shows table A followed by table B, regardless of the
    order in which data arrived or handles closed.
    """
strategy_name = "sequential_buffer"
_next_render_index: <span style="color: cyan;">int</span> = 0          <span style="opacity: 0.7;"># The declaration index to render next</span>
_rendered_buffers: <span style="color: cyan;">dict</span>[<span style="color: cyan;">int</span>, <span style="color: cyan;">str</span>]     <span style="opacity: 0.7;"># index → pre-rendered content (waiting)</span>
_closed_set: <span style="color: cyan;">set</span>[<span style="color: cyan;">int</span>]                <span style="opacity: 0.7;"># Declaration indices of closed elements</span>

class AccumulateMaterializer(MaterializationStrategy):
"""Materialization strategy for json and yaml formats.
    
    Accumulates all element data silently until the session ends, then
    serializes the complete StructuredOutput as a single JSON or YAML
    document.
    
    Behavior:
    - on_element_created: No output.
    - on_element_updated: No output.
    - on_element_closed: No output.
    - on_session_end: Calls session.snapshot() to get the final
      StructuredOutput, then delegates to the ElementRenderer's
      serialize() method for complete document serialization.
    
    This strategy is the simplest — it ignores all intermediate events and
    only acts on session_end. It exists as a distinct strategy (rather than
    a special case) to maintain the uniform strategy interface.
    """
strategy_name = "accumulate"

ElementRenderer Protocol

While the MaterializationStrategy controls when elements are rendered, the ElementRenderer controls how each element type is visually formatted. Each format has a paired ElementRenderer implementation:

class ElementRenderer(Protocol):
    """Interface for format-specific element rendering.
    
    An ElementRenderer knows how to paint each element type for a specific
    output format. It is called by the MaterializationStrategy when it is
    time to render an element.
    
    Implementations:
    - PlainElementRenderer: ASCII text, no escapes
    - ColorElementRenderer: ANSI-colored text, same layout as plain
    - TableElementRenderer: Unicode box-drawing with color
    - RichElementRenderer: Advanced terminal features (cursor, animation)
    - JsonElementRenderer: JSON serialization
    - YamlElementRenderer: YAML serialization
    """
format_name: <span style="color: cyan;">str</span>                     <span style="opacity: 0.7;"># &quot;plain&quot;, &quot;color&quot;, &quot;table&quot;, &quot;rich&quot;, &quot;json&quot;, &quot;yaml&quot;</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_panel</span>(self, panel: Panel, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a panel element to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_table</span>(self, table: Table, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a table element to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_tree</span>(self, tree: Tree, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a tree element to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_status</span>(self, status: StatusMessage, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a status message to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_progress</span>(self, progress: ProgressIndicator, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a progress indicator to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_code</span>(self, code: CodeBlock, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a code block to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_diff</span>(self, diff: DiffBlock, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a diff block to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_text</span>(self, text: TextBlock, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a text block to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_separator</span>(self, separator: Separator, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render a visual separator to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_action_hint</span>(self, hint: ActionHint, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Render an action hint to the stream.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_element</span>(self, element: OutputElement, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Dispatch to the appropriate render method based on element type.</span>

        
        This is the primary entry point used by materialization strategies.
        It uses a dispatch table to route to the correct typed method.
        """
dispatch = {
"panel": self.render_panel,
"table": self.render_table,
"tree": self.render_tree,
"status": self.render_status,
"progress": self.render_progress,
"code": self.render_code,
"diff": self.render_diff,
"text": self.render_text,
"separator": self.render_separator,
"action_hint": self.render_action_hint,
}
handler = dispatch.get(element.element_type)
if handler:
handler(element, stream)
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">serialize</span>(self, output: StructuredOutput, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Serialize a complete StructuredOutput to the stream.</span>

        
        Used by AccumulateMaterializer for json/yaml formats. For visual
        formats (plain/color/table/rich), this method iterates over
        output.elements and calls render_element for each.
        """
...
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">can_render</span>(self, terminal_caps: <span style="color: #66cc66;">&quot;TerminalCapabilities&quot;</span>) -&gt; <span style="color: cyan;">bool</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Whether this renderer can operate in the given terminal environment.&quot;&quot;&quot;</span>
    ...

Format Resolution

The active format is resolved using a precedence chain:

1. CLI flag: --format <value> on the command line (highest priority).
2. Environment variable: CLEVERAGENTS_FORMAT=<value>.
3. Config file: The format key in the global config (agents config set format <value>).
4. TTY detection: If stdout is not a TTY and no explicit format was set, fall back to plain (not rich), since non-TTY consumers cannot interpret ANSI codes or cursor movement.
5. Default: rich.

Once the format is resolved, the CLI framework selects the corresponding (MaterializationStrategy, ElementRenderer) pair from the RendererRegistry, opens an OutputSession bound to that strategy, and passes the session to the command implementation.

Format Specifications

plain — Plain Text

Philosophy: Maximum portability. Output is pure ASCII text with no escape codes, no box-drawing characters, and no color. Suitable for piping to files, logs, grep, awk, or any non-terminal consumer.

Rendering rules:

• Panels: Rendered as indented key-value pairs with a header line.
• Tables: Rendered as aligned columns separated by whitespace (no box drawing). Column headers are separated from data by a dashed line.
• Trees: Rendered with ASCII indentation using +-- and | characters.
• Status messages: Prefixed with [OK], [WARN], [ERROR], [INFO].
• Progress: Rendered as static status lines (no animation). Steps shown as [x] (done), [ ] (pending), [>] (active).
• Diffs: Standard unified diff format.
• Code blocks: Raw text with optional line numbers.
• No ANSI escape codes of any kind.
• No Unicode characters beyond basic ASCII (no box drawing, no checkmarks, no arrows).

Example (agents --format plain project show local/api-service):

$ agents --format plain project show local/api-service
Project Details
Name: local/api-service
ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1
Description: Backend API
Resources: 2
Remote: no
Created: 2026-02-08 12:46
Linked Resources
Resource          Type            Sandbox              Read-Only

local/api-repo    git-checkout    git_worktree          no
local/staging-db  local/database  transaction_rollback  yes
Validations (3)
val_01HXM5A  pytest --cov=src --cov-fail-under=80  required
val_01HXM5B  ruff check .                          required
val_01HXM5C  node scripts/check-bundle-size.js     info
Context
Include: repo
Exclude: /node_modules/
Max File Size: 1 MB
Indexing Status
Text Index: ready
Vector Index: ready
Graph Store: disabled
Indexed Files: 347
Last Indexed: 12:48
Active Plans
Plan ID   Action               Phase

01HXM7A9  local/code-coverage  execute
[OK] Project loaded

Example (agents --format plain plan list):

$ agents --format plain plan list --phase execute
Plans
ID        Phase    State       Action               Project            Elapsed

01HXM7A9  execute  processing  local/code-coverage  local/api-service  00:01:12
Filters
Phase: execute
State: (any)
Project: (any)
Action: (any)
Summary
Total: 1
Processing: 1
Completed: 0
Errored: 0
[OK] 1 plan listed

color — Colored Plain Text

Philosophy: Same structural layout as plain, but with ANSI color codes applied to improve readability. No box-drawing characters, no cursor movement, no animation.

Rendering rules:

• Identical layout to plain, but with color applied:
  • Headers/titles: Bold cyan.
  • Keys: Bold blue.
  • Values: Default color, with semantic coloring:
    • Success/positive: Green.
    • Warnings/attention: Yellow.
    • Errors/failures: Red.
    • Identifiers/names: Cyan.
    • Counts/numbers: Default (white).
  • Table headers: Bold cyan with underlines rendered as dim dashes.
  • Status prefixes: [OK] in green, [WARN] in yellow, [ERROR] in red, [INFO] in blue.
  • Diff lines: + lines green, - lines red, @@ headers cyan.
• No box-drawing characters — uses the same whitespace/dash layout as plain.
• No cursor movement or animation — pure scrolling output.
• Respects NO_COLOR environment variable: If NO_COLOR is set, color format falls back to plain.

Example (agents --format color project show local/api-service):

$ agents --format color project show local/api-service
Project Details
Name: local/api-service
ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1
Description: Backend API
Resources: 2
Remote: no
Created: 2026-02-08 12:46
Linked Resources
Resource          Type            Sandbox              Read-Only
----------------  --------------  --------------------  ---------
local/api-repo    git-checkout    git_worktree          no
local/staging-db  local/database  transaction_rollback  yes
Validations (3)
val_01HXM5A  pytest --cov=src --cov-fail-under=80  required
val_01HXM5B  ruff check .                          required
val_01HXM5C  node scripts/check-bundle-size.js     info
Context
Include: repo
Exclude: /node_modules/
Max File Size: 1 MB
Indexing Status
Text Index: ready
Vector Index: ready
Graph Store: disabled
Indexed Files: 347
Last Indexed: 12:48
Active Plans
Plan ID   Action               Phase
--------  -------------------  -------
01HXM7A9  local/code-coverage  execute
[OK] Project loaded

table — ASCII Box-Drawing Tables

Philosophy: Structured, visually distinct panels and tables using Unicode box-drawing characters (╭╮╰╯│─). Uses color. This is the style shown in the existing CLI examples throughout this document.

Rendering rules:

• Panels: Rendered as bordered boxes with title in the top border. Uses ╭─╮│╰─╯ characters for rounded corners.
• Tables: Rendered inside bordered boxes with column-aligned headers and separator lines using ─ and │.
• Trees: Rendered inside a bordered box using ├──, └──, │ tree guide characters.
• Status messages: Use Unicode indicators: ✓ (green) for OK, ⚠ (yellow) for WARN, ✗ (red) for ERROR, ℹ (blue) for INFO.
• Progress: Rendered as a step list inside a panel with ✓, ⏳, • markers.
• Color scheme: Same semantic coloring as color format, applied within box structures.
• No animation or cursor movement — the boxes are static, scrolling output.

Distinction from rich: The table format uses the same box-drawing panels and color as rich for static content, but it does not use any dynamic or interactive terminal features. There are no animated spinners, no live-updating progress bars, no cursor movement, and no in-place redraws. All output is static and scrolls sequentially. Where rich would show a spinning ⠋ and a live progress bar, table renders a static snapshot using fixed markers (✓, ⏳, •). This makes table suitable for terminals without advanced capabilities, and for output that will be reviewed after the fact (e.g., scrollback buffers).

Example (agents --format table project show local/api-service):

$ agents --format table project show local/api-service
╭─ Project Details ──────────────╮
│ Name: local/api-service        │
│ ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1 │
│ Description: Backend API       │
│ Resources: 2                   │
│ Remote: no                     │
│ Created: 2026-02-08 12:46      │
╰────────────────────────────────╯
╭─ Linked Resources ──────────────────────────────────────────────────────╮
│ Resource          Type            Sandbox              Read-Only       │
│ ────────────────  ──────────────  ────────────────────  ─────────       │
│ local/api-repo   git-checkout    git_worktree         no              │
│ local/staging-db local/database  transaction_rollback yes             │
╰─────────────────────────────────────────────────────────────────────────╯
╭─ Validations (3) ───────────────────────────────────────────╮
│ val_01HXM5A  pytest --cov=src --cov-fail-under=80  required │
│ val_01HXM5B  ruff check .                          required │
│ val_01HXM5C  node scripts/check-bundle-size.js     info     │
╰─────────────────────────────────────────────────────────────╯
╭─ Context ───────────────────╮
│ Include: repo               │
│ Exclude: /node_modules/ │
│ Max File Size: 1 MB        │
╰─────────────────────────────╯
╭─ Indexing Status ──────────╮
│ Text Index: ready         │
│ Vector Index: ready       │
│ Graph Store: disabled     │
│ Indexed Files: 347        │
│ Last Indexed: 12:48       │
╰────────────────────────────╯
╭─ Active Plans ──────────────────────────╮
│ Plan ID   Action               Phase    │
│ ────────  ───────────────────  ─────── │
│ 01HXM7A9  local/code-coverage  execute │
╰─────────────────────────────────────────╯
✓ OK Project loaded

Example (agents --format table plan execute 01HXM8C2ZK):

Unlike rich mode which would show animated spinners and a live progress bar, the table format renders a static snapshot of execution state:

$ agents --format table plan execute 01HXM8C2ZK
╭─ Execution ──────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: execute                   │
│ Sandbox: git_worktree            │
│ Worker: local/executor           │
│ Started: 12:58:10                │
│ Attempt: 1                       │
╰──────────────────────────────────╯
╭─ Strategy Summary ─────────────────────╮
│ Decisions: 8                          │
│ Invariants: 2                         │
│ Planned Child Plans: 2+               │
│ Estimated Files: ~12                  │
│ Risk: low                              │
╰────────────────────────────────────────╯
╭─ Progress ────────╮
│ ✓ Collect context │
│ ✓ Run tools       │
│ ⏳ Build changeset │
│ • Validate        │
╰───────────────────╯
✓ OK Execution started

rich — Modern Rich CLI Elements

Philosophy: The premium interactive terminal experience. Uses advanced terminal capabilities: cursor movement, inline updates, animated spinners, live-updating progress bars, collapsible sections, syntax highlighting, and dynamic layout. This is the default format.

Rendering rules:

• Panels: Rich bordered panels with rounded corners, title bars, and optional collapse/expand behavior. Panels may animate into view.
• Tables: Full-featured tables with automatic column sizing, truncation with ellipsis, sortable column indicators, alternating row shading, and horizontal scrolling for wide tables.
• Trees: Interactive collapsible trees. Nodes expand/collapse with visual animation. Color-coded by node type. Depth guides use dotted lines.
• Status messages: Use animated checkmarks/spinners that resolve to final state. Success messages may briefly flash or highlight.
• Progress: Live-updating progress bars with:
  • Animated spinners (Braille, dots, or bars depending on terminal capability).
  • Elapsed time and ETA.
  • Per-step status with animated transitions (pending → active → done).
  • Multi-line progress for parallel operations.
• Diffs: Syntax-highlighted side-by-side or unified diffs with line numbers, change highlighting at the character level (not just line level), and navigable hunks.
• Code blocks: Full syntax highlighting using terminal colors (256-color or truecolor when available). Line numbers in dim color. Highlighted lines with background color.
• Dynamic layout: Adapts to terminal width. Narrow terminals get a stacked layout; wide terminals get side-by-side panels.
• Live updates: Long-running commands (plan execute, plan status) use live-updating displays that redraw in place rather than scrolling.
• Graceful degradation: If the terminal does not support required capabilities (e.g., no truecolor, no cursor movement), the renderer automatically falls back to table rendering for those elements.

Example (agents --format rich plan execute 01HXM8C2ZK):

The rich format produces output that cannot be fully represented in static documentation — animated spinners cycle in place, progress bars fill smoothly, and elements update without scrolling. The rendering below is a static snapshot of what the terminal would display at a given moment:

$ agents --format rich plan execute 01HXM8C2ZK
╭─ Execution ──────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: execute                   │
│ Sandbox: git_worktree            │
│ Worker: local/executor           │
│ Started: 12:58:10                │
╰──────────────────────────────────╯
⠋ Collecting context...                      (⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏ animates in place)
├── repo: local/api-repo ✓
└── db: local/staging-db ✓
╭─ Strategy Summary ──────────────────────────────────────────────────────╮
│ 8 decisions │ 2 invariants │ 2+ child plans │ ~12 files │ risk: low │
╰─────────────────────────────────────────────────────────────────────────╯
Progress ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  42%  elapsed 0:01:12  ETA 0:01:40
✓ Collect context .................. 0.8s
✓ Run tools (8 calls) ............. 12.4s
⠙ Build changeset ................. (running)     (animates in place)
○ Validate ........................ (pending)
╭─ Live Tool Calls ───────────────────────────────────────╮
│ #6 read_file   src/auth/__init__.py   ✓  0.1s │
│ #7 write_file  tests/test_auth.py     ✓  0.2s │
│ #8 edit_file   src/auth/session.py    ⠙  ...  │
╰─────────────────────────────────────────────────────────╯

In rich mode:

• The Braille spinner characters (⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏) animate in real-time, cycling in place without scrolling.
• The progress bar (━━━) fills smoothly as work completes.
• Completed steps appear with a green ✓ via in-place line update (the line rewrites, it does not scroll).
• The "Live Tool Calls" panel scrolls its content internally, showing only the most recent N calls.
• The terminal is not flooded with scrolling text — elements update in place using cursor movement.

Example (agents --format rich version):

$ agents --format rich version
╭─────────────────────────────────────╮
│  CleverAgents CLI  v1.0.0           │
│  channel: stable                    │
╰─────────────────────────────────────╯
╭─ Build ────────────────────────────╮
│ Build Date: 2026-02-08              │
│ Commit: a17c3f9                    │
│ Schema: v3                          │
│ Platform: linux-x86_64              │
│ Python: 3.13.1                      │
╰────────────────────────────────────╯
╭─ Dependencies ─────────────────────────────────────────────────────────╮
│ LangGraph 0.2.60 │ LangChain 0.3.18 │ MCP SDK 1.4.0 │ Pydantic 2.10.4 │
╰────────────────────────────────────────────────────────────────────────╯
✓ OK Version reported

In rich mode, the version card may use background colors, bold gradients, or subtle box shadows (depending on terminal truecolor support). Elements may animate into view with a brief slide or fade transition.

json — JSON Data Structure

Philosophy: Machine-readable output for programmatic consumption. Every command produces a well-defined JSON object. No ANSI color codes in the structural data. Color codes may appear only within text values that represent verbatim content (such as code blocks or diff output where the original text contained ANSI sequences), but all structural keys, labels, and metadata are plain strings.

Rendering rules:

• Top-level structure: Always a JSON object with a standard envelope:

  
  {
    "command": "project show",
    "status": "ok",
    "exit_code": 0,
    "data": { ... },
    "timing": { "duration_ms": 42 },
    "metadata": { ... }
  }
  

• Panels: Rendered as nested objects within data.
• Tables: Rendered as arrays of objects within data.
• Trees: Rendered as nested objects with children arrays.
• Status messages: Included in a messages array within the envelope.
• Progress: Not rendered (JSON output is non-interactive; progress is omitted).
• Diffs: Rendered as structured objects with hunks arrays.
• No ANSI codes in any structural element. Raw ANSI codes are preserved only in string values that represent verbatim terminal output.
• Pretty-printed by default (indented). Compact mode available via --json-compact (future option).
• Consistent schema per command: Each command's JSON schema is stable and documented, enabling reliable programmatic parsing.

Example (agents --format json project show local/api-service):

{
  "command": "project show",
  "status": "ok",
  "exit_code": 0,
  "data": {
    "project": {
      "name": "local/api-service",
      "id": "01HXM4T08Y0N5R9VZ4QX4BPTZ1",
      "description": "Backend API",
      "type": "local",
      "remote": false,
      "created_at": "2026-02-08T12:46:00Z"
    },
    "linked_resources": [
      {
        "name": "local/api-repo",
        "type": "git-checkout",
        "sandbox_strategy": "git_worktree",
        "read_only": false
      },
      {
        "name": "local/staging-db",
        "type": "local/database",
        "sandbox_strategy": "transaction_rollback",
        "read_only": true
      }
    ],
    "validations": [
      {
        "id": "val_01HXM5A",
        "command": "pytest --cov=src --cov-fail-under=80",
        "mode": "required",
        "timeout": 600,
        "resource": "repo"
      },
      {
        "id": "val_01HXM5B",
        "command": "ruff check .",
        "mode": "required",
        "timeout": 300,
        "resource": null
      },
      {
        "id": "val_01HXM5C",
        "command": "node scripts/check-bundle-size.js",
        "mode": "informational",
        "timeout": 300,
        "resource": null
      }
    ],
    "context": {
      "include_resources": ["repo"],
      "exclude_paths": ["**/node_modules/**"],
      "max_file_size_bytes": 1048576
    },
    "indexing": {
      "text_index": "ready",
      "vector_index": "ready",
      "graph_store": "disabled",
      "indexed_files": 347,
      "last_indexed_at": "2026-02-08T12:48:00Z"
    },
    "active_plans": [
      {
        "plan_id": "01HXM7A9",
        "action": "local/code-coverage",
        "phase": "execute"
      }
    ]
  },
  "timing": {
    "duration_ms": 42
  },
  "messages": [
    { "level": "ok", "text": "Project loaded" }
  ]
}

Example (agents --format json plan list --phase execute):

{
  "command": "plan list",
  "status": "ok",
  "exit_code": 0,
  "data": {
    "plans": [
      {
        "id": "01HXM7A9",
        "phase": "execute",
        "state": "processing",
        "action": "local/code-coverage",
        "project": "local/api-service",
        "elapsed": "00:01:12"
      }
    ],
    "filters": {
      "phase": "execute",
      "state": null,
      "project": null,
      "action": null
    },
    "summary": {
      "total": 1,
      "processing": 1,
      "completed": 0,
      "errored": 0
    }
  },
  "timing": {
    "duration_ms": 18
  },
  "messages": [
    { "level": "ok", "text": "1 plan listed" }
  ]
}

yaml — YAML Data Structure

Philosophy: Same data as json but in YAML format. Preferred by users who find YAML more readable for configuration and scripting workflows. Follows the same structural conventions as json.

Rendering rules:

• Same data envelope as JSON (command, status, exit_code, data, timing, messages).
• YAML 1.2 compliant output.
• Multi-line strings use YAML block scalars (| for literal, > for folded) when appropriate.
• No ANSI codes in structural elements (same rule as JSON).
• Sorted keys for deterministic output.

Example (agents --format yaml project show local/api-service):

command: project show
status: ok
exit_code: 0
data:
  project:
    name: local/api-service
    id: 01HXM4T08Y0N5R9VZ4QX4BPTZ1
    description: Backend API
    type: local
    remote: false
    created_at: "2026-02-08T12:46:00Z"
  linked_resources:
    - name: local/api-repo
      type: git-checkout
      sandbox_strategy: git_worktree
      read_only: false
    - name: local/staging-db
      type: local/database
      sandbox_strategy: transaction_rollback
      read_only: true
  validations:
    - id: val_01HXM5A
      command: "pytest --cov=src --cov-fail-under=80"
      mode: required
      timeout: 600
      resource: repo
    - id: val_01HXM5B
      command: "ruff check ."
      mode: required
      timeout: 300
      resource: null
    - id: val_01HXM5C
      command: "node scripts/check-bundle-size.js"
      mode: informational
      timeout: 300
      resource: null
  context:
    include_resources:
      - repo
    exclude_paths:
      - "**/node_modules/**"
    max_file_size_bytes: 1048576
  indexing:
    text_index: ready
    vector_index: ready
    graph_store: disabled
    indexed_files: 347
    last_indexed_at: "2026-02-08T12:48:00Z"
  active_plans:
    - plan_id: 01HXM7A9
      action: local/code-coverage
      phase: execute
timing:
  duration_ms: 42
messages:
  - level: ok
    text: Project loaded

Format Comparison Matrix

Capability	plain	color	table	rich	json	yaml
Color codes	No	Yes	Yes	Yes	No	No
Box drawing	No	No	Yes	Yes	No	No
Animation/spinners	No	No	No	Yes	No	No
Live updates	No	No	No	Yes	No	No
Cursor movement	No	No	No	Yes	No	No
Syntax highlighting	No	No	No	Yes	No	No
Collapsible sections	No	No	No	Yes	No	No
Machine-parseable	Partially	Partially	No	No	Yes	Yes
Pipe-safe	Yes	No*	No	No	Yes	Yes
Unicode required	No	No	Yes	Yes	No	No
TTY required	No	No	No	Yes**	No	No

* color output can be piped if the consumer understands ANSI codes (e.g., less -R). ** rich gracefully degrades to table when stdout is not a TTY.

Renderer Registration and Extension

The framework uses a registry pattern for format renderers, enabling third-party or plugin renderers. Each format is registered as a (MaterializationStrategy, ElementRenderer) pair — the strategy controls timing/ordering, and the renderer controls visual formatting:

@dataclass
class FormatRegistration:
    """A registered format: its strategy factory, renderer factory, and fallback."""
    strategy_factory: Callable[[TerminalCapabilities], MaterializationStrategy]
    renderer_factory: Callable[[TerminalCapabilities], ElementRenderer]
    fallback: str | None                 # Format name to fall back to, or None
class RendererRegistry:
"""Central registry for format (strategy, renderer) pairs.
    
    Formats are registered by name and resolved at runtime based on the
    active format and terminal capabilities. The registry supports dynamic
    registration, enabling plugins to add custom formats (e.g., 'html',
    'csv', 'markdown').
    
    Built-in registrations:
        "rich"  → (LiveMaterializer, RichElementRenderer), fallback="table"
        "table" → (SequentialBufferMaterializer, TableElementRenderer), fallback="color"
        "color" → (SequentialBufferMaterializer, ColorElementRenderer), fallback="plain"
        "plain" → (SequentialBufferMaterializer, PlainElementRenderer), fallback=None
        "json"  → (AccumulateMaterializer, JsonElementRenderer), fallback=None
        "yaml"  → (AccumulateMaterializer, YamlElementRenderer), fallback=None
    """
_formats: <span style="color: cyan;">dict</span>[<span style="color: cyan;">str</span>, FormatRegistration] = {}

<span style="color: yellow;">@classmethod</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">register</span>(cls, format_name: <span style="color: cyan;">str</span>,
             strategy_factory: <span style="color: cyan;">Callable</span>[[TerminalCapabilities], MaterializationStrategy],
             renderer_factory: <span style="color: cyan;">Callable</span>[[TerminalCapabilities], ElementRenderer],
             fallback: <span style="color: cyan;">str</span> | <span style="color: magenta; font-weight: 600;">None</span> = <span style="color: magenta; font-weight: 600;">None</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Register a format.</span>

        
        Args:
            format_name: The format identifier (e.g., 'rich', 'json').
            strategy_factory: Callable that creates a MaterializationStrategy,
                given terminal capabilities.
            renderer_factory: Callable that creates an ElementRenderer,
                given terminal capabilities.
            fallback: Optional fallback format name if this format cannot
                operate in the current terminal environment.
        """
cls._formats[format_name] = FormatRegistration(
strategy_factory=strategy_factory,
renderer_factory=renderer_factory,
fallback=fallback,
)
<span style="color: yellow;">@classmethod</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">resolve</span>(cls, format_name: <span style="color: cyan;">str</span>,
            terminal_caps: TerminalCapabilities
            ) -&gt; <span style="color: cyan;">tuple</span>[MaterializationStrategy, ElementRenderer]:
    <span style="color: #66cc66;">&quot;&quot;&quot;Resolve the best (strategy, renderer) pair for the given format.</span>

        
        Walks the fallback chain if the requested format's renderer cannot
        operate in the current terminal environment. Returns the first pair
        where the renderer reports can_render(terminal_caps) == True.
        
        Raises:
            ValueError: If no usable format is found (should never happen
                since 'plain' has no fallback and always works).
        """
current = format_name
visited: set[str] = set()
    <span style="color: magenta; font-weight: 600;">while</span> current <span style="color: magenta; font-weight: 600;">and</span> current <span style="color: magenta; font-weight: 600;">not</span> <span style="color: magenta; font-weight: 600;">in</span> visited:
        visited.add(current)
        registration = cls._formats.get(current)
        <span style="color: magenta; font-weight: 600;">if</span> registration <span style="color: magenta; font-weight: 600;">is</span> <span style="color: magenta; font-weight: 600;">None</span>:
            <span style="color: magenta; font-weight: 600;">break</span>
        
        renderer = registration.renderer_factory(terminal_caps)
        <span style="color: magenta; font-weight: 600;">if</span> renderer.can_render(terminal_caps):
            strategy = registration.strategy_factory(terminal_caps)
            strategy.bind(renderer, terminal_caps=terminal_caps)
            <span style="color: magenta; font-weight: 600;">return</span> strategy, renderer
        
        current = registration.fallback
    
    <span style="opacity: 0.7;"># Ultimate fallback is always plain</span>
    plain = cls._formats[<span style="color: #66cc66;">&quot;plain&quot;</span>]
    renderer = plain.renderer_factory(terminal_caps)
    strategy = plain.strategy_factory(terminal_caps)
    strategy.bind(renderer, terminal_caps=terminal_caps)
    <span style="color: magenta; font-weight: 600;">return</span> strategy, renderer

<span style="color: yellow;">@classmethod</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">available_formats</span>(cls) -&gt; <span style="color: cyan;">list</span>[<span style="color: cyan;">str</span>]:
    <span style="color: #66cc66;">&quot;&quot;&quot;Return all registered format names.&quot;&quot;&quot;</span>
    <span style="color: magenta; font-weight: 600;">return</span> sorted(cls._formats.keys())

<span style="color: yellow;">@classmethod</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">is_registered</span>(cls, format_name: <span style="color: cyan;">str</span>) -&gt; <span style="color: cyan;">bool</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Check if a format is registered.&quot;&quot;&quot;</span>
    <span style="color: magenta; font-weight: 600;">return</span> format_name <span style="color: magenta; font-weight: 600;">in</span> cls._formats

Terminal Capability Detection

The framework detects terminal capabilities to guide format resolution, strategy selection, and renderer fallback:

@dataclass
class TerminalCapabilities:
    """Detected capabilities of the output terminal.
    
    This dataclass is populated once at CLI startup and passed to the
    RendererRegistry for format resolution. It is also available to
    individual strategies and renderers for fine-grained adaptation
    (e.g., adjusting column widths to terminal width, choosing between
    256-color and truecolor palettes).
    """
    is_tty: bool                         # Is stdout a TTY?
    width: int                           # Terminal width in columns
    height: int                          # Terminal height in rows
    supports_ansi: bool                  # Supports basic ANSI escape codes?
    supports_256_color: bool             # Supports 256-color palette?
    supports_truecolor: bool             # Supports 24-bit truecolor?
    supports_unicode: bool               # Supports Unicode (box-drawing, etc.)?
    supports_cursor_movement: bool       # Supports cursor repositioning?
    supports_alternate_screen: bool      # Supports alternate screen buffer?
    no_color: bool                       # Is NO_COLOR environment variable set?
    term_program: str | None             # TERM_PROGRAM value (e.g., "iTerm2", "vscode")
<span style="color: yellow;">@classmethod</span>
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">detect</span>(cls) -&gt; <span style="color: #66cc66;">&quot;TerminalCapabilities&quot;</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Auto-detect terminal capabilities from the environment.</span>

        
        Detection logic:
        - is_tty: os.isatty(sys.stdout.fileno())
        - width/height: os.get_terminal_size() with fallback to (80, 24)
        - supports_ansi: True if is_tty and not Windows legacy console
        - supports_256_color: True if TERM contains "256color" or COLORTERM is set
        - supports_truecolor: True if COLORTERM is "truecolor" or "24bit"
        - supports_unicode: True if locale encoding is UTF-8
        - supports_cursor_movement: True if is_tty and TERM is not "dumb"
        - supports_alternate_screen: True if supports_cursor_movement
        - no_color: True if NO_COLOR environment variable is set (any value)
        - term_program: Value of TERM_PROGRAM environment variable
        """
...

Plugin Format Registration

Third-party plugins can register custom formats using the registry:

# Example: Registering a custom 'csv' format plugin
from cleveragents.output import RendererRegistry, SequentialBufferMaterializer
class CsvElementRenderer(ElementRenderer):
"""Renders tables as CSV, other elements as plain text."""
format_name = "csv"
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">render_table</span>(self, table: Table, stream: <span style="color: cyan;">IO</span>) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    writer = csv.writer(stream)
    writer.writerow([col.name <span style="color: magenta; font-weight: 600;">for</span> col <span style="color: magenta; font-weight: 600;">in</span> table.columns])
    <span style="color: magenta; font-weight: 600;">for</span> row <span style="color: magenta; font-weight: 600;">in</span> table.rows:
        writer.writerow([row.get(col.name, <span style="color: #66cc66;">&quot;&quot;</span>) <span style="color: magenta; font-weight: 600;">for</span> col <span style="color: magenta; font-weight: 600;">in</span> table.columns])

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">can_render</span>(self, terminal_caps: TerminalCapabilities) -&gt; <span style="color: cyan;">bool</span>:
    <span style="color: magenta; font-weight: 600;">return</span> <span style="color: magenta; font-weight: 600;">True</span>  <span style="opacity: 0.7;"># CSV works everywhere</span>

# Register at plugin load time
RendererRegistry.register(
format_name="csv",
strategy_factory=lambda caps: SequentialBufferMaterializer(),
renderer_factory=lambda caps: CsvElementRenderer(),
fallback="plain",
)

Edge Cases and Special Behaviors

Error Output

Errors are rendered through the same framework. When a command raises an exception or signals an error, the session's context manager (__exit__) catches the exception, creates a StatusMessage with level="error" and an optional TextBlock with details, and closes the session with exit_code=1. In json/yaml formats, errors produce:

{
  "command": "project show",
  "status": "error",
  "exit_code": 1,
  "error": {
    "code": "NOT_FOUND",
    "message": "Project 'local/nonexistent' not found",
    "detail": "No project with name 'local/nonexistent' exists. Run 'agents project list' to see available projects.",
    "suggestions": [
      "agents project list",
      "agents project create local/nonexistent"
    ]
  },
  "timing": { "duration_ms": 5 }
}

Empty Results

When a list command returns no results, all formats handle it gracefully:

• plain/color: Prints a message like No projects found.
• table: Renders an empty table with headers and a (empty) message.
• rich: Renders a dimmed panel with an empty-state message and suggested actions.
• json/yaml: Returns an empty array in the appropriate data field.

Large Data Sets

For commands that may return large result sets (e.g., resource list with thousands of resources):

• plain/color/table (SequentialBufferMaterializer): The table handle accumulates rows as the producer adds them. Since the buffer is in-memory, very large result sets benefit from the max_rows_hint — the renderer truncates display at N rows with a (... and N more rows) indicator. The full data is still available in the snapshot for json/yaml.
• rich (LiveMaterializer): Uses a virtual-scrolling table that renders only visible rows. Shows a count indicator (e.g., "Showing 1-50 of 2,847"). New rows animate into view as they are added.
• json/yaml (AccumulateMaterializer): Accumulates and emits a complete array at session end. Streaming JSON lines (one JSON object per row) for very large sets is a future consideration.

Nested/Recursive Structures

Tree-like data (resource trees, decision trees, plan hierarchies) may be arbitrarily deep. Renderers respect max_depth_hint:

• plain/color: Truncate at depth N with a ... (N more levels) indicator.
• table: Same truncation, rendered inside a box.
• rich: Collapsible tree nodes — deep levels start collapsed. User can expand interactively if the terminal supports it.
• json/yaml: Full depth — no truncation (programmatic consumers need complete data).

Mixed Content

Some commands produce mixed output (e.g., plan status has panels, tables, progress bars, and status messages). Each element is created via its own handle on the session, and the materialization strategy renders them in declaration order. The ElementRenderer is responsible for visual spacing and grouping between heterogeneous elements (e.g., inserting blank lines between a panel and a table in plain format, or adding visual margins in rich format).

Producer Error Mid-Stream

When a producer encounters an error while writing to a handle (e.g., an API call fails while populating a table), the framework handles it as follows:

1. The handle is closed with partial data — the producer catches its exception, optionally calls handle.close() (or lets the context manager close it), and then creates a StatusMessage handle with level="error" to report the failure.

2. The materialization strategy renders whatever was accumulated — a table with 3 of an expected 10 rows is rendered with those 3 rows, followed by the error message. This is better than rendering nothing.

3. The session's exit code is set to 1 — indicating partial failure.

4. For json/yaml formats, the accumulated snapshot includes both the partial data and the error message in the messages array, giving programmatic consumers full visibility.

Example of producer error handling:

async def list_resources(session: OutputSession, client: ApiClient) -> None:
    table = session.table("Resources", columns=[
        ColumnDef(name="Name"), ColumnDef(name="Type"), ColumnDef(name="Status"),
    ])
<span style="color: magenta; font-weight: 600;">try</span>:
    <span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">for</span> resource <span style="color: magenta; font-weight: 600;">in</span> client.list_resources():
        table.add_row({
            <span style="color: #66cc66;">&quot;Name&quot;</span>: resource.name,
            <span style="color: #66cc66;">&quot;Type&quot;</span>: resource.type,
            <span style="color: #66cc66;">&quot;Status&quot;</span>: resource.status,
        })
<span style="color: magenta; font-weight: 600;">except</span> ApiError <span style="color: magenta; font-weight: 600;">as</span> e:
    table.close()  <span style="opacity: 0.7;"># Close with partial data</span>
    session.status(<span style="color: #66cc66;">f&quot;Error fetching resources: {e}&quot;</span>, level=<span style="color: #66cc66;">&quot;error&quot;</span>)
    <span style="color: magenta; font-weight: 600;">return</span>

table.close()
session.status(<span style="color: #66cc66;">f&quot;{table.element.row_count} resources listed&quot;</span>, level=<span style="color: #66cc66;">&quot;ok&quot;</span>)

Abandoned Handles

If a handle is never explicitly closed and the session ends (either normally via session.close() or via the context manager's __exit__), the session force-closes all remaining open handles:

1. A warning is logged (not rendered to the user): "Handle {handle_id} ({element_type}) was not explicitly closed; force-closing at session end."
2. The handle is closed with its current accumulated state.
3. The materialization strategy processes the ElementClosed event normally.

This ensures that no data is silently lost, even if producer code forgets to close a handle due to an unhandled code path.

Back-Pressure and Throttling

The LiveMaterializer (used by rich format) limits terminal redraws to its configured frame rate (default 15 fps). When handles emit updates faster than the frame rate:

1. Updates are coalesced — the materializer tracks a dirty set of handle IDs that have been updated since the last frame.
2. On each frame tick, all dirty elements are redrawn in a single pass, and the dirty set is cleared.
3. The event queue between session and strategy uses bounded capacity. If the queue fills (producer is vastly faster than rendering), the session's _emit_event method drops ElementUpdated events for handles that are already in the dirty set (since the next frame will redraw them anyway). ElementCreated and ElementClosed events are never dropped.

For SequentialBufferMaterializer and AccumulateMaterializer, there is no back-pressure concern — updates are buffered in memory and never rendered incrementally.

Cancellation Semantics

When a command is cancelled (e.g., user presses Ctrl+C):

1. The session receives a cancellation signal and enters the "closing" state.
2. All open handles are force-closed with their current state.
3. A StatusMessage with level="warn" and message "Operation cancelled" is emitted.
4. The session closes with exit_code=130 (standard SIGINT exit code).
5. For rich format: The LiveMaterializer freezes the display, resolves any active spinners to a cancellation indicator (e.g., yellow ⚠), and moves the cursor to the end of the output.
6. For buffered formats: Any elements that have been rendered stay on screen. Pending buffered elements are flushed in order, followed by the cancellation message.
7. For json/yaml: The accumulated snapshot is serialized with "status": "cancelled" and the appropriate exit code.

Interleaved Status Messages During Concurrent Production

When multiple producers are running concurrently (e.g., populating two tables), status messages may be created at any time by any producer. The materialization strategy handles these based on format:

• rich (LiveMaterializer): Status messages are rendered immediately in a dedicated status region at the bottom of the display (below all element regions). Multiple concurrent status messages stack vertically.
• plain/color/table (SequentialBufferMaterializer): Status messages created during concurrent production are treated as elements in declaration order, just like tables and panels. A status message created between two table creations will be rendered between those tables. Status messages created after all tables will render after all tables are flushed.
• json/yaml (AccumulateMaterializer): All status messages are collected in the messages array of the final snapshot, ordered by timestamp.

Integration with Future TUI

The reactive OutputSession architecture is intentionally designed to serve as the data layer for a future TUI (text user interface). The session's event-driven model maps directly to TUI widget patterns:

1. Element handles become observable data sources. A TUI MaterializationStrategy (e.g., TuiMaterializer) would subscribe to element events and route them to TUI widgets. The producer code (command logic) is completely unaware of whether it is driving a CLI, TUI, or web frontend — it writes to handles identically in all cases.

2. Element types map to TUI widgets:

   • PanelHandle → info pane or detail card widget
   • TableHandle → sortable, filterable data grid widget (rows arrive incrementally via add_row events)
   • TreeHandle → collapsible tree view widget (nodes arrive incrementally via add_child events)
   • ProgressHandle → animated progress bar or step-list widget
   • StatusHandle → toast notification or status bar message
   • CodeHandle → syntax-highlighted code viewer widget
   • DiffHandle → side-by-side diff viewer widget

3. Interactive features are additive. The TUI can offer features that the CLI cannot — sorting table columns, filtering rows, collapsing/expanding tree nodes, searching within code blocks — without any changes to producer code. These features are implemented in the TUI's ElementRenderer and widget layer.

4. Concurrent updates are native. Because the session already supports multiple concurrent producers writing to different handles, the TUI naturally displays multiple simultaneously-updating widgets (e.g., two tables being populated in parallel by concurrent operations). The TuiMaterializer routes events to widgets, and each widget redraws independently using the TUI framework's event loop.

5. The StructuredOutput snapshot provides the initial state when navigating to a completed session in the TUI (e.g., reviewing a past command's output), while live sessions use the event stream.

The separation between production (element handles), timing (materialization strategy), and presentation (element renderer) ensures that the same command logic supports CLI, TUI, and web frontends without modification — only the (MaterializationStrategy, ElementRenderer) pair changes.

Programmatic Usage Examples

This section demonstrates how command implementations use the Output Rendering Framework through the OutputSession API, and how the same producer code produces correct output across all formats.

Example 1: Simple Static Command Output

The simplest usage — a command that creates elements, populates them synchronously, and closes them. No concurrency, no streaming.

Producer code (agents project show):

async def cmd_project_show(session: OutputSession, project_name: str) -> None:
    """Implementation of 'agents project show <project>'."""
    project = await api.get_project(project_name)
    resources = await api.list_project_resources(project.id)
    validations = await api.list_project_validations(project.id)
<span style="opacity: 0.7;"># --- Build output elements ---</span>

<span style="opacity: 0.7;"># Panel: Project details</span>
<span style="color: magenta; font-weight: 600;">with</span> session.panel(<span style="color: #66cc66;">&quot;Project Details&quot;</span>) <span style="color: magenta; font-weight: 600;">as</span> panel:
    panel.set_entries({
        <span style="color: #66cc66;">&quot;Name&quot;</span>: project.name,
        <span style="color: #66cc66;">&quot;ID&quot;</span>: project.id,
        <span style="color: #66cc66;">&quot;Description&quot;</span>: project.description,
        <span style="color: #66cc66;">&quot;Resources&quot;</span>: <span style="color: cyan;">str</span>(len(resources)),
        <span style="color: #66cc66;">&quot;Remote&quot;</span>: <span style="color: #66cc66;">&quot;yes&quot;</span> <span style="color: magenta; font-weight: 600;">if</span> project.remote <span style="color: magenta; font-weight: 600;">else</span> <span style="color: #66cc66;">&quot;no&quot;</span>,
        <span style="color: #66cc66;">&quot;Created&quot;</span>: project.created_at.strftime(<span style="color: #66cc66;">&quot;%Y-%m-%d %H:%M&quot;</span>),
    }, style_hints={
        <span style="color: #66cc66;">&quot;Name&quot;</span>: <span style="color: #66cc66;">&quot;identifier&quot;</span>,
        <span style="color: #66cc66;">&quot;Resources&quot;</span>: <span style="color: #66cc66;">&quot;number&quot;</span>,
        <span style="color: #66cc66;">&quot;Remote&quot;</span>: <span style="color: #66cc66;">&quot;success&quot;</span> <span style="color: magenta; font-weight: 600;">if</span> <span style="color: magenta; font-weight: 600;">not</span> project.remote <span style="color: magenta; font-weight: 600;">else</span> <span style="color: #66cc66;">&quot;info&quot;</span>,
        <span style="color: #66cc66;">&quot;Created&quot;</span>: <span style="color: #66cc66;">&quot;success&quot;</span>,
    })

<span style="opacity: 0.7;"># Table: Linked resources</span>
<span style="color: magenta; font-weight: 600;">with</span> session.table(<span style="color: #66cc66;">&quot;Linked Resources&quot;</span>, columns=[
    ColumnDef(name=<span style="color: #66cc66;">&quot;Resource&quot;</span>, type=<span style="color: #66cc66;">&quot;string&quot;</span>, style_hint=<span style="color: #66cc66;">&quot;identifier&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Type&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Sandbox&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Read-Only&quot;</span>),
]) <span style="color: magenta; font-weight: 600;">as</span> table:
    <span style="color: magenta; font-weight: 600;">for</span> r <span style="color: magenta; font-weight: 600;">in</span> resources:
        table.add_row({
            <span style="color: #66cc66;">&quot;Resource&quot;</span>: r.name,
            <span style="color: #66cc66;">&quot;Type&quot;</span>: r.type,
            <span style="color: #66cc66;">&quot;Sandbox&quot;</span>: r.sandbox_strategy,
            <span style="color: #66cc66;">&quot;Read-Only&quot;</span>: <span style="color: #66cc66;">&quot;yes&quot;</span> <span style="color: magenta; font-weight: 600;">if</span> r.read_only <span style="color: magenta; font-weight: 600;">else</span> <span style="color: #66cc66;">&quot;no&quot;</span>,
        })

<span style="opacity: 0.7;"># Table: Validations</span>
<span style="color: magenta; font-weight: 600;">with</span> session.table(<span style="color: #66cc66;">f&quot;Validations ({len(validations)})&quot;</span>, columns=[
    ColumnDef(name=<span style="color: #66cc66;">&quot;ID&quot;</span>, type=<span style="color: #66cc66;">&quot;id&quot;</span>, style_hint=<span style="color: #66cc66;">&quot;identifier&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Command&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Mode&quot;</span>),
]) <span style="color: magenta; font-weight: 600;">as</span> table:
    <span style="color: magenta; font-weight: 600;">for</span> v <span style="color: magenta; font-weight: 600;">in</span> validations:
        table.add_row({
            <span style="color: #66cc66;">&quot;ID&quot;</span>: v.id,
            <span style="color: #66cc66;">&quot;Command&quot;</span>: v.command,
            <span style="color: #66cc66;">&quot;Mode&quot;</span>: v.mode,
        })

<span style="opacity: 0.7;"># Status: Final message</span>
session.status(<span style="color: #66cc66;">&quot;Project loaded&quot;</span>, level=<span style="color: #66cc66;">&quot;ok&quot;</span>)

What this produces in plain format:

Project Details
  Name: local/api-service
  ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1
  Description: Backend API
  Resources: 2
  Remote: no
  Created: 2026-02-08 12:46
Linked Resources
Resource          Type            Sandbox              Read-Only

local/api-repo    git-checkout    git_worktree          no
local/staging-db  local/database  transaction_rollback  yes
Validations (3)
ID           Command                                Mode

val_01HXM5A  pytest --cov=src --cov-fail-under=80   required
val_01HXM5B  ruff check .                           required
val_01HXM5C  node scripts/check-bundle-size.js      informational
[OK] Project loaded

What this produces in rich format:

╭─ Project Details ──────────────╮
│ Name: local/api-service        │
│ ID: 01HXM4T08Y0N5R9VZ4QX4BPTZ1 │
│ Description: Backend API       │
│ Resources: 2                   │
│ Remote: no                     │
│ Created: 2026-02-08 12:46      │
╰────────────────────────────────╯
╭─ Linked Resources ──────────────────────────────────────────────────────╮
│ Resource          Type            Sandbox              Read-Only       │
│ ────────────────  ──────────────  ────────────────────  ─────────       │
│ local/api-repo   git-checkout    git_worktree         no              │
│ local/staging-db local/database  transaction_rollback yes             │
╰─────────────────────────────────────────────────────────────────────────╯
╭─ Validations (3) ───────────────────────────────────────────────────╮
│ ID           Command                                Mode           │
│ ───────────  ─────────────────────────────────────  ───────────  │
│ val_01HXM5A  pytest --cov=src --cov-fail-under=80   required     │
│ val_01HXM5B  ruff check .                           required     │
│ val_01HXM5C  node scripts/check-bundle-size.js      informational│
╰─────────────────────────────────────────────────────────────────────╯
✓ OK Project loaded

What this produces in json format:

{
  "command": "project show",
  "status": "ok",
  "exit_code": 0,
  "data": {
    "project_details": {
      "Name": "local/api-service",
      "ID": "01HXM4T08Y0N5R9VZ4QX4BPTZ1",
      "Description": "Backend API",
      "Resources": "2",
      "Remote": "no",
      "Created": "2026-02-08 12:46"
    },
    "linked_resources": [
      {
        "Resource": "local/api-repo",
        "Type": "git-checkout",
        "Sandbox": "git_worktree",
        "Read-Only": "no"
      },
      {
        "Resource": "local/staging-db",
        "Type": "local/database",
        "Sandbox": "transaction_rollback",
        "Read-Only": "yes"
      }
    ],
    "validations": [
      {
        "ID": "val_01HXM5A",
        "Command": "pytest --cov=src --cov-fail-under=80",
        "Mode": "required"
      },
      {
        "ID": "val_01HXM5B",
        "Command": "ruff check .",
        "Mode": "required"
      },
      {
        "ID": "val_01HXM5C",
        "Command": "node scripts/check-bundle-size.js",
        "Mode": "informational"
      }
    ]
  },
  "timing": { "duration_ms": 42 },
  "messages": [
    { "level": "ok", "text": "Project loaded" }
  ]
}

In all three formats, the producer code is identical. The OutputSession and its materialization strategy handle the differences transparently.

Example 2: Streaming Rows into a Table

A command that streams rows into a table as results arrive from a paginated API. The table handle stays open while the producer fetches pages.

Producer code (agents resource list):

async def cmd_resource_list(session: OutputSession, project: str | None) -> None:
    """Implementation of 'agents resource list'."""
<span style="opacity: 0.7;"># Create the table handle — it will accumulate rows as we stream them</span>
table = session.table(<span style="color: #66cc66;">&quot;Resources&quot;</span>, columns=[
    ColumnDef(name=<span style="color: #66cc66;">&quot;Name&quot;</span>, type=<span style="color: #66cc66;">&quot;string&quot;</span>, style_hint=<span style="color: #66cc66;">&quot;identifier&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Type&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Project&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Sandbox&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Status&quot;</span>),
], sort_key=<span style="color: #66cc66;">&quot;Name&quot;</span>)

<span style="opacity: 0.7;"># Create a progress indicator for the fetch operation</span>
progress = session.progress(<span style="color: #66cc66;">&quot;Fetching resources...&quot;</span>, indeterminate=<span style="color: magenta; font-weight: 600;">True</span>)

<span style="opacity: 0.7;"># Stream pages from the API</span>
count = 0
<span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">for</span> page <span style="color: magenta; font-weight: 600;">in</span> api.list_resources_paginated(project=project):
    <span style="color: magenta; font-weight: 600;">for</span> resource <span style="color: magenta; font-weight: 600;">in</span> page.items:
        table.add_row({
            <span style="color: #66cc66;">&quot;Name&quot;</span>: resource.name,
            <span style="color: #66cc66;">&quot;Type&quot;</span>: resource.type,
            <span style="color: #66cc66;">&quot;Project&quot;</span>: resource.project,
            <span style="color: #66cc66;">&quot;Sandbox&quot;</span>: resource.sandbox_strategy,
            <span style="color: #66cc66;">&quot;Status&quot;</span>: resource.status,
        })
        count += 1
    
    <span style="opacity: 0.7;"># Update progress label with count so far</span>
    progress.set_label(<span style="color: #66cc66;">f&quot;Fetching resources... ({count} found)&quot;</span>)

<span style="opacity: 0.7;"># Close the progress indicator (it has served its purpose)</span>
progress.close()

<span style="opacity: 0.7;"># Set summary and close the table</span>
table.set_summary({<span style="color: #66cc66;">&quot;total&quot;</span>: count})
table.close()

<span style="opacity: 0.7;"># Final status</span>
session.status(<span style="color: #66cc66;">f&quot;{count} resources listed&quot;</span>, level=<span style="color: #66cc66;">&quot;ok&quot;</span>)

What this looks like in plain format (SequentialBufferMaterializer):

The progress indicator is rendered as a static line. The table is buffered until table.close() is called, then rendered in full. The user sees nothing until the fetch is complete — then the entire result appears at once:

Fetching resources... (47 found) [done]
Resources
Name                  Type            Project            Sandbox              Status

local/api-repo        git-checkout    local/api-service  git_worktree          active
local/staging-db      local/database  local/api-service  transaction_rollback  active
local/docs-repo       git-checkout    local/docs-site    git_worktree          active
... (44 more rows)
Total: 47
[OK] 47 resources listed

What this looks like in rich format (LiveMaterializer):

The progress spinner animates in real-time. The table updates in-place as rows arrive — each new row appears at the bottom of the table, the row count updates, and the terminal display is rewritten without scrolling. This is a static snapshot of the live display mid-fetch:

⠙ Fetching resources... (23 found)
╭─ Resources ─────────────────────────────────────────────────────────────────╮
│ Name                  Type            Project            Sandbox              Status  │
│ ────────────────────  ──────────────  ─────────────────  ────────────────────  ──────  │
│ local/api-repo        git-checkout    local/api-service  git_worktree          active  │
│ local/staging-db      local/database  local/api-service  transaction_rollback  active  │
│ local/docs-repo       git-checkout    local/docs-site    git_worktree          active  │
│   ...                                                                               │
│ local/test-fixtures   git-checkout    local/api-service  git_worktree          active  │
│                                                                                       │
│ Showing 1-23 of 23 (fetching...)                                                    │
╰───────────────────────────────────────────────────────────────────────────────────────╯

In rich mode, the spinner animates, the table grows as rows arrive, and the row count updates — all in-place without scrolling. When the fetch completes, the spinner resolves to ✓, and the table shows its final state.

Example 3: Concurrent Parallel Operations (Two Tables Simultaneously)

This is the key motivating example for the reactive architecture. Two tables are populated simultaneously by parallel workers, and the producer code is completely format-agnostic.

Producer code (agents plan status — showing resources and active tool calls concurrently):

async def cmd_plan_status(session: OutputSession, plan_id: str) -> None:
    """Implementation of 'agents plan status <plan_id>'.
    
    This command fetches plan metadata, then concurrently streams two data
    sources: resource statuses and active tool call logs. Both data sources
    are long-running — they produce results over several seconds as the
    backend resolves each item.
    """
    plan = await api.get_plan(plan_id)
<span style="opacity: 0.7;"># Panel: Plan metadata (created and closed synchronously)</span>
<span style="color: magenta; font-weight: 600;">with</span> session.panel(<span style="color: #66cc66;">&quot;Plan&quot;</span>) <span style="color: magenta; font-weight: 600;">as</span> panel:
    panel.set_entries({
        <span style="color: #66cc66;">&quot;Plan ID&quot;</span>: plan.id,
        <span style="color: #66cc66;">&quot;Phase&quot;</span>: plan.phase,
        <span style="color: #66cc66;">&quot;State&quot;</span>: plan.state,
        <span style="color: #66cc66;">&quot;Action&quot;</span>: plan.action,
        <span style="color: #66cc66;">&quot;Project&quot;</span>: plan.project,
        <span style="color: #66cc66;">&quot;Started&quot;</span>: plan.started_at.strftime(<span style="color: #66cc66;">&quot;%H:%M:%S&quot;</span>),
    }, style_hints={
        <span style="color: #66cc66;">&quot;Plan ID&quot;</span>: <span style="color: #66cc66;">&quot;identifier&quot;</span>,
        <span style="color: #66cc66;">&quot;Phase&quot;</span>: <span style="color: #66cc66;">&quot;info&quot;</span>,
        <span style="color: #66cc66;">&quot;State&quot;</span>: <span style="color: #66cc66;">&quot;warning&quot;</span> <span style="color: magenta; font-weight: 600;">if</span> plan.state == <span style="color: #66cc66;">&quot;processing&quot;</span> <span style="color: magenta; font-weight: 600;">else</span> <span style="color: #66cc66;">&quot;success&quot;</span>,
    })

<span style="opacity: 0.7;"># Create both table handles BEFORE starting concurrent producers.</span>
<span style="opacity: 0.7;"># Declaration order determines rendering order in sequential formats.</span>
resource_table = session.table(<span style="color: #66cc66;">&quot;Resource Status&quot;</span>, columns=[
    ColumnDef(name=<span style="color: #66cc66;">&quot;Resource&quot;</span>, style_hint=<span style="color: #66cc66;">&quot;identifier&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Type&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Status&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Latency&quot;</span>, type=<span style="color: #66cc66;">&quot;string&quot;</span>, alignment=<span style="color: #66cc66;">&quot;right&quot;</span>),
])

tool_table = session.table(<span style="color: #66cc66;">&quot;Tool Call Log&quot;</span>, columns=[
    ColumnDef(name=<span style="color: #66cc66;">&quot;#&quot;</span>, type=<span style="color: #66cc66;">&quot;number&quot;</span>, alignment=<span style="color: #66cc66;">&quot;right&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Tool&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Target&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Result&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Duration&quot;</span>, type=<span style="color: #66cc66;">&quot;string&quot;</span>, alignment=<span style="color: #66cc66;">&quot;right&quot;</span>),
])

<span style="opacity: 0.7;"># --- Run two producers concurrently ---</span>
<span style="opacity: 0.7;"># Each producer writes to its own handle. Neither producer knows</span>
<span style="opacity: 0.7;"># which format is active. The materialization strategy handles</span>
<span style="opacity: 0.7;"># the coordination.</span>

<span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">stream_resources</span>():
    <span style="color: #66cc66;">&quot;&quot;&quot;Producer A: streams resource status checks.&quot;&quot;&quot;</span>
    <span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">for</span> status <span style="color: magenta; font-weight: 600;">in</span> api.stream_resource_statuses(plan.id):
        resource_table.add_row({
            <span style="color: #66cc66;">&quot;Resource&quot;</span>: status.resource_name,
            <span style="color: #66cc66;">&quot;Type&quot;</span>: status.resource_type,
            <span style="color: #66cc66;">&quot;Status&quot;</span>: status.status,
            <span style="color: #66cc66;">&quot;Latency&quot;</span>: <span style="color: #66cc66;">f&quot;{status.latency_ms}ms&quot;</span>,
        })
    resource_table.close()

<span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">stream_tool_calls</span>():
    <span style="color: #66cc66;">&quot;&quot;&quot;Producer B: streams tool call results.&quot;&quot;&quot;</span>
    <span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">for</span> call <span style="color: magenta; font-weight: 600;">in</span> api.stream_tool_calls(plan.id):
        tool_table.add_row({
            <span style="color: #66cc66;">&quot;#&quot;</span>: call.sequence_number,
            <span style="color: #66cc66;">&quot;Tool&quot;</span>: call.tool_name,
            <span style="color: #66cc66;">&quot;Target&quot;</span>: call.target,
            <span style="color: #66cc66;">&quot;Result&quot;</span>: call.result_summary,
            <span style="color: #66cc66;">&quot;Duration&quot;</span>: <span style="color: #66cc66;">f&quot;{call.duration_ms}ms&quot;</span>,
        })
    tool_table.close()

<span style="opacity: 0.7;"># Launch both producers concurrently</span>
<span style="color: magenta; font-weight: 600;">await</span> asyncio.gather(stream_resources(), stream_tool_calls())

<span style="opacity: 0.7;"># Final status</span>
session.status(<span style="color: #66cc66;">f&quot;Plan {plan_id} status retrieved&quot;</span>, level=<span style="color: #66cc66;">&quot;ok&quot;</span>)

What this produces in plain format (SequentialBufferMaterializer):

Both tables are populated concurrently, but the materializer buffers each one and renders them in declaration order when their handles close. The user sees nothing until the first-declared table (Resource Status) closes, then it prints. Then when the second table (Tool Call Log) closes, it prints. Data may have arrived interleaved across both tables, but the output is perfectly sequential:

Plan
  Plan ID: 01HXM7A9
  Phase: execute
  State: processing
  Action: local/code-coverage
  Project: local/api-service
  Started: 12:58:10
Resource Status
Resource          Type            Status   Latency

local/api-repo    git-checkout    ready    42ms
local/staging-db  local/database  ready    128ms
Tool Call Log
Tool        Target                  Result         Duration

1  read_file   src/auth/__init__.py    200 lines      0.1s
2  read_file   src/auth/session.py     340 lines      0.1s
3  write_file  tests/test_auth.py      created        0.2s
4  edit_file   src/auth/session.py     12 lines +/-   0.3s
5  run_tests   pytest tests/test_auth  3 passed       2.1s
[OK] Plan 01HXM7A9 status retrieved

What this produces in rich format (LiveMaterializer):

Both tables are visible simultaneously and update in-place as data arrives. This snapshot shows the display mid-stream — the resource table has two rows and the tool call table has three so far:

╭─ Plan ──────────────────────────────╮
│ Plan ID: 01HXM7A9                   │
│ Phase: execute                     │
│ State: processing                  │
│ Action: local/code-coverage         │
│ Project: local/api-service          │
│ Started: 12:58:10                   │
╰─────────────────────────────────────╯
╭─ Resource Status ⠙ ───────────────────────────────────────────╮
│ Resource          Type            Status   Latency        │
│ ────────────────  ──────────────  ───────  ───────        │
│ local/api-repo    git-checkout    ready    42ms           │
│ local/staging-db  local/database  ready    128ms          │
│                                                              │
│ 2 resources (streaming...)                                  │
╰──────────────────────────────────────────────────────────────╯
╭─ Tool Call Log ⠙ ────────────────────────────────────────────────────╮
│ #   Tool        Target                  Result         Duration   │
│ ──  ──────────  ──────────────────────  ─────────────  ────────   │
│  1  read_file   src/auth/__init__.py    200 lines      0.1s       │
│  2  read_file   src/auth/session.py     340 lines      0.1s       │
│  3  write_file  tests/test_auth.py      created        0.2s       │
│                                                                      │
│ 3 calls (streaming...)                                              │
╰──────────────────────────────────────────────────────────────────────╯

In rich mode, both tables have animated spinners in their titles indicating active streaming. As new rows arrive from either producer, the corresponding table's display is updated in-place. When a producer finishes and closes its handle, the spinner resolves to a ✓ and the "(streaming...)" indicator is removed. The other table continues updating independently.

What this produces in json format (AccumulateMaterializer):

Nothing is printed until the session closes. Then the complete accumulated state is serialized:

{
  "command": "plan status",
  "status": "ok",
  "exit_code": 0,
  "data": {
    "plan": {
      "Plan ID": "01HXM7A9",
      "Phase": "execute",
      "State": "processing",
      "Action": "local/code-coverage",
      "Project": "local/api-service",
      "Started": "12:58:10"
    },
    "resource_status": [
      { "Resource": "local/api-repo", "Type": "git-checkout", "Status": "ready", "Latency": "42ms" },
      { "Resource": "local/staging-db", "Type": "local/database", "Status": "ready", "Latency": "128ms" }
    ],
    "tool_call_log": [
      { "#": 1, "Tool": "read_file", "Target": "src/auth/__init__.py", "Result": "200 lines", "Duration": "0.1s" },
      { "#": 2, "Tool": "read_file", "Target": "src/auth/session.py", "Result": "340 lines", "Duration": "0.1s" },
      { "#": 3, "Tool": "write_file", "Target": "tests/test_auth.py", "Result": "created", "Duration": "0.2s" },
      { "#": 4, "Tool": "edit_file", "Target": "src/auth/session.py", "Result": "12 lines +/-", "Duration": "0.3s" },
      { "#": 5, "Tool": "run_tests", "Target": "pytest tests/test_auth", "Result": "3 passed", "Duration": "2.1s" }
    ]
  },
  "timing": { "duration_ms": 3200 },
  "messages": [
    { "level": "ok", "text": "Plan 01HXM7A9 status retrieved" }
  ]
}

The critical point: the producer code in all three formats is exactly the same. The asyncio.gather call runs both producers concurrently regardless of format. The materialization strategy — LiveMaterializer, SequentialBufferMaterializer, or AccumulateMaterializer — transparently decides how that concurrent data reaches the user.

Example 4: Progress with Concurrent Sub-Operations

A command that executes a multi-step process with a progress indicator, where some steps involve parallel sub-operations.

Producer code (agents plan execute):

async def cmd_plan_execute(session: OutputSession, plan_id: str) -> None:
    """Implementation of 'agents plan execute <plan_id>'."""
    plan = await api.get_plan(plan_id)
<span style="opacity: 0.7;"># Panel: Execution metadata</span>
<span style="color: magenta; font-weight: 600;">with</span> session.panel(<span style="color: #66cc66;">&quot;Execution&quot;</span>) <span style="color: magenta; font-weight: 600;">as</span> panel:
    panel.set_entries({
        <span style="color: #66cc66;">&quot;Plan&quot;</span>: plan.id,
        <span style="color: #66cc66;">&quot;Phase&quot;</span>: <span style="color: #66cc66;">&quot;execute&quot;</span>,
        <span style="color: #66cc66;">&quot;Sandbox&quot;</span>: plan.sandbox_strategy,
        <span style="color: #66cc66;">&quot;Worker&quot;</span>: plan.worker,
        <span style="color: #66cc66;">&quot;Started&quot;</span>: <span style="color: cyan;">datetime</span>.now().strftime(<span style="color: #66cc66;">&quot;%H:%M:%S&quot;</span>),
    })

<span style="opacity: 0.7;"># Progress indicator with named steps</span>
progress = session.progress(<span style="color: #66cc66;">&quot;Executing plan&quot;</span>, total=4, steps=[
    <span style="color: #66cc66;">&quot;Collect context&quot;</span>,
    <span style="color: #66cc66;">&quot;Run tools&quot;</span>,
    <span style="color: #66cc66;">&quot;Build changeset&quot;</span>,
    <span style="color: #66cc66;">&quot;Validate&quot;</span>,
])

<span style="opacity: 0.7;"># Step 1: Collect context</span>
progress.set_step_status(<span style="color: #66cc66;">&quot;Collect context&quot;</span>, <span style="color: #66cc66;">&quot;active&quot;</span>)
context = <span style="color: magenta; font-weight: 600;">await</span> api.collect_context(plan.id)
progress.set_step_status(<span style="color: #66cc66;">&quot;Collect context&quot;</span>, <span style="color: #66cc66;">&quot;done&quot;</span>)
progress.set_progress(1, 4)

<span style="opacity: 0.7;"># Step 2: Run tools (parallel sub-operations)</span>
progress.set_step_status(<span style="color: #66cc66;">&quot;Run tools&quot;</span>, <span style="color: #66cc66;">&quot;active&quot;</span>)
tool_results = <span style="color: magenta; font-weight: 600;">await</span> api.run_tools(plan.id, context)
progress.set_step_status(<span style="color: #66cc66;">&quot;Run tools&quot;</span>, <span style="color: #66cc66;">&quot;done&quot;</span>)
progress.set_progress(2, 4)

<span style="opacity: 0.7;"># Step 3: Build changeset</span>
progress.set_step_status(<span style="color: #66cc66;">&quot;Build changeset&quot;</span>, <span style="color: #66cc66;">&quot;active&quot;</span>)
changeset = <span style="color: magenta; font-weight: 600;">await</span> api.build_changeset(plan.id, tool_results)
progress.set_step_status(<span style="color: #66cc66;">&quot;Build changeset&quot;</span>, <span style="color: #66cc66;">&quot;done&quot;</span>)
progress.set_progress(3, 4)

<span style="opacity: 0.7;"># Step 4: Validate</span>
progress.set_step_status(<span style="color: #66cc66;">&quot;Validate&quot;</span>, <span style="color: #66cc66;">&quot;active&quot;</span>)
validation = <span style="color: magenta; font-weight: 600;">await</span> api.validate_changeset(plan.id, changeset)
progress.set_step_status(<span style="color: #66cc66;">&quot;Validate&quot;</span>, <span style="color: #66cc66;">&quot;done&quot;</span>)
progress.set_progress(4, 4)

progress.close()

<span style="opacity: 0.7;"># Summary panel</span>
<span style="color: magenta; font-weight: 600;">with</span> session.panel(<span style="color: #66cc66;">&quot;Strategy Summary&quot;</span>) <span style="color: magenta; font-weight: 600;">as</span> panel:
    panel.set_entries({
        <span style="color: #66cc66;">&quot;Decisions&quot;</span>: <span style="color: cyan;">str</span>(changeset.decision_count),
        <span style="color: #66cc66;">&quot;Invariants&quot;</span>: <span style="color: cyan;">str</span>(changeset.invariant_count),
        <span style="color: #66cc66;">&quot;Planned Child Plans&quot;</span>: <span style="color: #66cc66;">f&quot;{changeset.child_plan_count}+&quot;</span>,
        <span style="color: #66cc66;">&quot;Estimated Files&quot;</span>: <span style="color: #66cc66;">f&quot;~{changeset.file_count}&quot;</span>,
        <span style="color: #66cc66;">&quot;Risk&quot;</span>: changeset.risk_level,
    })

<span style="opacity: 0.7;"># Final status</span>
<span style="color: magenta; font-weight: 600;">if</span> validation.passed:
    session.status(<span style="color: #66cc66;">&quot;Execution complete — all validations passed&quot;</span>, level=<span style="color: #66cc66;">&quot;ok&quot;</span>)
<span style="color: magenta; font-weight: 600;">else</span>:
    session.status(
        <span style="color: #66cc66;">f&quot;Execution complete — {validation.failure_count} validation(s) failed&quot;</span>,
        level=<span style="color: #66cc66;">&quot;warn&quot;</span>,
        detail=validation.summary,
    )

What this looks like in plain format:

The progress indicator renders as a static step list. Since SequentialBufferMaterializer buffers each element until its handle closes, the progress indicator is not visible during execution — it appears as a completed snapshot after the fact:

Execution
  Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J
  Phase: execute
  Sandbox: git_worktree
  Worker: local/executor
  Started: 12:58:10
Executing plan  [4/4]
[x] Collect context
[x] Run tools
[x] Build changeset
[x] Validate
Strategy Summary
Decisions: 8
Invariants: 2
Planned Child Plans: 2+
Estimated Files: ~12
Risk: low
[OK] Execution complete — all validations passed

What this looks like in rich format:

The progress indicator is live — the spinner animates, the progress bar fills, and steps transition from pending to active to done in real-time. This snapshot shows the display mid-execution (step 3 active):

╭─ Execution ──────────────────────╮
│ Plan: 01HXM8C2ZK4Q7C2B3F2R4VYV6J │
│ Phase: execute                   │
│ Sandbox: git_worktree            │
│ Worker: local/executor           │
│ Started: 12:58:10                │
╰──────────────────────────────────╯
Executing plan ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  50%  elapsed 0:00:13
✓ Collect context .................. 0.8s
✓ Run tools ...................... 12.4s
⠙ Build changeset ................. (running)
○ Validate ........................ (pending)

When execution completes, the progress indicator resolves to its final state (all steps ✓), the Strategy Summary panel appears below it, and the final status message is displayed.

Example 5: Error Mid-Stream with Partial Output

A command where one of multiple concurrent producers fails, demonstrating graceful partial output.

Producer code (hypothetical agents resource verify):

async def cmd_resource_verify(session: OutputSession, project: str) -> None:
    """Verify all resources in a project. Some verifications may fail."""
    resources = await api.list_project_resources(project)
<span style="opacity: 0.7;"># Create a table that will be populated concurrently</span>
results_table = session.table(<span style="color: #66cc66;">&quot;Verification Results&quot;</span>, columns=[
    ColumnDef(name=<span style="color: #66cc66;">&quot;Resource&quot;</span>, style_hint=<span style="color: #66cc66;">&quot;identifier&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Type&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Check&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Status&quot;</span>),
    ColumnDef(name=<span style="color: #66cc66;">&quot;Detail&quot;</span>),
])

<span style="opacity: 0.7;"># Progress indicator</span>
progress = session.progress(
    <span style="color: #66cc66;">&quot;Verifying resources&quot;</span>,
    total=len(resources),
    steps=[r.name <span style="color: magenta; font-weight: 600;">for</span> r <span style="color: magenta; font-weight: 600;">in</span> resources],
)

<span style="opacity: 0.7;"># Verify each resource concurrently</span>
<span style="color: magenta; font-weight: 600;">async</span> <span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">verify_one</span>(resource):
    progress.set_step_status(resource.name, <span style="color: #66cc66;">&quot;active&quot;</span>)
    <span style="color: magenta; font-weight: 600;">try</span>:
        result = <span style="color: magenta; font-weight: 600;">await</span> api.verify_resource(resource.id)
        results_table.add_row({
            <span style="color: #66cc66;">&quot;Resource&quot;</span>: resource.name,
            <span style="color: #66cc66;">&quot;Type&quot;</span>: resource.type,
            <span style="color: #66cc66;">&quot;Check&quot;</span>: result.check_name,
            <span style="color: #66cc66;">&quot;Status&quot;</span>: <span style="color: #66cc66;">&quot;pass&quot;</span> <span style="color: magenta; font-weight: 600;">if</span> result.passed <span style="color: magenta; font-weight: 600;">else</span> <span style="color: #66cc66;">&quot;fail&quot;</span>,
            <span style="color: #66cc66;">&quot;Detail&quot;</span>: result.detail,
        })
        progress.set_step_status(
            resource.name,
            <span style="color: #66cc66;">&quot;done&quot;</span> <span style="color: magenta; font-weight: 600;">if</span> result.passed <span style="color: magenta; font-weight: 600;">else</span> <span style="color: #66cc66;">&quot;error&quot;</span>,
        )
    <span style="color: magenta; font-weight: 600;">except</span> ApiError <span style="color: magenta; font-weight: 600;">as</span> e:
        results_table.add_row({
            <span style="color: #66cc66;">&quot;Resource&quot;</span>: resource.name,
            <span style="color: #66cc66;">&quot;Type&quot;</span>: resource.type,
            <span style="color: #66cc66;">&quot;Check&quot;</span>: <span style="color: #66cc66;">&quot;connection&quot;</span>,
            <span style="color: #66cc66;">&quot;Status&quot;</span>: <span style="color: #66cc66;">&quot;error&quot;</span>,
            <span style="color: #66cc66;">&quot;Detail&quot;</span>: <span style="color: cyan;">str</span>(e),
        })
        progress.set_step_status(resource.name, <span style="color: #66cc66;">&quot;error&quot;</span>)
    progress.increment()

<span style="opacity: 0.7;"># Launch all verifications concurrently</span>
<span style="color: magenta; font-weight: 600;">await</span> asyncio.gather(
    *[verify_one(r) <span style="color: magenta; font-weight: 600;">for</span> r <span style="color: magenta; font-weight: 600;">in</span> resources],
    return_exceptions=<span style="color: magenta; font-weight: 600;">True</span>,  <span style="opacity: 0.7;"># Don&#x27;t fail fast — collect all results</span>
)

progress.close()
results_table.close()

<span style="opacity: 0.7;"># Summarize</span>
snapshot = results_table.element
pass_count = sum(1 <span style="color: magenta; font-weight: 600;">for</span> r <span style="color: magenta; font-weight: 600;">in</span> snapshot.rows <span style="color: magenta; font-weight: 600;">if</span> r[<span style="color: #66cc66;">&quot;Status&quot;</span>] == <span style="color: #66cc66;">&quot;pass&quot;</span>)
fail_count = sum(1 <span style="color: magenta; font-weight: 600;">for</span> r <span style="color: magenta; font-weight: 600;">in</span> snapshot.rows <span style="color: magenta; font-weight: 600;">if</span> r[<span style="color: #66cc66;">&quot;Status&quot;</span>] <span style="color: magenta; font-weight: 600;">in</span> (<span style="color: #66cc66;">&quot;fail&quot;</span>, <span style="color: #66cc66;">&quot;error&quot;</span>))

<span style="color: magenta; font-weight: 600;">if</span> fail_count == 0:
    session.status(<span style="color: #66cc66;">f&quot;All {pass_count} resources verified&quot;</span>, level=<span style="color: #66cc66;">&quot;ok&quot;</span>)
<span style="color: magenta; font-weight: 600;">else</span>:
    session.status(
        <span style="color: #66cc66;">f&quot;{fail_count} of {pass_count + fail_count} resources failed verification&quot;</span>,
        level=<span style="color: #66cc66;">&quot;error&quot;</span>,
    )

What this produces in plain format (after all concurrent verifications complete):

Verifying resources  [3/3]
  [x] local/api-repo
  [!] local/staging-db
  [x] local/docs-repo
Verification Results
Resource          Type            Check       Status  Detail

local/api-repo    git-checkout    integrity   pass    All refs valid
local/staging-db  local/database  connection  error   Connection refused (port 5432)
local/docs-repo   git-checkout    integrity   pass    All refs valid
[ERROR] 1 of 3 resources failed verification

What this produces in color format:

Verifying resources  [3/3]
  [x] local/api-repo
  [!] local/staging-db
  [x] local/docs-repo
Verification Results
Resource          Type            Check       Status  Detail
----------------  --------------  ----------  ------  ----------------------------------
local/api-repo    git-checkout    integrity   pass    All refs valid
local/staging-db  local/database  connection  error   Connection refused (port 5432)
local/docs-repo   git-checkout    integrity   pass    All refs valid
[ERROR] 1 of 3 resources failed verification

What this produces in yaml format:

command: resource verify
status: error
exit_code: 1
data:
  verification_results:
    - Resource: local/api-repo
      Type: git-checkout
      Check: integrity
      Status: pass
      Detail: All refs valid
    - Resource: local/staging-db
      Type: local/database
      Check: connection
      Status: error
      Detail: "Connection refused (port 5432)"
    - Resource: local/docs-repo
      Type: git-checkout
      Check: integrity
      Status: pass
      Detail: All refs valid
timing:
  duration_ms: 2840
messages:
  - level: error
    text: "1 of 3 resources failed verification"

In all formats, the concurrent verification produces a complete result set with partial failures clearly visible. The producer code uses return_exceptions=True on asyncio.gather to ensure all verifications complete even if some fail, and the error handling within each verify_one coroutine ensures that failures are recorded as table rows rather than causing the entire command to abort.

Core Concepts

Plan

A plan is the fundamental unit of orchestration and traceability.

Plan Lifecycle Phases

A plan always moves through the following phases, in order:

Action → Strategize → Execute → Apply → Applied (terminal)

In this spec:

• Strategize is the phase name (the output is a strategy).
• Execute is the phase name (the output is a changeset).
• Apply is the phase name (the output is an applied change).
• Applied is the resulting terminal state after Apply succeeds.

This four-stage model is explicitly called out as the new architecture replacing a prior linear pipeline.

Phase Transition Verbs (CLI / UX Contract)

Verbs that trigger phase transitions. CleverAgents should standardize these verbs as the public API (CLI, TUI, web):

Current Phase	Command Verb	Next Phase
(none)	create	Action
Action	use	Strategize
Strategize	execute	Execute
Execute	apply	Applied

Important behavioral rule: CleverAgents uses automation profiles to control which of these phase transitions happen automatically. The profile determines whether each transition requires explicit user action or proceeds autonomously, but the verbs remain the conceptual contract.

Plan States (Per Phase)

A plan's phase indicates "what step of the lifecycle it is in." Separately, the plan has a processing state indicating "what is happening right now."

Recommended state model:

• Action phase states

  • available (action exists and can be used)
  • draft (action is being authored/edited)
  • archived (soft-deleted or hidden, optional)

• Strategize / Execute / Apply phase states

  • queued (waiting for compute/worker)
  • processing (currently running)
  • errored (failed; includes error metadata)
  • complete (finished successfully)
  • cancelled (user/system cancelled; safe terminal for that phase)

Plan Identity and Traceability

Every plan should have:

• plan_id: Unique, immutable ID (UUID or ULID).
• parent_plan_id: Nullable; present for child plans.
• root_plan_id: The top-most plan in the tree.
• attempt: An integer attempt counter that increments when re-running a phase (e.g., re-executing after a fix).
• created_at / updated_at / completed_at timestamps.
• created_by (user identity / session identity).

Plan Hierarchy and Parallelism

A single plan should usually represent the smallest "complete" unit of work (similar to what would fit in one git commit). However:

• Plans are hierarchical. A child plan is simply a Plan with a parent — it follows the same lifecycle, has the same data model, and is functionally identical to a root plan except that it has a parent_plan_id.
• Decisions about child plans are made during Strategize (as subplan_spawn decision types). Multiple child plans that should execute concurrently are grouped under a subplan_parallel_spawn decision.
• Child plans are actually spawned during Execute (based on those decisions).
• Child plans can run in parallel (via subplan_parallel_spawn) or sequentially (via individual subplan_spawn decisions). Without a subplan_parallel_spawn wrapper, each subplan_spawn decision results in sequential execution.
• Applicable invariants are enforced during Strategize by adding invariant_enforced decisions to the tree, which constrain downstream decisions and child plans.
• The parent plan is responsible for merging results.

This is core to the long-term objective: tackling large tasks while only recomputing parts of the decision tree when corrected.

Hierarchical Decomposition for Scale

When handling massive tasks (e.g., converting Firefox to Rust), the system uses hierarchical decomposition. Each level spawns child plans (which are themselves full Plans with their own decision trees):

1. Root Plan: High-level architectural decisions and invariants

   • "Convert Firefox Renderer to Rust"
   • Invariant: "Maintain API compatibility with existing C++ callers"
   • Decision: "Start with leaf modules, work inward"
   • Context: Module dependency graph (2,847 modules)

2. Subsystem-Level Plans: Major component decisions (spawned via subplan_parallel_spawn for independent subsystems)

   • "Phase 1: Convert utility libraries (no external deps)"
   • Each subsystem plan gets its own bounded context and inherits parent invariants

3. Module-Level Plans: Individual module conversions

   • "Convert string_utils module"
   • Context: Only the 47 functions and 12 dependent files
   • Decision: "Use Rust's String type"

4. File-Level Plans: Specific file changes

   • Actual code transformations
   • Minimal context needed

At each level, only the relevant context is loaded. The persistent decision graph means we can always reconstruct why we're converting a particular module and what constraints (including inherited invariants) apply from higher-level decisions.

Child Plan Spawning Mechanism

In the actor definition for the execution actor, tool nodes can directly invoke registered tools to trigger child plans. The local/create-subplan tool is independently registered and referenced by name in the actor graph node. During execution, subplan_spawn decisions are realized as actual child plans, and subplan_parallel_spawn groups trigger concurrent spawning of all enclosed child plans.

# Example: Execution actor with subplan spawning capability.
# The graph uses a tool node referencing a named registered tool.
actors:
  code_executor:
    type: graph
    config:
      actor: anthropic/claude-3-opus
    skills:
      - local/plan-tools          # Skill containing create_subplan for LLM tool-calling
      - local/file-ops
    routes:
      execute_workflow:
        nodes:
          - name: spawn_test_subplan
            type: tool
            tool: local/create-subplan # Named tool from Tool Registry

The local/create-subplan tool is independently registered via its own YAML configuration file:

# File: tools/create-subplan.yaml
cleveragents:
  version: "3.0"
tool:
name: local/create-subplan
description: "Spawn a subplan for a given action"
source: custom
input_schema:
type: object
properties:
action: { type: string }
target_files: { type: array, items: { type: string } }
required: [action]
capability:
writes: true
checkpointable: false
side_effects: [spawn_subplan]
code: |
subplan = ctx.spawn_subplan(
action=params["action"],
target_files=params.get("target_files", [])
)
return {"subplan_id": subplan.id}

The local/plan-tools skill references this tool (and others) by name:

# File: skills/plan-tools.yaml
skill:
  name: local/plan-tools
  description: "Tools for spawning and managing subplans"
  tools:
    - local/create-subplan

Child Plan Execution Modes

• Sequential: Individual subplan_spawn decisions without a subplan_parallel_spawn wrapper execute one after another. If one fails, subsequent child plans are not started.
• Parallel: Multiple subplan_spawn decisions grouped under a subplan_parallel_spawn decision execute concurrently. If one fails, others can continue. The subplan_parallel_spawn decision acts as a container that signals the system to spawn all enclosed child plans simultaneously.

Child Plan Failure Handling

• Parallel execution (via subplan_parallel_spawn): Other parallel child plans continue even if one fails.
• Sequential execution: Subsequent child plans are not started if a prior one fails.
• Note: An "error" only occurs if an exception is thrown by the application (a bug). Plan failures (e.g., tests don't pass) are handled within the plan's logic, not as application errors.

Child Plan Result Merging

The way child plan results are merged depends on the resource type:

• Git-compatible resources (source code, text files): Git-style merge
• Databases: Transaction coordination or sequential application
• Other resources: Pluggable merge strategies based on resource type
• Non-mergeable resources: May require sequential execution only

The Plan "Decision Tree" and Visualization

CleverAgents intends to record enough information to render:

• an ASCII tree in the TUI, and
• optionally a GUI tree via visualization tools (D3/Cytoscape) once the data exists.

This implies each plan should persist:

• decisions made,
• the rationale (or at least the prompt/context snapshot that produced it),
• dependencies ("this decision influenced these child plans").

This is required for "correcting plans" (see Behavior section).

Decision Data Model

Relationship Between Plan Description and Decisions

Each plan has a description field (inherited from the action's description, potentially with argument substitutions). This description acts as the primary component of the prompt fed to the strategy actor during the Strategize phase.

Decisions are choices that are NOT explicitly defined by the plan description. They represent the gaps, ambiguities, or implementation details that must be resolved to execute the plan.

For example:

• Plan description: "Increase test coverage to 85%"
• Decisions that emerge:
  • "Which modules should be prioritized?" (not specified in description)
  • "Should we use mocks or integration tests for the database layer?" (not specified)
  • "Should we refactor the auth module to make it more testable, or write tests around it as-is?" (not specified)

Decision Making Based on Autonomy Level

Who makes decisions depends on the plan's automation profile:

Profile Flag	Who Makes Decisions
auto_decisions_strategize = false	User is prompted for each decision point during Strategize
auto_decisions_strategize = true	Strategy actor makes decisions autonomously, records reasoning
auto_decisions_execute = false	User is prompted for each decision point during Execute
auto_decisions_execute = true	Execution actor makes decisions autonomously

When automation allows automatic decisions, the strategy actor uses its best judgment based on context, and records its reasoning in the decision's rationale field.

When user input is required, the system pauses and prompts the user:

Decision required: Which modules should be prioritized for test coverage?
Options identified by the strategy actor:

auth module (currently 45% coverage, high risk)
payment module (currently 52% coverage, high risk)
user module (currently 71% coverage, medium risk)

Your choice (or provide custom guidance): _

The Prompt as the Root Decision

The prompt passed to the strategize actor is itself a decision node in the decision tree—specifically, it's the root decision of type prompt_definition.

This is important because:

1. Every plan has its own prompt: The root plan's prompt comes from the action description + user arguments. Child plan prompts are created by parent plans during their execution.

2. Parent plans create child plan prompts: When a parent plan spawns a child plan, it decides what prompt to give that child plan. This is recorded as a prompt_definition decision in the parent's tree, and becomes the root decision of the child plan's tree.

3. Invariants flow into the decision tree: During Strategize, applicable invariants (from global, project, action, and plan scopes) are reconciled via the Invariant Reconciliation Actor and recorded as invariant_enforced decisions, making them explicit constraints that influence downstream decisions and child plans.

4. Unified correction mechanism: Since the prompt, invariants, and all other decisions are part of the same tree, correcting any of them uses the same agents plan correct command.

Plan: 01KH29QDEE6DZTXKWNKCV8VP0F
├── [prompt_definition] "Increase test coverage to 85% for the whole project."
├── [invariant_enforced] "Prioritize all functionality related to financial transactions and user management"
├── [invariant_enforced] "All API calls over TCP must be mocked"
├── [strategy_choice] "Prioritize auth and payment modules, implement them in parallel before the rest"
├── [subplan_parallel_spawn] "Implement auth and payment modules, in parallel"
│   ├── [subplan_spawn] "Write tests for auth module"
│   │   └── Plan: 01KH29R8WPKPBHRY7Q0NA9XW86
│   │       ├── [prompt_definition] "Write unit tests for auth module using mocks for the remote API calls"
│   │       ├── [implementation_choice] "Test login flow first"
│   │       └── ...
│   └── [subplan_spawn] "Write tests for payment module"
│       └── Plan: 01KH29RN2YKSXMTBDG82AKRHRA
│           ├── [prompt_definition] "Write unit tests for payment module using"
│           └── ...
└── [subplan_parallel_spawn] "Write tests for all modules except the auth and payment modules"
    └──  ...

Correcting Decisions (Including Prompts)

All corrections use the same unified command:

agents plan correct <decision_id> --mode=<mode> --guidance "<corrected decision text>"

Parameters:

• <decision_id>: The ULID of the decision to correct
• --mode: Either revert (rollback and re-run) or append (add fix at end)
• --guidance: Free-form text specifying what the correct decision should be

Examples:

# Correct a strategy choice
agents plan correct 01ARZ3NDEKTSV4RRFFQ69G5FAV --mode=revert \
  --guidance "Prioritize the payment module first, not auth, due to upcoming deadline"
# Correct the root prompt to be more specific
agents plan tree <plan_id>
# Shows: [prompt_definition] id=01ARZ3NDEKTSV4RRFFQ69G5FAV "Increase test coverage to 85%"
agents plan correct 01ARZ3NDEKTSV4RRFFQ69G5FAV --mode=revert </span>
--guidance "Increase test coverage to 85%, prioritizing auth and payment modules. Use mocks for database tests, not integration tests."
# Correct a subplan's prompt (originally created by parent plan)
agents plan correct 01BRZ4PDFLUTW5SSGR70H6GBW --mode=revert </span>
--guidance "Write unit tests for auth module, focusing on edge cases for token expiration"
# Append a fix rather than rewriting history
agents plan correct 01ARZ3NDEKTSV4RRFFQ69G5FAV --mode=append </span>
--guidance "The previous approach missed error handling tests - add comprehensive error path coverage"
# Remove an invariant that shouldn't apply
agents plan correct 01CRZ5QEHMVUX6TTHR81I7HCX --mode=revert </span>
--guidance "Remove this invariant - TCP mocking is not needed for this module since it has no network calls"
# Add a missing invariant to the plan
agents invariant add --plan 01HXM8C2ZK4Q7C2B3F2R4VYV6J "All database queries must use parameterized statements"

Note: CLI commands should not require interactive input. The --guidance parameter provides the correction inline.

When to correct the prompt vs. a specific decision:

Situation	Correction Approach
Original request was too vague	Correct the prompt_definition decision
Strategy actor made a bad choice on a specific question	Correct that specific decision
Parent plan gave a child plan a bad prompt	Correct the child plan's prompt_definition
An invariant should not apply to this plan	Correct (remove) the invariant_enforced decision
A missing constraint should be added	Add a new invariant_enforced decision via agents invariant add --plan or correct the plan's strategy
Sequential child plans should run in parallel	Correct the relevant decisions to use a subplan_parallel_spawn grouping

Because the prompt is part of the decision tree, the system automatically knows that correcting it invalidates all downstream decisions in that plan (and its child plans).

Decisions are only created during the Strategize phase. The decision tree captures what choices were made and why, enabling correction and replay.

Decision Record Structure

Decision:
  # Identity
  decision_id: ULID # Unique identifier
  plan_id: ULID # Parent plan this decision belongs to
  parent_decision_id: ULID | null # Parent decision (for tree structure)
  sequence_number: int # Order within the plan's decisions
  # Classification
decision_type: enum
- prompt_definition         # The prompt/description for this plan (root decision)
- invariant_enforced        # An invariant (from global, project, action, or plan scope) applicable to this plan, added as a constraint
- strategy_choice           # High-level approach decision during Strategize
- implementation_choice     # How to implement a specific task
- resource_selection        # Which resources to read/modify
- subplan_spawn             # Decision to create a child plan (spawned later in Execute)
- subplan_parallel_spawn    # Decision to spawn a group of child plans in parallel (contains subplan_spawn children)
- tool_invocation           # Which skill/tool to use
- error_recovery            # How to handle a failure
- validation_response       # Response to validation failure
- user_intervention         # User provided guidance/correction
  # The Decision Itself
question: str # What question was being answered
chosen_option: str # What was decided
alternatives_considered: list[str] # Other options that were evaluated
confidence_score: float | null # 0.0-1.0 if the actor provided confidence
  # Context Snapshot (for replay)
context_snapshot:
hot_context_hash: str # Cryptographic hash of the exact context
hot_context_ref: str # Pointer to the full stored snapshot
relevant_resources: list[ResourceRef] # Every file/symbol that influenced this decision
actor_state_ref: str # Complete LangGraph checkpoint
  # When the system decides "refactor the authentication module to use async patterns," 
  # it permanently records:
  # - Which files were examined to make that decision
  # - What symbols and dependencies were traced
  # - The exact code state that was analyzed
  # - The reasoning chain that led to this choice
  # - Alternative approaches that were considered but rejected
  # Rationale
rationale: str # Why this option was chosen
actor_reasoning: str | null # Raw LLM reasoning if available
  # Downstream Impact (populated during Execute phase)
downstream_decision_ids: list[ULID] # Decisions that depend on this one
downstream_plan_ids: list[ULID] # Child plans spawned because of this decision
artifacts_produced: list[ArtifactRef] # Files/outputs created under this decision
  # Timestamps
created_at: datetime
  # Correction Metadata
is_correction: bool # Was this decision a correction of another?
corrects_decision_id: ULID | null # If correction, which decision was replaced
correction_reason: str | null # Why the correction was made
superseded_by: ULID | null # If this decision was later corrected

Decision Timing

Phase	Decision Activity
Strategize	Decisions are created, including invariant_enforced decisions for applicable invariants, subplan_spawn decisions, and subplan_parallel_spawn decisions grouping parallel child plans. downstream_plan_ids is empty.
Execute	Child plans are spawned. downstream_plan_ids is populated when child plans are created based on subplan_spawn decisions (both standalone and those within subplan_parallel_spawn groups).
Apply	No new decisions. History can be flagged for cleanup after successful apply.

Decision Tree Storage Schema

-- Core decision table
CREATE TABLE decisions (
    decision_id TEXT PRIMARY KEY,      -- ULID
    plan_id TEXT NOT NULL,
    parent_decision_id TEXT,
    sequence_number INTEGER NOT NULL,
    decision_type TEXT NOT NULL,        -- prompt_definition, invariant_enforced, strategy_choice,
                                       -- implementation_choice, resource_selection, subplan_spawn,
                                       -- subplan_parallel_spawn, tool_invocation, error_recovery,
                                       -- validation_response, user_intervention
    question TEXT,
    chosen_option TEXT NOT NULL,
    alternatives_considered TEXT,      -- JSON array
    confidence_score REAL,
    rationale TEXT,
    actor_reasoning TEXT,
    context_snapshot TEXT NOT NULL,    -- JSON blob
    is_correction BOOLEAN DEFAULT FALSE,
    corrects_decision_id TEXT,
    correction_reason TEXT,
    superseded_by TEXT,
    created_at TEXT NOT NULL,
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (plan_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> plans(plan_id),
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (parent_decision_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> decisions(decision_id),
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (corrects_decision_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> decisions(decision_id),
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (superseded_by) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> decisions(decision_id)

);
-- Downstream relationships (many-to-many for DAG)
CREATE TABLE decision_dependencies (
upstream_decision_id TEXT NOT NULL,
downstream_decision_id TEXT NOT NULL,
dependency_type TEXT NOT NULL,     -- 'decision', 'plan', 'artifact'
downstream_ref TEXT NOT NULL,      -- The actual ID of decision/plan/artifact
<span style="color: #5599ff; font-weight: 600;">PRIMARY</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (upstream_decision_id, downstream_decision_id, downstream_ref),
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (upstream_decision_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> decisions(decision_id)

);
-- Correction history
CREATE TABLE correction_attempts (
attempt_id TEXT PRIMARY KEY,       -- ULID
plan_id TEXT NOT NULL,
original_decision_id TEXT NOT NULL,
new_decision_id TEXT,
original_subtree_snapshot TEXT,    -- Reference to archived state
correction_reason TEXT,
status TEXT NOT NULL,              -- 'pending', 'executing', 'completed', 'failed'
created_at TEXT NOT NULL,
completed_at TEXT,
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (plan_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> plans(plan_id),
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (original_decision_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> decisions(decision_id),
<span style="color: #5599ff; font-weight: 600;">FOREIGN</span> <span style="color: #5599ff; font-weight: 600;">KEY</span> (new_decision_id) <span style="color: #5599ff; font-weight: 600;">REFERENCES</span> decisions(decision_id)

);

Action

What an Action Is

An action is a reusable plan template that is not associated with any projects yet.

Actions are created via CLI commands with a required --config YAML file that fully defines the action. Any CLI options supplied alongside the config file act as optional overrides for values in the YAML.

Examples:

• "Increase test coverage to 80%"
• "Refactor module X to be async-safe"
• "Write an RFC for feature Y"
• "Provision an infra cluster and validate access" (non-code)

Actions are intentionally project-agnostic so they can be reused across projects.

An action is the first stage of a plan — before it is used. Because of this, invariants can be attached to actions. When an action is used (via agents plan use), any invariants attached to the action are carried forward as plan-level invariants on the resulting plan. This allows teams to bake constraints directly into reusable templates. Invariants can also be added to a plan after the action is used, via agents invariant add --plan or --invariant flags on agents plan use.

Action Creation (CLI)

Actions are created using the CLI. There are two modes of creation:

1. Config-only (all values from YAML):

agents action create "local/code-coverage" \
  --config ./actions/code-coverage.yaml

2. Config with CLI overrides:

agents action create "local/code-coverage" \
  --config ./actions/code-coverage.yaml \
  --execution-actor "local/fast-executor" \
  --available

In both modes, the YAML file provides the full action definition. Any CLI options provided alongside --config override the corresponding values from the file.

Required parameters:

• --config: YAML configuration file that fully defines the action (strategy-actor, execution-actor, definition-of-done, etc.)

Optional parameters (overrides):

• [<NAME>]: Namespaced name (positional, e.g., local/code-coverage, myorg/deploy-action). When provided, overrides the name in the config file.
• --strategy-actor: Override the Strategize actor from config
• --execution-actor: Override the Execution actor from config
• --definition-of-done: Override the completion criteria from config

Optional parameters:

• --description: Human-readable description
• --arg: Argument definitions (can be repeated). Format: name:type:required|optional:description
• --invariant: Invariant to attach to this action (can be repeated). These are carried forward as plan-level invariants when the action is used.
• --invariant-actor: Invariant Reconciliation Actor for plans created from this action. Can be overridden at use time via agents plan use --invariant-actor.
• --reusable: Whether action remains available after use (default: true)
• --read-only: Whether action only performs read operations (default: false)

Arguments defined with --arg are values that will be:

• Injected into the description and/or definition of done (via templating)
• Passed into the context of the actors
• Required when using the action on projects

Action Data Model (Expanded)

A plan in the Action phase has:

1) name (namespaced)

Format:

• [server:][namespace/]<name>

Rules:

• If server is omitted, default server is assumed unless namespace is local.
• If namespace is omitted, default is local.
• Names should be stable identifiers (kebab-case recommended).

Examples:

• local/code-coverage
• myusername/code-coverage
• myorgname/code-coverage
• prod:myorgname/code-coverage (server-qualified)

2) short_description

Optional at creation; auto-filled if blank.

3) long_description

Optional but recommended for reusable actions.

4) definition_of_done (DoD)

Required. Must be explicit and testable.

5) actors

Two actors minimum, two optional:

• strategy_actor (planner/architect) — required
• execution_actor (builder/implementer) — required
• estimation_actor (cost/risk estimator) — optional
• invariant_actor (Invariant Reconciliation Actor) — optional; resolves invariant conflicts when the plan enters Strategize. Lookup falls back to project, then global config.

Actors can be:

• an LLM agent (built-in),
• a graph (custom yaml, or built-in),

Note: graphs are hierarchical allowing them to reference other actors as nodes.

Actor abstraction is central: an actor may be a single agent or an entire graph.

6) reusable (boolean)

• Default: true.
• If true: using the action creates a new plan in Strategize while leaving the action available.
• If false: action self-deletes (or auto-archives) after first use.

7) read_only (boolean)

• Default: false.
• If true: the plan must only use read-only tools (tools with read_only: true in their capability metadata) and must never modify resources (even in sandbox).
• Read-only actions are still useful for "investigation reports," architecture reviews, or dry-run planning.

8) inputs_schema (recommended addition)

To make actions genuinely reusable, actions should declare their inputs:

• required args (e.g., target coverage percent),
• optional args (e.g., test framework),
• validation rules (types, bounds).

Example:

• target_coverage_percent: integer 0–100

9) automation_profile

The resolved automation profile name for this plan (e.g., trusted, autonomous, local/careful-auto). Determined at plan use time using the profile precedence rules (plan > action > project > global). Once set, it is locked to the plan.

Strategy (Strategize Phase)

Using an Action (Transition to Strategize)

The use command transitions an Action into the Strategize phase by applying it to one or more projects:

# Basic usage
agents plan use local/code-coverage my-api-service
# Multiple projects
agents plan use local/schema-update </span>
api-service </span>
web-frontend </span>
mobile-app
# With action arguments
agents plan use local/code-coverage </span>
my-api-service </span>
--arg target_coverage_percent=85 </span>
--arg test_framework=pytest
# With explicit automation profile
agents plan use local/deploy-action </span>
staging-env </span>
--automation-profile manual
# With invariants attached at use time
agents plan use local/code-coverage </span>
my-api-service </span>
--arg target_coverage_percent=85 </span>
--invariant "All API calls over TCP must be mocked" </span>
--invariant "Do not modify the payments module"

Parameters:

• <PROJECT>: Project to apply the action to (positional, can be repeated for multi-project plans)
• --arg: Action argument values (format: name=value)
• --automation-profile: Override automation profile for this plan
• --invariant: Invariant to attach to the created plan (can be repeated). These are added as plan-level invariants in addition to any inherited from the action, project, or global scope.

When the action is used:

1. A new plan is created with a unique ULID
2. The plan's automation profile is resolved (plan > action > project > global precedence)
3. Any invariants from the action are carried forward as plan-level invariants, combined with any --invariant flags provided
4. The plan enters the Strategize phase
5. The Invariant Reconciliation Actor computes the effective invariant view (resolving conflicts using plan > project > global precedence)
6. The strategy_actor begins analyzing the project(s)

What Strategize Does

When an action is used on projects, it becomes a plan in Strategize.

Strategize is:

• read-only, producing a plan of attack,
• responsible for gathering context from project resources,
• responsible for collecting applicable invariants (from global, project, action, and plan scopes), computing the effective invariant view via the Invariant Reconciliation Actor (applying plan > project > global precedence), and recording them as invariant_enforced decisions,
• responsible for generating a strategy and child plan blueprint (using subplan_spawn and subplan_parallel_spawn decisions),
• not allowed to execute child plans or modify resources.

This "architect vs coder" separation is explicitly described as a core motivation.

Resource-aware dependency analysis: During the Strategize phase, the strategy actor employs specialized mechanisms to compute precise dependency closures:

# Pseudocode of what happens inside a strategy actor
def compute_closure_for_refactoring(target_module):
    closure = ResourceClosure()
<span style="opacity: 0.7;"># Direct file dependencies</span>
closure.add_files(find_imports(target_module))
closure.add_files(find_includes(target_module))

<span style="opacity: 0.7;"># Symbol dependencies</span>
<span style="color: magenta; font-weight: 600;">for</span> symbol <span style="color: magenta; font-weight: 600;">in</span> extract_exported_symbols(target_module):
    closure.add_files(find_symbol_usage(symbol, scope=<span style="color: #66cc66;">&#x27;project&#x27;</span>))

<span style="opacity: 0.7;"># Test dependencies</span>
closure.add_files(find_tests_for_module(target_module))

<span style="opacity: 0.7;"># Build system dependencies</span>
closure.add_files(find_build_references(target_module))

<span style="color: magenta; font-weight: 600;">return</span> closure

The system leverages several key insights:

• Modular boundaries exist: Even in legacy codebases, there are natural boundaries
• Changes are incremental: We don't convert 50,000 files atomically
• Dependencies are sparse: Most modules depend on a small fraction of the codebase
• Interfaces are narrow: Public APIs are much smaller than implementations

Strategize Data Model

A plan in Strategize contains all Action fields plus:

1) projects

A list of projects the plan is used on.

Important: A strategy plan may target multiple projects. Multi-project work in one "window" is considered a major usability advantage over tools that require being run from a single directory.

2) strategy_context

A structured object describing:

• what resources were considered,
• how they were retrieved,
• what filtering/limits were applied,
• what the actor saw.

This matters because a plan must be debuggable and correctable later.

Recommended fields:

• resource_refs: IDs of resources used
• queries: search queries performed
• selected_chunks: chunk IDs + sources + reasons
• constraints: context window limits, file ignore patterns
• generated_summaries: if summarization occurred

3) strategy

The output plan:

• steps (ordered and/or DAG),
• conditions/branches ("if tests fail, do X"),
• child plans to spawn (including which action templates to use, and whether they should run in parallel via subplan_parallel_spawn),
• evaluation criteria (how to know success),
• risk assessment.

4) execution_blueprint (recommended addition)

Strategize should output not only narrative text but also a machine-usable blueprint:

• list of tasks,
• required skills,
• expected outputs,
• dependencies between tasks.

This blueprint becomes the input to Execute.

5) cost_estimate and risk_estimate (optional)

Cost and risk estimation is optional but recommended for production use.

When enabled, a specialized estimation actor analyzes:

• The initial prompt/request
• The strategy produced by the Strategize phase
• Historical data from similar plans (if available)

And produces estimates for:

• LLM tokens/cost range
• Number of steps/child plans expected
• Expected risk of rollbacks
• Estimated execution time

Implementation: Similar to how there's a strategy_actor and execution_actor for each action, there can be an optional estimation_actor whose entire job is cost/risk estimation. This actor runs after Strategize completes (before Execute) and its output is informational only.

# Example: Action with estimation actor (estimation-actor overrides config)
agents action create --config ./actions/expensive-refactor.yaml \
  --estimation-actor "local/cost-estimator" \
  "local/expensive-refactor"

This becomes critical in server/multi-user usage and cost controls.

Execution (Execute Phase)

What Execute Does

Execute is where the plan actually performs work, but in a sandboxed environment that can later be reviewed and applied.

Key properties:

1. Work happens in a sandbox All file modifications, generated artifacts, and intermediate outputs live in an isolated "execution workspace" until Apply.

2. Execute may spawn child plans Child plan spawning is a first-class behavior of Execute: a parent plan can distribute work to child plans (sequentially via subplan_spawn or concurrently via subplan_parallel_spawn) and merge results.

3. Execute must support checkpointing / rollback (when enabled) Checkpointable tools allow rolling back to a checkpoint ID to recover from partial failure or wrong turns.

4. Execute produces a "reviewable diff" Diff review sandbox is described as a differentiating feature: users can inspect changes before applying.

Execution Workspace / Sandbox Model (Detailed)

A sandbox isolates plan execution from the real project resources until Apply.

Key Sandbox Principles

1. Lazy Sandboxing: Resources are sandboxed only when accessed, not upfront.

   • A project may have many resources (git repo + 10 databases + cloud accounts)
   • A plan may only modify one resource
   • Only accessed resources are sandboxed
   • Efficient for large projects

2. Per-Plan Sandboxes: Each plan and child plan has its own sandbox containing only the resources it edits.

3. Resource-Defined Strategy: The sandbox strategy is defined on each resource, not by skills or globally.

4. Cleanup Behavior:

   • Sandboxes are cleaned up before application exit when possible
   • Abandoned sandboxes (from crashes, etc.) are cleaned up on next application run
   • Completed plan sandboxes are cleaned or archived based on retention policy

Sandbox Implementation Strategies

Different resource types require different sandbox strategies:

Resource Type	Strategy	Rollback Mechanism
git-checkout	git_worktree	Git reset/checkout
git	none	N/A (represents a repo instance — not directly sandboxable)
fs-mount	copy_on_write or overlay	Restore from snapshot
fs-directory	copy_on_write	Restore from snapshot
Custom database types	transaction_rollback	Transaction rollback
Custom API types	none	Often not sandboxable

1. Git worktree / branch sandbox (preferred for code)

• Create a worktree or temporary branch
• All modifications are commits or staged changes
• Apply merges/cherry-picks

Pros: natural rollback, diff support, efficient Cons: requires git

2. Filesystem copy sandbox

• Copy project directory to a sandbox directory
• Execute modifies sandbox copy
• Apply syncs diff back

Pros: simple Cons: expensive for huge repos

3. Overlay filesystem sandbox

• Use overlayfs-style "copy-on-write" to avoid full copies

Pros: efficient Cons: more complex, OS-dependent

4. Transaction-based sandbox (for databases)

• Begin transaction at sandbox creation
• All operations within transaction
• Rollback on failure, commit on apply

Pros: native to databases Cons: long-running transactions can cause issues

5. No sandbox (for non-sandboxable resources)

• Some resources cannot be sandboxed (certain APIs, cloud services)
• User proceeds at their own risk
• Plan should warn about non-sandboxable resources

Multi-Resource Sandboxing

When a plan accesses multiple resources:

• Each resource gets its own sandbox (based on its defined strategy)
• Sandboxes are independent
• Apply commits each sandbox separately
• If any sandbox Apply fails, others may still succeed (partial apply)

Complete isolation during execution prevents compound errors: Each plan executes in its own sandbox, which means:

Plan A (refactoring auth module):
- Sandbox A1: Contains only auth/*.cpp, auth_tests/*.cpp
- Cannot see Plan B's intermediate states
- Cannot accidentally depend on Plan B's half-done work
Plan B (updating API endpoints):

Sandbox B1: Contains only api/.cpp, api_tests/.cpp
Makes changes assuming current auth interface
Protected from Plan A's intermediate refactoring

Hierarchical merge resolution: When child plans complete, the parent plan performs intelligent merging:

def merge_subplan_results(subplan_results):
    # Group by resource type
    by_resource = group_by_resource_type(subplan_results)
<span style="opacity: 0.7;"># Apply resource-specific merge strategies</span>
<span style="color: magenta; font-weight: 600;">for</span> resource_type, changes <span style="color: magenta; font-weight: 600;">in</span> by_resource:
    <span style="color: magenta; font-weight: 600;">if</span> resource_type == <span style="color: #66cc66;">&#x27;git-checkout&#x27;</span>:
        merge_git_changes(changes)  <span style="opacity: 0.7;"># Three-way merge</span>
    <span style="color: magenta; font-weight: 600;">elif</span> resource_type == <span style="color: #66cc66;">&#x27;fs-mount&#x27;</span>:
        merge_fs_changes(changes)   <span style="opacity: 0.7;"># Copy-on-write reconciliation</span>
    <span style="color: magenta; font-weight: 600;">elif</span> resource_type.startswith(<span style="color: #66cc66;">&#x27;database&#x27;</span>):
        merge_db_changes(changes)   <span style="opacity: 0.7;"># Sequential application</span>

<span style="opacity: 0.7;"># Validate merged state</span>
run_integration_tests()

Execution Data Model

A plan in Execute contains:

1) execution_context

The context used for execution (often smaller/more tactical than strategy context).

2) execution_log

Structured timeline of:

• tool calls (with parent skill noted),
• actor calls,
• outputs,
• errors and retries,
• checkpoints created.

This log is essential for debugging.

3) artifacts

Outputs produced:

• changed files,
• generated files,
• reports,
• diagrams,
• test outputs,
• diffs.

4) sandbox_ref

Pointer to the sandbox location/state:

• path, branch name, workspace ID, container ID, etc.

5) checkpoint_graph (if enabled)

A record of checkpoints:

• checkpoint ID
• timestamp
• tool responsible (and its parent skill)
• resources affected
• rollback instructions / metadata

Checkpointing in Execute (Core Safety Mechanism)

The intended user-level behavior is:

• "Give me a checkpoint ID."
• Perform additional operations.
• "Roll back to checkpoint X."

Not all tools can support this; checkpointing must be declared per tool (in its capability metadata).

Tool-level checkpointability

Each tool declares (via its capability metadata):

• checkpointable: true|false
• checkpoint_scope: what granularity of rollback is supported (file, transaction, commit, snapshot)
• rollback_mechanism: how rollback occurs

Examples:

• File tools (from a file-ops skill): snapshot file states pre-modification
• Git tools (from a git-ops skill): create commit or stash; rollback is reset/checkout
• Shell/CLI tools (running inside a container): rollback by restoring filesystem snapshot or reloading base image state

It should be noted that checkpointing is easier when tool scope is constrained (e.g., "only files within a docker image + git").

Plan-level rollback policy

Plans should have an option:

• rollback_enabled: true|false

If disabled, the plan may use more generic/unsafe tools with fewer restrictions (useful for low-stakes tasks).

Execution as Transactions (Recommended)

Execution should be treated like a transactional pipeline:

• Each step either:

  • commits a checkpoint on success, or
  • rolls back to the previous checkpoint on failure.

This is explicitly motivated by "partial failure leaves codebase inconsistent" and the need for transaction rollback.

Tool-Based Resource Modification (Modern Architecture)

IMPORTANT: CleverAgents does NOT parse LLM output to extract code. Instead, it uses the modern tool-based approach pioneered by Claude Code, Cursor, and Aider where:

1. LLMs call tools directly (edit_file(), write_file(), delete_file(), etc.) — tools provided by referenced skills
2. Tools operate on the sandbox - each tool invocation modifies sandbox state directly
3. ChangeSet is built from tool invocations - not by parsing LLM text output
4. Validation runs on sandbox state - after tools execute, not on parsed output

This architecture provides:

• Atomic operations: Each tool call is a discrete, trackable change
• No parsing ambiguity: Tools have structured parameters (path, content, etc.)
• Resource-agnostic: Same pattern works for files, databases, APIs, any resource type
• Safety by design: Tools run in sandbox with defined capabilities and restrictions
• MCP compatibility: Tools from MCP-based skills map directly to MCP tools for external integrations

How It Works

sequenceDiagram
    participant LLM as LLM Agent
    participant Router as Tool Router
    participant Sandbox as Sandbox
    participant CS as ChangeSet
    participant Val as Validator

    LLM->>Router: Tool call (with parameters)
    Router->>Router: Validate parameters
    Router->>Router: Enforce capability restrictions
    Router->>Sandbox: Execute tool in sandbox
    Sandbox->>Sandbox: Operate on sandboxed state
    Sandbox->>Sandbox: Record invocation
    Sandbox->>Sandbox: Create checkpoint (if needed)
    Sandbox->>CS: Emit Change record
    CS->>CS: Accumulate into ChangeSet
    CS->>Val: Submit for validation
    Val->>Val: Run validators on sandbox state
    Val->>Val: Generate diff from ChangeSet
    Val-->>LLM: Present for review before Apply

Built-in Resource Tools

CleverAgents provides these core tools (via built-in skills) for resource manipulation:

Tool	Description	Creates Change?
read_file(path)	Read file contents	No
write_file(path, content)	Create/overwrite file	Yes
edit_file(path, changes)	Apply targeted edits	Yes
delete_file(path)	Remove file	Yes
move_file(src, dst)	Rename/move file	Yes
create_directory(path)	Create directory	Yes
list_files(pattern)	List files matching glob	No
search_files(pattern, content)	Search file contents	No
get_file_info(path)	Get file metadata	No

Each built-in tool automatically:

• Operates within sandbox boundaries
• Records changes to the ChangeSet
• Validates parameters against project configuration
• Enforces deny-list patterns (.git/, node_modules/, etc.)

Why Not Parse LLM Output?

The obsolete approach of parsing markdown code fences has fundamental problems:

1. Ambiguity: Is text explanation or code? Where does one file end and another begin?
2. Fragility: Models output varying formats; regex parsing is brittle
3. Loss of semantics: You lose the intent (create vs modify vs delete)
4. No atomicity: Can't rollback individual operations
5. Resource-limited: Only works for files, not databases or other resources

The tool-based approach solves all of these by making each operation explicit, typed, and trackable.

Semantic Error Prevention

CleverAgents provides multiple layers of proactive error prevention that catch semantic errors before they can propagate through the system.

Layer 1: Decision-time Validation During Strategize

Every decision includes semantic validation:

Decision: Refactor payment module to async
alternatives_considered:
  - "Convert to async/await patterns" (chosen)
  - "Use thread pool with channels" (rejected: doesn't integrate with async ecosystem)
  - "Keep synchronous with timeout" (rejected: doesn't solve core latency issue)
confidence_score: 0.85
validation_performed:
  - Checked all payment API consumers can handle async
  - Verified database driver supports async operations
  - Confirmed no regulatory requirement for sync processing

Layer 2: Execution-time Semantic Guards

The execution actor uses a tool node that references the independently registered local/validate-api-compat tool:

# Actor graph uses a named tool node for semantic validation
actors:
  code_executor:
    type: graph
    skills:
      - local/semantic-validators    # Skill containing validation tools for LLM tool-calling
    nodes:
      - name: semantic_validator
        type: tool
        tool: local/validate-api-compat # Named tool from Tool Registry

The local/validate-api-compat tool is independently registered via its own YAML:

# File: tools/validate-api-compat.yaml
cleveragents:
  version: "3.0"
tool:
name: local/validate-api-compat
description: "Check for breaking API changes and attempt auto-migration"
source: custom
capability:
writes: true
checkpointable: true
code: |
    # Not just syntax checking - semantic validation
old_api = extract_api_signature(previous_version)
new_api = extract_api_signature(current_version)
breaking_changes = find_breaking_changes(old_api, new_api)
if breaking_changes:
    affected_consumers = find_api_consumers(breaking_changes)
    migration_plan = generate_migration(breaking_changes)
    
    if can_auto_migrate(affected_consumers, migration_plan):
        apply_migration(migration_plan)
    <span style="color: cyan; font-weight: 600;">else</span>:
        raise SemanticError(
            &quot;Breaking API changes require manual review&quot;,
            changes=breaking_changes,
            affected=affected_consumers
        )

The local/semantic-validators skill references this tool by name:

# File: skills/semantic-validators.yaml
skill:
  name: local/semantic-validators
  description: "Semantic validation tools for API compatibility and code invariants"
  tools:
    - local/validate-api-compat

Layer 3: Invariant Enforcement

Invariants are named constraints that guide and constrain plan execution. They can be attached at four scopes, all managed through the unified agents invariant command:

• Global invariants: Apply to all plans across all projects. Added via agents invariant add --global.
• Project invariants: Apply to all plans targeting a specific project. Added via agents invariant add --project. Can also be attached at creation time via agents project create --invariant.
• Action invariants: Attached to an action and carried forward as plan-level invariants when the action is used. Added via agents invariant add --action. Can also be attached at creation time via agents action create --invariant.
• Plan invariants: Apply to a specific plan and its child plans. Added via agents invariant add --plan. Can also be attached at creation time via agents plan use --invariant.

Precedence and conflict resolution: When invariants from different scopes conflict, narrower scopes override broader scopes:

• Plan-level invariants override project-level and global-level invariants.
• Project-level invariants override global-level invariants.

Conflict resolution is performed by the Invariant Reconciliation Actor — a dedicated actor responsible for comparing invariants across scopes, identifying conflicts, and producing the final effective invariant view for a plan. The Invariant Reconciliation Actor is set at three levels via --invariant-actor:

1. Global config: Set via agents config set invariant-actor <ACTOR>. Defines the default Invariant Reconciliation Actor used when neither the project nor the plan specifies one.
2. Project-level: Set via --invariant-actor on agents project create. If a project defines an Invariant Reconciliation Actor, it is used to reconcile that project's invariants against global invariants.
3. Plan-level: Set via --invariant-actor on agents action create (carried forward when the action is used) or agents plan use (which overrides whatever was set on the action). If a plan has an Invariant Reconciliation Actor, it reconciles invariants from all scopes (plan, project, and global) and produces the final effective view for that plan.

The lookup order is: plan → project → global config. The first Invariant Reconciliation Actor found is used.

Invariant view calculation: When an action is used and a plan enters the Strategize phase, the Invariant Reconciliation Actor computes the effective invariant view by:

1. Collecting all invariants from global, project, plan, and action scopes.
2. Identifying conflicts (invariants from different scopes that contradict each other).
3. Applying precedence rules (plan > project > global) to resolve conflicts.
4. Producing the final set of effective invariants.

Each effective invariant is then recorded as an invariant_enforced decision in the plan's decision tree. This makes invariants visible, auditable, and correctable through the standard decision correction mechanism.

Child plan inheritance: When a top-level plan spawns child plans, the parent's effective invariant view (already reconciled) is passed down to each child plan. Child plans do not re-run reconciliation — they inherit the parent's resolved view.

Correcting invariants: The correction mechanism for invariant_enforced decisions supports two operations:

• Remove: Remove an existing invariant from the plan's decision tree (the invariant remains defined at its scope but is no longer enforced for this plan).
• Add: Add a new invariant to the plan. When adding, the user can select from invariants already accessible to the plan (those defined at the plan, action, project, or global scope), or provide free-form text to create a new ad-hoc invariant.

# Add invariants at different scopes
agents invariant add --global "All public APIs must maintain backward compatibility"
agents invariant add --global "Payment processing must be idempotent"
agents invariant add --project local/api-service \
  "Database transactions must complete within 5 seconds"
agents invariant add --project local/api-service \
  "Authentication must always use OAuth2"
agents invariant add --plan <PLAN_ID> "All API calls over TCP must be mocked"
agents invariant add --action local/code-coverage "Test files must not import production secrets"
# List invariants
agents invariant list --global
agents invariant list --project local/api-service
agents invariant list --plan <PLAN_ID> --effective   # Shows reconciled view
# Remove an invariant
agents invariant remove <INVARIANT_ID>
# Attach invariants at creation time (convenience)
agents project create --invariant "All endpoints must validate auth tokens" local/api-service
agents action create --config ./actions/code-coverage.yaml --invariant "Test files must not import production secrets" local/code-coverage
agents plan use local/code-coverage local/api-service --invariant "Mock all network calls"
# Correct an invariant decision (remove or replace via standard correction)
agents plan correct <DECISION_ID> --mode=revert </span>
--guidance "Remove this invariant - it does not apply to this module"

The system collects, reconciles, and checks invariants:

class InvariantEnforcer:
    def compute_effective_invariants(self, plan):
        """Compute the effective invariant view for a plan using the Invariant Reconciliation Actor."""
        # 1. Collect raw invariants from all scopes
        raw = self.collect_all_invariants(plan)
    <span style="opacity: 0.7;"># 2. Find the Invariant Reconciliation Actor (plan -&gt; project -&gt; global config)</span>
    reconciler = (
        self.get_plan_invariant_actor(plan)
        <span style="color: magenta; font-weight: 600;">or</span> self.get_project_invariant_actor(plan)
        <span style="color: magenta; font-weight: 600;">or</span> self.get_global_invariant_actor()
    )
    
    <span style="opacity: 0.7;"># 3. Reconcile: apply precedence (plan &gt; project &gt; global), resolve conflicts</span>
    effective = reconciler.reconcile(raw, precedence=[<span style="color: #66cc66;">&#x27;plan&#x27;</span>, <span style="color: #66cc66;">&#x27;project&#x27;</span>, <span style="color: #66cc66;">&#x27;global&#x27;</span>])
    <span style="color: magenta; font-weight: 600;">return</span> effective

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">collect_all_invariants</span>(self, plan):
    <span style="color: #66cc66;">&quot;&quot;&quot;Collect invariants from all scopes accessible to this plan.&quot;&quot;&quot;</span>
    invariants = []
    invariants.extend(self.get_global_invariants())
    <span style="color: magenta; font-weight: 600;">for</span> project <span style="color: magenta; font-weight: 600;">in</span> plan.projects:
        invariants.extend(self.get_project_invariants(project))
    invariants.extend(self.get_action_invariants(plan.action))
    invariants.extend(self.get_plan_invariants(plan))
    <span style="color: magenta; font-weight: 600;">return</span> invariants

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">check_invariant_preservation</span>(self, changes, enforced_invariants):
    <span style="color: #66cc66;">&quot;&quot;&quot;Check that changes respect all enforced invariants.&quot;&quot;&quot;</span>
    <span style="color: magenta; font-weight: 600;">for</span> invariant <span style="color: magenta; font-weight: 600;">in</span> enforced_invariants:
        <span style="color: magenta; font-weight: 600;">if</span> <span style="color: magenta; font-weight: 600;">not</span> self.verify_invariant(invariant, changes):
            <span style="color: magenta; font-weight: 600;">return</span> InvariantViolation(invariant, changes)
    <span style="color: magenta; font-weight: 600;">return</span> Success()

Layer 4: Predictive Error Prevention

The system learns from past failures:

Error Pattern Database:
  - pattern: "Async conversion in payment module"
    historical_failures:
      - "Race condition in payment confirmation"
      - "Timeout handling breaks idempotency"
    preventive_checks:
      - "Add explicit transaction boundaries"
      - "Verify idempotency keys are preserved"
      - "Check distributed lock acquisition"

Applied (Apply Phase)

What Apply Does

Apply takes the sandboxed work product and makes it "real" in the project.

Core properties:

1. Apply is a controlled commit step Apply exists specifically to separate "generated work" from "committed work," enabling review and safer automation.

2. Apply is often the highest-risk step It changes real systems. This is where permissions, approvals, and checks matter most.

3. Apply produces a terminal 'applied' plan After successful apply, the plan becomes Applied.

Apply Responsibilities (Recommended Checklist)

Apply should perform (configurable) validations before committing:

• Diff review gate

  • If auto_apply is false in the automation profile, show:

    • changed files summary,
    • full diff,
    • risk warnings.

• Pre-apply tests

  • Run validation as defined by the actor and/or project (see below).

• Conflict resolution

  • If applying to a git repo, handle rebase/merge conflicts safely.

• Audit log

  • Record who applied, what changed, when, and why.

Validation Configuration

Validation is defined by the actor configuration and project settings, not hardcoded.

Actor-defined validation: The execution actor's workflow should include validation nodes that:

1. Read project-defined validations from context
2. Execute each validation command and collect results
3. For required validations that fail: attempt to fix the issue within the bounds of the strategy, retrying up to the configured limit. If the actor determines it cannot solve the problem within the restrictions given by the strategy phase, it may suggest a change to the decision tree and cause the strategy to be recalculated (whether this happens automatically depends on the automation profile).
4. For informational validations that fail: record the result in the plan summary without blocking execution.

Project-defined validation: Each project defines its validations via agents project validation add:

# Add required validations
agents project validation add --description "Run unit tests with coverage" \
  --required --resource repo local/api-service "pytest --cov=src --cov-fail-under=80"
agents project validation add --description "Lint check" \
  --required local/api-service "ruff check ."
agents project validation add --description "Type checking" \
  --required local/api-service "pyright"
# Add informational (non-blocking) validations
agents project validation add --description "Check bundle size (advisory)" </span>
--informational local/api-service "node scripts/check-bundle-size.js"

This information is passed into the actor's context, allowing generic actors (not specific to any project) to execute appropriate validation. Each validation is run independently, and results are collected into the plan's validation summary.

Validation Failure Handling

When a required validation fails during Execute:

1. Self-fix: The execution actor attempts to fix the issue within the bounds of the strategy (e.g., fix failing tests, correct lint errors). The actor iterates, re-running the validation after each fix attempt.
2. Retry limit: After the configured number of failed attempts, self-fix stops.
3. Strategy recalculation: If the actor determines it cannot solve the problem within the restrictions given by the strategy phase, it may request a change to the decision tree. This causes the Strategize phase to be re-run for the affected subtree. Whether this happens automatically or requires user approval depends on the automation profile (the auto_strategy_revision flag).
4. User intervention: If the automation profile requires approval, or if strategy recalculation also fails, the system pauses and requests user guidance.
5. Resume: Plan continues with new guidance after user input.

When an informational validation fails, the result is recorded in the plan's validation summary but execution continues normally.

The user can prompt the plan with additional instructions when stuck:

agents plan prompt <plan_id> "Try using mock objects for the database tests"

Apply Data Model

A plan in Apply includes:

• apply_summary
• applied_artifacts (final commit hash, merged PR link, file list)
• final_validation_results (per-validation command outputs and pass/fail status)
• approval_record (if human approvals are required)
• deployment_record (optional, if apply triggers deploy)

"Applied" Terminal State

When Apply succeeds:

• plan.phase = applied
• plan.state = complete
• the sandbox may be cleaned up or archived depending on retention policy

When Apply fails:

• plan.phase remains Apply
• plan.state = errored
• sandbox remains intact for inspection/retry

Project

A project is the boundary that answers:

• "Where is the work happening?"
• "What can this plan read and write?"
• "What skills (and their tools) can this plan use?"
• "What context is available?"

A project is a collection of linked resources and configuration. Projects link to independently registered resources from the Resource Registry — they do not define resources inline. A resource can be linked to multiple projects, enabling shared resources across teams and workflows.

Important: Projects are created via CLI commands, NOT YAML configuration files.

Project Types: Local vs Remote

Projects are classified based on their resources:

Type	Definition	Where Plans Can Execute
Local	Contains at least one local-only resource	Client only
Remote	All resources are remotely accessible	Client or Server

This distinction matters for server mode: the server can only execute plans on remote projects because it needs network access to all resources.

Project Creation (CLI)

# Step 1: Register resources independently
agents resource add git-checkout local/api-repo \
  --path /repos/api-service \
  --branch main
agents resource add local/database local/staging-db </span>
--connection-string "postgresql://staging.example.com/mydb" </span>
--read-only
# Step 2: Create the project
agents project create "my-api-service"
# Step 3: Link resources to the project
agents project link-resource "my-api-service" local/api-repo
agents project link-resource "my-api-service" local/staging-db --read-only

Resources are registered once and can be linked to multiple projects. The resource's type, sandbox strategy, and capabilities are defined by its resource type in the Resource Registry — not by the project.

Project Data Model

A project includes:

1) Identity

• project_id (ULID)
• name
• namespace (follows same rules as actors: local/, <username>/, <orgname>/)
• is_remote (boolean, derived from resources)

2) Linked Resources

Resources are the "things you can act on." Projects link to independently registered resources from the Resource Registry rather than defining them inline. This means:

• A resource can be linked to multiple projects (shared resources).
• The resource's type, capabilities, sandbox strategy, and DAG relationships are defined by the resource itself — not by the project.
• Projects can apply project-level overrides when linking (e.g., marking a writable resource as read-only within a specific project context).

Each linked resource reference has:

• resource_name (reference to a Resource Registry entry)
• project_read_only (boolean — project-level read-only override)
• alias (optional short name for referencing within the project)

The full resource details (type, location, sandbox strategy, capabilities, parent/child DAG, etc.) are stored in the Resource Registry. See the Resources section for details.

3) Context configuration

Project-level defaults:

• ignore patterns (like .gitignore semantics)
• max file size
• indexing strategy
• preferred chunking/summarization policy (even if evolving)
• context retention policy

4) Security / permissions defaults

• who can run write plans
• which skill categories are allowed or denied
• whether apply requires approvals

Multi-Project Operations

A single plan may target multiple projects (e.g., updating shared schemas across services). This is considered a key UX advantage over "run in one directory" systems. Because resources are independently registered and can be linked to multiple projects, shared resources across projects are a natural part of the architecture.

In multi-project execution:

• Strategize must clarify which steps affect which projects.
• Execution must isolate sandboxes per project OR define a composite sandbox. When multiple projects share the same resource, a single sandbox for that resource is used.
• Apply must commit changes to each project separately, with separate approval records if necessary.
• Tool resource bindings are resolved per-project — the same tool may bind to different resources depending on which project context it runs in.

Namespaces

Namespaces define ownership, scoping, and discoverability of actors, tools, skills, resources, resource types, actions, projects, and plans.

All named entities use the format <namespace>/<name>.

Namespace Types

Namespace	Scope	Storage	Examples
local/	Current machine only	Local database	local/my-reviewer, local/test-action
<username>/	Personal server namespace	Server database	freemo/code-analyzer, jsmith/deploy-script
<orgname>/	Organization namespace	Server database	cleverthis/standard-review, acme/deploy-action
openai/, anthropic/, etc.	Built-in LLM actors	N/A (built-in)	openai/gpt-4, anthropic/claude-3-opus

Namespace Rules

• local/

  • Reserved namespace for local-only items
  • Exists only on the current machine
  • Stored in local database
  • Fast iteration, no sharing
  • Default namespace when none specified

• <username>/ (e.g., freemo/, jsmith/)

  • Personal namespace on the server
  • Created when user registers an account
  • Stored on server, synced when connected
  • Used for reusable entities (actions, actors, tools, skills, resources) a user wants across machines
  • Only the owning user can create/modify items

• <orgname>/ (e.g., cleverthis/, acme/)

  • Organization namespace on the server
  • Created when organization is registered
  • Shared across team members
  • Permissions and approvals managed at org level
  • All namespaced entities (actions, actors, tools, skills, resources, projects) can be centrally managed

• Built-in Provider Namespaces (openai/, anthropic/, google/, etc.)

  • Reserved for built-in LLM actors
  • Automatically available when API keys are configured
  • In server mode: available if logged in and server has keys
  • In local mode: requires environment variables or app configuration
  • Cannot be used for custom actors

Server-qualified Names

To disambiguate between servers (when connected to multiple):

• dev:freemo/code-coverage (personal namespace on dev server)
• prod:cleverthis/deploy-action (org namespace on prod server)

This enables a pattern where:

• local machine runs a lightweight client
• server stores canonical definitions
• multiple servers can coexist

Actor

What an Actor Is

An actor is the abstraction that generalizes "agent" into "anything conversational."

• It can be as small as a single LLM agent.
• It can also be an entire graph that itself calls other actors/tools.
• Actors can be nested/hierarchical, enabling "orchestrator of orchestrators."

Every custom actor IS a graph (a LangGraph defined via YAML configuration). Even a simple actor wrapping a single LLM is technically a graph with one node.

Actor Naming

Actors are always named using <namespace>/<name> format:

• local/my-reviewer - Local actor
• freemo/code-analyzer - Personal server actor
• cleverthis/deploy-specialist - Organization actor
• openai/gpt-4 - Built-in LLM actor

Actor Definition (YAML Configuration)

Actors are defined via YAML configuration files. Tools and skills are also defined via their own YAML configuration files. YAML configuration is used for actors, tools, and skills (not for actions or projects).

Example actor configuration (see examples/ directory for full examples):

cleveragents:
  version: "3.0"
  default_actor: workflow_controller
actors:
  # Simple LLM actor with skills referenced by name
my_assistant:
type: llm
config:
actor: openai/gpt-4 # Reference to built-in actor
temperature: 0.7
system_prompt: |
You are a helpful assistant.
Current task: {{ context.task_description }}
skills:
- local/file-ops               # Grants access to all tools in this skill
- local/git-ops
  # LLM actor with a composite skill (includes many sub-skills)
data_processor:
type: llm
config:
actor: anthropic/claude-3-opus
system_prompt: |
You are a data processing assistant.
skills:
- local/data-toolkit           # A skill containing analysis + transformation tools
  # Actor referencing another actor
reviewer:
type: llm
config:
actor: local/code-reviewer # Reference to another custom actor
memory_enabled: true
max_history: 20
skills:
- local/file-ops
- local/git-ops
routes:
main_workflow:
type: graph
entry_point: start
nodes:
- name: analyze
type: agent
agent: my_assistant
- name: process
type: agent
agent: data_processor
edges:
- source: start
target: analyze
- source: analyze
target: process
- source: process
target: end
context:
global:
task_description: "Default task"

Actor Arguments

All actors can receive arguments when invoked, including built-in actors. Arguments are passed when:

1. An action is used on projects (arguments flow to strategy/execution actors)
2. An actor is directly invoked

Arguments are injected into the actor's context and can be used in Jinja2 templates within prompts.

For built-in actors (like openai/gpt-4), common arguments include:

• temperature
• max_tokens
• system_prompt

Actor Composition (Hierarchical References)

Actors can reference other actors by name:

actors:
  complex_workflow:
    type: llm
    config:
      actor: local/base-analyzer # References another actor

Load order matters: Referenced actors must be loaded/defined before actors that depend on them.

This enables hierarchical composition where:

• Actor A's graph can include nodes that call Actor B (by name)
• Actor B itself is a graph that might call Actor C
• And so on...

Actor vs Agent (Relationship)

• Agent: an actor that is specifically an LLM with tools and reasoning behaviors.

• Actor: may be an agent, but may also be:

  • a composite workflow,
  • a multi-step graph,
  • a wrapper around a third-party system (as long as it's "text in → text out" conversationally).

Actor Definition Fields (From Notes + Extended)

A robust actor schema should include:

• name (namespaced)
• provider (LLM provider or runtime target)
• model
• system_prompt (or prompt template)
• skill_access_policy (which skill categories/names are allowed or denied)
• graph_descriptor (for composite actors)
• memory_policy (per-plan/per-actor—see memory section)
• context_view_policy (what context this actor sees)
• limits (token limits, tool call limits, retries)
• cost_policy (caps, budgets)
• metadata (use cases, version)

Actor Composition and Graphs

Actors can reference:

• other actors (by namespaced name)
• skills (by namespaced name — all tools from referenced skills become available)
• subgraphs

This is central to enabling both:

• multi-agent orchestration, and
• modular reuse of workflows.

Nodes in the Graph: Actors and Tools

Graph nodes can be any of:

• an actor (another LLM agent or composite workflow, referenced by name),
• a tool node — a deterministic, non-LLM step that directly invokes a tool. Tool nodes can:
  • Reference a named registered tool by its fully-qualified name (e.g., tool: local/run-migrations). The tool must be registered in the Tool Registry via agents tool add. Metadata can optionally be overridden at the point of use.
  • Define an anonymous inline tool using the same format as a tool YAML body (with anonymous: true). This is useful for one-off, workflow-specific operations that don't warrant separate registration.

This is a powerful simplification: actors provide intelligence, tools provide capability (both as graph nodes and through skills for LLM tool-calling), and skills organize tools into reusable collections. Everything participates in the same graph.

Tool node with named tool reference:

nodes:
  - name: run_db_migrate
    type: tool
    tool: local/run-migrations # Named tool from Tool Registry
    override:                           # Optional metadata override
      capability:
        human_approval_required: true

name: spawn_tests
type: tool
tool: local/create-subplan # Another named tool

Tool node with anonymous inline tool:

nodes:
  - name: custom_validation
    type: tool
    anonymous: true
    description: "Validate output format before proceeding"
    input_schema:
      type: object
      properties:
        data: { type: object }
    capability:
      read_only: true
    code: |
      # Inline Python — same format as a named tool YAML body
      if not params["data"].get("status"):
          raise ValueError("Missing status field")
      return {"valid": True}

Agent

Agent Definition

In CleverAgents, an agent is a specialized actor with:

• a conversational interface,
• tool-calling capability,
• potentially memory, planning heuristics, and role identity.

Examples of agent roles:

• planner/architect (strategy actor)
• coder/implementer (execution actor)
• reviewer/qa agent
• release/apply agent

The transcript explicitly discusses role separation like planner/coder/reviewer in context views/memory proposals.

Agent Behavior Configuration

Agents should be configurable without code changes:

• prompt templates
• tool sets
• safety constraints
• style constraints (verbosity, code style)
• reliability controls (self-checks, validations)

A design goal is user empowerment: "users customize LLM behavior without modifying core code."

Tools

What a Tool Is

A tool is a namespaced, independently registered, callable operation. It is the atomic unit of execution in CleverAgents — the smallest piece of functionality that can read, write, or transform resources. Tools are defined in their own YAML configuration files, managed through the agents tool CLI commands, and registered in the Tool Registry.

Tools follow the same <namespace>/<name> naming convention as actors, skills, and other entities (e.g., local/run-migrations, cleverthis/validate-api, local/create-subplan). They support optional server-qualified prefixes for multi-server disambiguation (e.g., dev:freemo/custom-analysis).

The Dual Role of Tools

Tools serve two distinct roles in CleverAgents:

1. As components of a Skill: A skill references tools by name to assemble a reusable capability collection. When an actor references a skill, all of that skill's tools (including those from included child skills) become available to the actor's LLM agent for tool-calling.

2. As tool nodes in an Actor graph: An actor's graph definition can include type: tool nodes that directly invoke a specific tool. This is used for deterministic, non-LLM steps in a workflow — e.g., spawning a child plan, running validation, or executing a migration. The tool node either references a named registered tool or defines an anonymous inline tool.

block-beta
    columns 3
    space:3
    block:header:3
        A["Tool: Dual Role"]
    end
    space:3
    block:role1:1
        B["Role 1: In a Skill"]
        C["Skill: local/devops"]
        D["tools:"]
        E["  - local/run-migrations"]
        F["  - local/validate-schema"]
        G["(tool-calling by LLM)"]
    end
    space:1
    block:role2:1
        H["Role 2: In an Actor Graph"]
        I["Actor Graph node:"]
        J["  name: run_db"]
        K["  type: tool"]
        L["  tool: local/run-migrations"]
        M["(deterministic invoke)"]
    end

Tool Configuration (YAML)

Tools are defined in their own YAML configuration files, separate from skills and actors. A tool YAML file declares the tool's identity, schema, capability metadata, and implementation:

# File: tools/run-migrations.yaml
cleveragents:
  version: "3.0"
tool:
name: local/run-migrations
description: "Run database migrations for the API service"
source: custom # mcp | agent_skill | builtin | custom
  # Resource bindings — what resources this tool needs access to
resources:
db:
type: local/database
access: read_write
required: true
description: "Target database for migrations"
input_schema:
type: object
properties:
direction:
type: string
enum: [up, down]
count:
type: integer
default: 1
required: [direction]
capability:
writes: true
write_scope:
resource_slots: [db] # References the "db" resource slot
checkpointable: true
checkpoint_scope: transaction
side_effects: [schema_mutation]
code: |
import subprocess
direction = params["direction"]
count = params.get("count", 1)
db = ctx.resources["db"]          # Access the bound database resource
result = subprocess.run(
["alembic", direction, str(count)],
capture_output=True, text=True, cwd=db.sandbox.root
)
return {"stdout": result.stdout, "returncode": result.returncode}

Another example — a tool that wraps an MCP server endpoint:

# File: tools/create-github-issue.yaml
cleveragents:
  version: "3.0"
tool:
name: local/create-github-issue
description: "Create a GitHub issue via MCP"
source: mcp
mcp_server:
command: "npx @anthropic/mcp-github"
env:
GITHUB_TOKEN: "${GITHUB_TOKEN}"
tool_name: create_issue # The tool name as exposed by the MCP server
capability:
writes: true
write_scope: [github:issues]
checkpointable: false

And an Agent Skill tool:

# File: tools/deploy-staging.yaml
cleveragents:
  version: "3.0"
tool:
name: local/deploy-staging
description: "Deploy the current branch to the staging environment"
source: agent_skill
agent_skill:
path: ./skills/deploy-to-staging
sandbox_policy: container
allowed_tools: ["Bash(docker:)", "Bash(kubectl:)", "Read"]
capability:
writes: true
checkpointable: false
side_effects: [deploy, infrastructure]

Tool Registration and Management

Tools are managed through the agents tool CLI commands:

# Register a new tool from its YAML configuration
agents tool add --config ./tools/run-migrations.yaml local/run-migrations
# Update an existing tool (re-reads the config file, overwrites registration)
agents tool add --config ./tools/run-migrations.yaml --update local/run-migrations
# List all registered tools
agents tool list
# Show details for a tool (schema, capability, references)
agents tool show local/run-migrations
# Remove a tool
agents tool remove local/run-migrations

Once registered, a tool is available to be referenced by skills (in their tools list) and by actor graphs (as type: tool nodes). Tools persist in the database (local or server) and follow the same namespace rules as actors and skills.

Anonymous Tools

An anonymous tool is an inline tool definition that appears directly in a skill YAML or an actor graph node. Anonymous tools use the same format as a named tool's YAML definition (same input_schema, capability, and code fields) but lack a namespaced name. They are:

• Not registered in the Tool Registry
• Not reusable — they exist only within the YAML file where they are defined
• Useful for one-off operations that are too specific to warrant separate registration

Anonymous tools in a skill YAML:

skill:
  name: local/my-skill
  tools:
    - local/run-migrations          # Named tool reference
    - local/validate-api-compat     # Named tool reference
anonymous_tools:                  # Inline definitions, same format as tool YAML
- description: "One-off data cleanup for this project"
input_schema:
type: object
properties:
table: { type: string }
capability:
writes: true
checkpointable: true
code: |
        # ... Python code ...
return {"cleaned": count}

Anonymous tools in an actor graph node:

nodes:
  - name: custom_step
    type: tool
    anonymous: true
    description: "Inline validation specific to this workflow"
    input_schema:
      type: object
      properties:
        data: { type: object }
    capability:
      read_only: true
    code: |
      # ... Python code ...
      return {"valid": True}

The anonymous tool format is intentionally identical to the body of a named tool YAML — this means promoting an anonymous tool to a named, registered tool is a simple copy-paste into its own YAML file and agents tool add.

Metadata Overrides

When referencing a named tool in a skill or actor graph, its registered metadata can optionally be overridden at the point of use. This allows context-specific adjustments without modifying the tool's global registration.

Overriding tool metadata in a skill:

skill:
  name: local/strict-devops
  tools:
    - name: local/run-migrations
      override:
        capability:
          human_approval_required: true # Override: require approval in this skill
          write_scope: [database:staging] # Override: restrict scope for this context
- local/validate-api-compat            # No overrides, use as registered

Overriding tool metadata in an actor graph node:

nodes:
  - name: safe_migrate
    type: tool
    tool: local/run-migrations
    override:
      capability:
        human_approval_required: true

Overriding tool metadata when including a sub-skill:

When a skill includes another skill (importing all its tools), individual tools from the included skill can have their metadata overridden:

skill:
  name: local/production-ops
  includes:
    - name: local/devops-toolkit
      tool_overrides:
        - tool: local/run-migrations
          override:
            capability:
              human_approval_required: true # In this context, require human approval
              write_scope: [database:production]
    - <span style="color: cyan;">tool</span>: local/create-github-issue
      <span style="color: cyan; font-weight: 600;">override</span>:
        <span style="color: cyan; font-weight: 600;">capability</span>:
          <span style="color: cyan; font-weight: 600;">required_permissions</span>: [org:write] <span style="opacity: 0.7;"># Stricter permissions in this context</span>

Override rules:

1. Overrides are shallow-merged — only the specified fields are replaced; unspecified fields retain their registered values.
2. Overrides never persist back to the Tool Registry — they apply only at the point of use.
3. Built-in tool metadata cannot be overridden (it is authoritative from the implementation).
4. The override scope is limited to capability and description fields. Schema (input_schema, output_schema) cannot be overridden because it would break callers' expectations.

Resource Bindings

Tools operate on resources — git repositories, filesystems, databases, and more. The resource binding system declares and resolves the relationship between a tool and the resources it needs access to.

Resource Slots

A tool declares one or more resource slots in its YAML configuration. Each slot is a typed placeholder that specifies:

• Slot name: A logical name used to reference the resource within the tool's code and parameters.
• Resource type: The resource type required (e.g., git, fs-mount, local/database). The bound resource must be of this type (or a compatible subtype).
• Access mode: read_only, write_only, or read_write.
• Description: Human-readable explanation of what the tool uses this resource for.
• Required/optional: Whether the slot must be bound for the tool to function.

Example tool YAML with resource slots:

tool:
  name: local/run-migrations
  description: "Run database migrations"
  source: custom
resources:
db:
type: local/database
access: read_write
required: true
description: "Target database for migrations"
capability:
writes: true
write_scope: [db:migrations] # References the "db" slot
checkpointable: true
code: |
direction = params["direction"]
db_resource = ctx.resources["db"]   # Access the bound resource
result = db_resource.handler.execute_migration(direction, db_resource.sandbox)
return {"status": "ok"}

A tool that works with multiple resources:

tool:
  name: local/cross-repo-diff
  description: "Compare files across two git repositories"
  source: custom
resources:
source_repo:
type: git-checkout
access: read_only
required: true
description: "Source repository to compare from"
target_repo:
type: git-checkout
access: read_only
required: true
description: "Target repository to compare against"
capability:
read_only: true
code: |
source = ctx.resources["source_repo"]
target = ctx.resources["target_repo"]
    # ... compare files across repos ...

Three Binding Modes

Resource slots are resolved to actual resources through one of three binding modes:

1. Contextual Binding (default)

The slot declares a resource type requirement, and the system resolves it from the plan's project context at activation time. This is the most common mode — the tool says "I need a git-checkout resource" and the system finds one among the project's linked resources.

resources:
  repo:
    type: git-checkout
    access: read_write
    # No `bind` field → contextual binding

Resolution rules for contextual binding:

• The system searches the plan's project for linked resources matching the slot's type.
• If exactly one resource of the right type exists, it is automatically bound.
• If multiple resources match, the system uses the slot name as a hint (e.g., a slot named repo prefers a resource with alias repo). If ambiguous, the plan execution raises an error requiring explicit binding.
• If no resource matches, the tool cannot be activated for this plan (a validation error is raised during plan creation).

2. Static Binding

The slot is hardcoded to a specific registered resource by name. This is useful for tools that always operate on the same resource, regardless of project context.

resources:
  docs:
    type: fs-mount
    access: read_only
    bind: local/company-docs # Static: always this resource
    description: "Company documentation corpus"

Static bindings are resolved at registration time and validated — the named resource must exist and be of the correct type.

3. Parameter Binding

The resource reference is passed as a tool argument at invocation time. This is useful for tools that operate on user-specified resources.

resources:
  target:
    type: git-checkout
    access: read_only
    from_param: repository # Bound from the "repository" input parameter
    description: "Repository to analyze"
input_schema:
type: object
properties:
repository:
type: string
description: "Name of the registered resource to analyze"
required: [repository]

The from_param field links a resource slot to an input parameter. At invocation time, the system resolves the parameter value as a resource name from the Resource Registry and validates type compatibility.

Binding Resolution Flow

stateDiagram-v2
    [*] --> ToolActivation: Actor references skill or tool node

    state "For Each Resource Slot" as ForEach {
        state binding_check <<choice>>
        [*] --> binding_check: Check binding type

        binding_check --> StaticBinding: has bind field
        binding_check --> ContextualBinding: no bind, no from_param
        binding_check --> ParameterBinding: has from_param

        state "Static Binding" as StaticBinding {
            [*] --> ResolveByName: Resolve from Resource Registry
            ResolveByName --> ValidateType: Validate type compatibility
        }

        state "Contextual Binding" as ContextualBinding {
            [*] --> SearchProject: Search plan's project resources
            SearchProject --> FilterByType: Filter by resource type
            state match_check <<choice>>
            FilterByType --> match_check
            match_check --> AutoBind: One match
            match_check --> TryAlias: Multiple matches
            match_check --> ValidationError: No matches
            TryAlias --> AliasMatch: Try alias/name match
            AliasMatch --> AutoBind: Match found
            AliasMatch --> ValidationError: No match
        }

        state "Parameter Binding" as ParameterBinding {
            [*] --> DeferBinding: Defer to invocation time
        }

        StaticBinding --> StoreBindings
        AutoBind --> StoreBindings
        ParameterBinding --> StoreBindings
        state "Store in ToolActivationContext" as StoreBindings
    }

    state "Tool Invocation" as Invocation {
        [*] --> ResolveParams: Resolve parameter-bound slots\nfrom invocation params
        [*] --> ValidateAccess: Ensure sandbox exists\nValidate access mode
        ResolveParams --> Execute
        ValidateAccess --> Execute
        state "Execute tool with bound resources" as Execute
    }

    ForEach --> Invocation
    Invocation --> [*]

Built-in Tool Resource Bindings

Built-in tools have implicit resource slots that do not need to be declared in YAML (they are hardcoded in the implementation):

Built-in Tool Group	Implicit Slot	Slot Type	Access
file_operations (read_file, write_file, edit_file, etc.)	directory	fs-directory or git-checkout	read_write
directory_operations (create_directory, list_directory, etc.)	directory	fs-directory or git-checkout	read_write
search_operations (search_files, find_definition, etc.)	directory	fs-directory or git-checkout	read_only
git_operations (git_status, git_diff, git_log, etc.)	repo	git-checkout	read_only

Built-in tools accept both fs-directory and git-checkout types for file operations because a git-checkout resource's worktree root is an fs-directory. When binding to a git-checkout, the resource router automatically resolves to the worktree root fs-directory child for file operations. A standalone fs-mount resource also works — the router resolves through the fs-mount → root fs-directory chain.

Resource Discovery via Bindings

The binding system enables powerful resource discovery queries:

• "What tools can modify this resource?" → Find all tools with resource slots matching the resource's type and access: read_write or access: write_only.
• "What tools can read this virtual file?" → Find the virtual file's physical children, then find tools with slots matching each physical resource's type.
• "What resources does this tool need?" → Inspect the tool's declared resource slots.
• "Is this tool compatible with this project?" → Check if the project's linked resources can satisfy all of the tool's required resource slots.

Tool Registry

CleverAgents maintains a Tool Registry — a persistent catalog of all independently registered tools:

@startuml
skinparam classAttributeIconSize 0
skinparam classFontSize 13
skinparam noteFontSize 11
skinparam defaultFontSize 12

class ToolRegistry {
  - toolIndex : Map<String, ToolRecord>
  --
  + add(config_path) : ToolRecord
  + update(name, config_path) : ToolRecord
  + remove(name) : void
  + lookup(name) : ToolRecord
  + list(filters) : ToolRecord[]
}

class ToolRecord {
  + name : String
  + description : String
  + source : String {mcp|agent_skill|builtin|custom}
  + config_path : String
  + input_schema : JSONSchema
  + output_schema : JSONSchema
  + capability_metadata : CapabilityMetadata
  + resource_slots : List<ResourceSlot>
  + code : String
}

ToolRegistry "1" *-- "0..*" ToolRecord : indexes >

note right of ToolRegistry
  **Populated by:**
  - agents tool add CLI command
  - Dynamic refresh on MCP notifications

  **Consumed by:**
  - Skill registration
  - Actor graph construction
  - Resource binding resolution
  - Plan validation
  - Permission enforcement
end note
@enduml

The Tool Registry works alongside the Skill Registry (described in the Skills section). Skills reference tools by name from the Tool Registry; the Skill Registry's flattened tool sets are composed from Tool Registry entries plus any anonymous inline tools.

Tool Interface and Architecture

Each individual tool — whether independently registered or defined as an anonymous inline tool — conforms to a uniform interface regardless of its source:

@startuml
skinparam classAttributeIconSize 0
skinparam classFontSize 13
skinparam defaultFontSize 12
skinparam linetype ortho

class Tool {
}

class Identity {
  + name : String
  + qualified_name : String
  + source : ToolSource
}

class Schema {
  + input_schema : JSONSchema
  + output_schema : JSONSchema
}

class CapabilityMetadata {
  + read_only : Boolean
  + writes : Boolean
  + write_scope : String
  + idempotent : Boolean
  + checkpointable : Boolean
  + side_effects : List<String>
  + required_permissions : List<String>
  + cost_profile : String
  + human_approval_required : Boolean
}

class ResourceBindings {
  + slots : Map<String, ResourceSlot>
}

class ResourceSlot {
  + type : String
  + access : String
  + required : Boolean
  + bind : String
  + from_param : String
  + description : String
}

interface Lifecycle <<interface>> {
  + discover() : ToolDescriptor
  + activate() : void
  + execute(params, ctx) : Result
  + deactivate() : void
}

class ExecutionContext {
  + sandbox : Sandbox
  + plan : Plan
  + changes : List<Change>
  + resources : Map<String, BoundResource>
}

enum ToolSource {
  mcp
  agent_skill
  builtin
  custom
}

Tool *-- Identity
Tool *-- Schema
Tool *-- CapabilityMetadata
Tool *-- ResourceBindings
Tool *-- Lifecycle
Tool o-- ExecutionContext : uses at runtime >
ResourceBindings *-- "0..*" ResourceSlot
Identity --> ToolSource
@enduml

Every tool implements the same four lifecycle methods. The tool adapter layer is responsible for translating source-specific behavior into these methods.

Tool Adapter Layer

Each tool source has a corresponding adapter that translates source-specific protocols into the uniform tool interface:

@startuml
skinparam classAttributeIconSize 0
skinparam classFontSize 13
skinparam defaultFontSize 12

interface "ToolInterface" as UTI <<interface>> {
  + discover() : ToolDescriptor
  + activate() : void
  + execute(params, ctx) : Result
  + deactivate() : void
}

class MCPToolAdapter {
  + discover() : ToolDescriptor
  .. tools/list RPC → descriptors ..
  + activate() : void
  .. spawn server, init JSON-RPC ..
  + execute(params, ctx) : Result
  .. tools/call RPC → result ..
  + deactivate() : void
  .. shutdown server ..
}

class AgentSkillAdapter {
  + discover() : ToolDescriptor
  .. parse SKILL.md frontmatter ..
  + activate() : void
  .. load SKILL.md body into agent context ..
  + execute(params, ctx) : Result
  .. agent follows instructions, runs scripts ..
  + deactivate() : void
  .. remove from context ..
}

class BuiltinAdapter {
  + discover() : ToolDescriptor
  .. return hardcoded descriptors ..
  + activate() : void
  .. no-op ..
  + execute(params, ctx) : Result
  .. call native Python impl ..
  + deactivate() : void
  .. no-op ..
}

MCPToolAdapter .up.|> UTI
AgentSkillAdapter .up.|> UTI
BuiltinAdapter .up.|> UTI
@enduml

MCPToolAdapter

Bridges external MCP servers into the tool model:

1. discover(): Spawns the MCP server process (or connects to a remote Streamable HTTP endpoint), performs the MCP initialize handshake, negotiates capabilities, then calls tools/list to enumerate available tools. Each MCP tool becomes a separate ToolDescriptor with its inputSchema and inferred capability metadata.

2. activate(): Ensures the MCP server process is running and the JSON-RPC connection is healthy. For remote servers, validates the authentication token. Registers for notifications/tools/list_changed so CleverAgents can dynamically update available tools.

3. execute(): Translates a Tool.execute(params, ctx) call into an MCP tools/call JSON-RPC request. Before dispatching:

   • Rewrites file paths to sandbox-relative paths
   • Validates params against inputSchema
   • Checks capability metadata against plan permissions
   • Creates a checkpoint if the tool is marked checkpointable

   After the MCP tool returns its content[] response, the adapter:

   • Parses the result into the CleverAgents Result format
   • Records any resource mutations as Change objects in the plan's ChangeSet

4. deactivate(): Sends a clean shutdown to the MCP server process and closes the JSON-RPC connection.

Capability inference: MCP tools expose limited metadata (name, description, inputSchema). The adapter infers extended capability metadata using heuristics:

• Tools whose names contain read, get, list, search, find → read_only: true
• Tools whose names contain write, create, update, delete, set → writes: true
• All inferences can be overridden via the overrides block in tool or skill YAML

AgentSkillAdapter

Bridges Agent Skills Standard (SKILL.md folders) into the tool model. Agent Skills are fundamentally different from MCP tools — they are instruction-driven rather than schema-driven. An Agent Skill is not a single function call; it is a bundle of procedural knowledge that an LLM agent loads into its context and follows.

1. discover(): Scans the configured skill directory for a SKILL.md file. Parses only the YAML frontmatter (name, description, optional compatibility, metadata, allowed-tools) to produce a lightweight ToolDescriptor. This metadata is injected into the agent's system prompt in a structured format so the LLM can decide when the skill is relevant:

   
   <style="color: cyan; font-weight: 600;">available_agent_skills>
     <style="color: cyan; font-weight: 600;">agent_skill>
       <style="color: cyan; font-weight: 600;">name>deploy-to-staging</style="color: cyan; font-weight: 600;">name>
       <style="color: cyan; font-weight: 600;">description>Deploy the current branch to the staging environment.</style="color: cyan; font-weight: 600;">description>
       <style="color: cyan; font-weight: 600;">tool>local/deploy-staging</style="color: cyan; font-weight: 600;">tool>
     </style="color: cyan; font-weight: 600;">agent_skill>
   </style="color: cyan; font-weight: 600;">available_agent_skills>
   

   Discovery is low-cost — only ~50–100 tokens per Agent Skill for metadata. The full instructions are not loaded until activation.

2. activate(): When the LLM agent determines (or is instructed) that a task matches the skill's description, the adapter loads the full SKILL.md Markdown body into the agent's active context. This injects step-by-step instructions, examples, edge cases, and references to bundled scripts. The tool is now "active" — the agent has the procedural knowledge to execute it.

   If the skill references additional files (references/*.md, scripts/*.py, assets/*), these are made available on demand — the agent can read them as needed, following the Agent Skills Standard's progressive disclosure model.

3. execute(): Unlike MCP tools (which are single RPC calls), Agent Skill execution is agent-mediated. The LLM agent follows the loaded instructions, potentially:

   • Running bundled scripts via shell execution (sandboxed)
   • Reading reference files for additional context
   • Using other available tools (e.g., built-in file operations) as part of the procedure
   • Making multiple tool calls in sequence to accomplish the workflow

   The adapter wraps this execution in a tool execution context so that all mutations are tracked, sandboxed, and checkpointable. Script execution respects the allowed_tools and sandbox_policy declared in the tool's YAML.

4. deactivate(): Removes the skill's instructions from the agent's active context to free up token budget. The skill's metadata remains available for re-activation.

Key design principle: Agent Skills extend the agent's knowledge, not just its toolset. An Agent Skill can teach an agent a multi-step workflow that involves calling multiple other tools, making decisions based on intermediate results, and following domain-specific best practices — something a single MCP tool call cannot express.

BuiltinToolAdapter

Wraps CleverAgents' native resource operations as tools:

1. discover(): Returns hardcoded ToolDescriptor objects for each built-in operation. These descriptors have fully specified capability metadata since the implementation is first-party.

2. activate(): No-op. Built-in tools are always available.

3. execute(): Calls the native Python implementation directly. Built-in tools operate through the resource abstraction layer, automatically integrating with sandbox path mapping, change tracking, and checkpointing.

4. deactivate(): No-op.

Built-in tool groups:

File Operations (file_operations):

read_file(path: str) -> str
write_file(path: str, content: str) -> None
edit_file(path: str, edits: list[Edit]) -> None
delete_file(path: str) -> None
move_file(source: str, destination: str) -> None
copy_file(source: str, destination: str) -> None

Directory Operations (directory_operations):

create_directory(path: str) -> None
list_directory(path: str, pattern: str = "*") -> list[str]
delete_directory(path: str, recursive: bool = False) -> None

Search Operations (search_operations):

search_files(pattern: str, content_pattern: str = None) -> list[Match]
find_definition(symbol: str) -> list[Location]
find_references(symbol: str) -> list[Location]

Git Operations (git_operations, when resource is a git repository):

git_status() -> GitStatus
git_diff(path: str = None) -> str
git_log(count: int = 10) -> list[Commit]
git_blame(path: str) -> list[BlameLine]

Each built-in tool:

• Has fully defined capability metadata
• Operates through the resource abstraction layer
• Automatically tracks changes to the ChangeSet
• Respects sandbox boundaries and deny-lists

Tool Capability Metadata (Critical for Safety)

MCP's metadata is not sufficient (read-only/idempotent is not enough; write scope is unclear). CleverAgents extends every tool — regardless of source — with a uniform capability metadata schema:

capability:
  read_only: bool # Whether tool only performs read operations
  writes: bool # Whether tool can modify resources
  write_scope:             # What the tool is allowed to mutate
    - file_paths: ["src/**", "tests/**"]     # Path patterns within bound resources
    - resource_slots: ["repo", "db"]         # Resource slot names (from resource bindings)
    - environment: container | host
  idempotent: bool # Whether repeated calls produce same result
  checkpointable: bool # Whether tool supports checkpoint/rollback
  checkpoint_scope: str # What can be rolled back (file, transaction, commit, snapshot)
  side_effects:            # Non-reversible effects
    - install_packages
    - mutate_infra
    - send_email
  required_permissions:    # Permissions needed to invoke
    - resource:write
    - sandbox:shell
  cost_profile:            # Usage constraints
    rate_limit: "10/min"
    estimated_cost: "$0.01/call"
  human_approval_required: bool # Whether a human must approve invocation

Where metadata comes from per source:

Source	Metadata origin	Override mechanism
Built-in	Hardcoded in implementation	Not overridable (authoritative)
MCP	Inferred from tool name/description + MCP annotations	overrides block in tool or skill YAML; override at skill/actor reference point
Agent Skill	Declared in SKILL.md frontmatter metadata + inferred from allowed-tools	Tool YAML capability block; override at skill/actor reference point
Custom	Manually declared in tool YAML capability block	override at skill/actor reference point (author is the source of truth for registered values)

Read-Only Actions and Tool Permissions

When an action is marked read_only: true, it can only use tools that have read_only: true in their capability metadata. This is enforced at runtime by the tool execution context — any attempt to invoke a tool with writes: true from a read-only plan raises a PermissionDeniedError.

Tool Execution Flow

When an LLM agent decides to use a tool (regardless of source), the following flow occurs through the unified execution pipeline:

1. LLM generates tool call
   e.g., edit_file(path="src/main.py", changes=[...])
   or:   local/github.create_issue(title="Bug fix", body="...")
   or:   (activates Agent Skill "deploy-to-staging" via instructions)
                                ↓
2. Tool Router receives call
   - Resolves tool by name from the Tool Registry or actor's skill tool sets
   - Validates parameters against inputSchema
   - Checks capability metadata against plan's permission policy:
     • Is this tool in allowed skill categories?
     • Does the plan allow writes?
     • Is human approval required?
   - If denied → return PermissionDeniedError to LLM
                                ↓
3. Resource Binding Resolution & Sandbox Context
   - Resolve resource bindings for this tool:
     • Static bindings: already resolved at registration
     • Contextual bindings: resolve from plan's project resources
     • Parameter bindings: resolve from invocation arguments
   - Validate resource type compatibility for each slot
   - Validate access mode (e.g., read_write tool on read_only resource → error)
   - Ensure sandbox exists for each bound resource (lazy sandboxing)
   - Maps logical paths to sandbox-relative paths via bound resource handlers
   - Inject bound resources into ctx.resources[slot_name]
   - If tool is checkpointable → create pre-execution checkpoint
                                ↓
4. Adapter-Specific Execution
   - MCP: sends tools/call JSON-RPC to server process
   - Agent Skill: agent follows loaded SKILL.md instructions,
     running scripts and tools in sandboxed shell
   - Built-in: calls native Python implementation directly
   - Custom: executes inline code with sandboxed context
                                ↓
5. Change Recording
   - If tool modified resources → create Change record(s)
   - Append Change(s) to plan's ChangeSet
   - Update sandbox state
   - If checkpointable → record checkpoint for rollback
                                ↓
6. Result Return
   - Normalize result to uniform Result type
   - Return to LLM agent for continued reasoning

Change Tracking from Tool Invocations

Critical Architecture Point: The ChangeSet is NOT built by parsing LLM output. It is built by recording the effects of tool invocations:

class ToolExecutionContext:
    """Context provided to every tool execution, regardless of source."""
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">__init__</span>(self, plan: Plan, sandbox: Sandbox,
             resources: <span style="color: cyan;">dict</span>[<span style="color: cyan;">str</span>, BoundResource]):
    self.plan = plan
    self.sandbox = sandbox
    self.resources = resources    <span style="opacity: 0.7;"># slot_name → BoundResource</span>
    self.changes: <span style="color: cyan;">list</span>[Change] = []

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">record_change</span>(self, change: Change) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Record a change made by a tool.&quot;&quot;&quot;</span>
    self.changes.append(change)
    self.plan.changeset.add_change(change)

class WriteFileTool:
"""Example: built-in tool for writing files."""
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">execute</span>(self, path: <span style="color: cyan;">str</span>, content: <span style="color: cyan;">str</span>, ctx: ToolExecutionContext) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    handler = ctx.sandbox.get_handler(path)
    change = handler.write(path, content, ctx.sandbox)
    ctx.record_change(change)

This approach means:

• Every resource modification is explicit and tracked
• The ChangeSet accurately reflects what was done, not what was said
• Rollback is precise (replay inverse of recorded changes)
• Audit logs show exactly which tool invocation produced each change

MCP Integration Details

MCP Concepts Mapping

MCP Concept	CleverAgents Equivalent	Extension
Tool	Independently registered Tool (source: mcp), referenced by skills	Extended capability metadata (write_scope, checkpointable, side_effects)
Resource	Resource	Extended to support both read AND write operations
Prompt	Action template	Full plan lifecycle (Strategize → Execute → Apply)
Server	MCP Server connection (declared in tool or skill YAML)	Managed by MCPToolAdapter with lifecycle and reconnection
JSON-RPC	Internal adapter protocol	Abstracted behind Tool.execute()

MCP Server Lifecycle Management

CleverAgents manages MCP server processes as part of the tool/skill/actor lifecycle:

sequenceDiagram
    participant CLI as CLI
    participant Reg as Registry
    participant ActorRT as Actor Runtime
    participant Adapter as MCPToolAdapter
    participant MCP as MCP Server

    note over CLI,Reg: Phase 1 - Registration
    CLI->>Reg: agents tool add or skill add
    Reg->>Reg: Validate server command or endpoint
    Reg->>Reg: Store server config in record

    note over ActorRT,MCP: Phase 2 - Actor Activation
    ActorRT->>Adapter: Activate (for each MCP server)
    Adapter->>MCP: Spawn process (stdio) or connect (HTTP)
    Adapter->>MCP: MCP initialize handshake
    MCP-->>Adapter: Capabilities
    Adapter->>MCP: tools list
    MCP-->>Adapter: Tool descriptors
    Adapter->>MCP: Subscribe notifications tools list_changed

    note over ActorRT,MCP: Phase 3 - Execution
    ActorRT->>Adapter: LLM generates tool call
    Adapter->>MCP: tools call (JSON-RPC)
    MCP-->>Adapter: Result
    Adapter->>ActorRT: Result + Change tracking

    note over ActorRT,MCP: Phase 4 - Deactivation
    ActorRT->>Adapter: Deactivate
    Adapter->>MCP: Clean shutdown

Sandbox Path Rewriting for MCP Tools

MCP servers operate on real filesystem paths, but CleverAgents executes plans in sandboxes. The MCPToolAdapter transparently rewrites paths:

# Before sending to MCP server:
# Logical path: "src/main.py"
# Sandbox path: "/tmp/sandbox-01HXM/worktree/src/main.py"
# The adapter rewrites the tool arguments so the MCP server
# operates on sandboxed state without knowing about the sandbox.

This ensures MCP tools respect sandbox boundaries even though they have no awareness of the CleverAgents sandbox model.

Agent Skills Integration Details

Discovery and Progressive Disclosure

Agent Skills follow a three-tier progressive disclosure model that maps directly to the tool lifecycle:

Tier	What loads	When	Token cost
Metadata	name + description from SKILL.md frontmatter	Tool registration / actor activation	~50–100 tokens per Agent Skill
Instructions	Full SKILL.md Markdown body	When LLM determines task matches the skill (activate phase)	Recommended < 5000 tokens
Resources	scripts/, references/, assets/	On demand during execution	Variable

This means an actor can have dozens of Agent Skill tools available but only pay the token cost for their metadata at startup. Full instructions load only when relevant.

Agent Skills vs. MCP Tools: When to Use Which

Dimension	MCP Tools	Agent Skills
Interaction model	Single function call with JSON params/result	Multi-step procedure with instructions the agent follows
Knowledge type	"Here is a function you can call"	"Here is how to accomplish a complex task"
Statefulness	Stateless per-call	Stateful across multiple tool calls within a procedure
Authoring	Implement an MCP server (code)	Write a SKILL.md file (prose + optional scripts)
Portability	Any MCP-compatible host	Any Agent Skills-compatible agent
Best for	Atomic operations (CRUD, queries, API calls)	Complex workflows (code review processes, deployment procedures, data analysis pipelines)

Both can coexist in the same skill. A common pattern is an Agent Skill tool that teaches the agent a workflow which involves calling multiple MCP tools:

Agent Skill Tool: "local/deploy-staging"
  SKILL.md instructions:
    1. Run tests using the built-in shell tool
    2. Create a PR using the GitHub MCP tool (create_pull_request)
    3. Wait for CI using the GitHub MCP tool (get_check_runs)
    4. Deploy using the AWS MCP tool (ecs_update_service)
    5. Verify deployment using the HTTP MCP tool (fetch_url)

Tool-Level Checkpointability

Each tool declares whether it supports checkpoint/rollback via its capability metadata. Checkpointing behavior varies by source:

Source	Checkpoint mechanism
Built-in file ops	Snapshot file state pre-modification; rollback restores file content
Built-in git ops	Create commit or stash; rollback via git reset / git checkout
MCP tools	Adapter-created checkpoints of affected resources; rollback replays inverse operations where possible
Agent Skills	Composite — each sub-tool-call within the skill's execution is individually checkpointed
Custom (containerized)	Filesystem snapshot or container image layer; rollback restores snapshot

Checkpointing is easier when tool scope is constrained (e.g., "only files within a sandbox worktree + git"). The capability metadata's checkpoint_scope field communicates what granularity of rollback is supported.

When require_checkpoints is enabled on a plan, the plan may only use tools that have checkpointable: true. If disabled, the plan may use more generic/unsafe tools with fewer restrictions (useful for low-stakes tasks).

Skills

What a Skill Is

A skill is a namespaced, reusable collection of tools that is registered in the system via its own YAML configuration file and managed through agents skill CLI commands. Skills are the unit of capability composition in CleverAgents — they define what an actor can do by assembling tools into coherent, reusable bundles.

A skill is not a single tool. It is a container that references one or more tools (by name from the Tool Registry) and/or defines anonymous inline tools, along with metadata describing the collection's purpose, capabilities, and safety characteristics. Tools are independently registered, callable operations (see the Tools section above). Skills organize them into reusable groups.

Key properties of a skill:

• Named and namespaced: Skills follow the same <namespace>/<name> naming convention as actors, tools, actions, and plans (e.g., local/github-ops, cleverthis/file-management, local/deploy-tools).
• Defined in their own YAML files: Skills are NOT defined inline in actor configurations. They have their own configuration files and are managed as independent, reusable entities.
• A collection of tools: Each skill references named tools from the Tool Registry and/or defines anonymous inline tools. Skills can also expose tools from MCP servers, Agent Skills Standard folders, and built-in tool groups.
• Hierarchically composable: A skill can include other skills by reference, inheriting all of their tools. This enables layered composition — a "full-stack" skill might include a "file-ops" skill, a "git-ops" skill, and a "github" skill. When including a sub-skill, individual tool metadata can optionally be overridden.
• Referenced by actors: Actors reference skills by fully-qualified name. The actor's graph gains access to all tools within the referenced skills.

The Skill / Tool Distinction

@startuml
skinparam packageStyle rectangle
skinparam defaultFontSize 12
skinparam componentFontSize 12

package "Skill: local/devops-toolkit" as Skill {
  package "builtin: file_operations" as FileOps {
    component [read_file()] as T1
    component [write_file()] as T2
    component [edit_file()] as T3
  }

  package "builtin: git_operations" as GitOps {
    component [git_status()] as T4
    component [git_diff()] as T5
  }

  package "mcp: github-server" as GH {
    component [create_issue()] as T6
    component [create_pr()] as T7
    component [list_repos()] as T8
  }

  package "custom" as Custom {
    component [run_migrations()] as T9
  }

  package "Included Skills" as Includes {
    component [local/pdf-processing\n(adds pdf tools)] as I1
    component [local/data-analysis\n(adds analysis tools)] as I2
  }
}
@enduml

Tools are independently registered, atomic units of execution (see the Tools section above for full details). Each tool has:

• A namespaced name, description, and JSON Schema for inputs/outputs
• Its own YAML configuration file, registered via agents tool add
• A source type (mcp, agent_skill, builtin, custom)
• Capability metadata (read_only, writes, checkpointable, etc.)
• A lifecycle: discover(), activate(), execute(params, ctx), deactivate()

Skills are the organizational units. Each skill has:

• A namespaced name
• A YAML configuration file defining its tool composition
• Zero or more named tool references (pointing to independently registered tools in the Tool Registry)
• Zero or more anonymous inline tools (one-off tools defined directly in the skill YAML)
• Zero or more included child skills (whose tools are merged in, with optional per-tool metadata overrides)
• Tool sources: MCP servers, Agent Skills folders, built-in tool groups
• Metadata (description, capability summary)

When an actor references a skill, it gains access to the flattened set of all tools — named tool references, anonymous tools, tools from MCP/Agent Skills/builtins, and those inherited from included child skills.

Skill Configuration (YAML)

Skills are defined in their own YAML configuration files, separate from tool and actor configurations. A skill YAML file declares which registered tools it includes (by name), which other skills it includes, and optionally defines anonymous inline tools:

# File: skills/devops-toolkit.yaml
cleveragents:
  version: "3.0"
skill:
name: local/devops-toolkit
description: "Full-stack development tools for file ops, git, GitHub, and deployment"
  # ── Named Tool References ───────────────────────────────
  # Reference independently registered tools by name.
  # These tools must already be registered via agents tool add.
  # Optional metadata overrides can be applied per tool.
tools:
- local/run-migrations                  # Simple reference, use as registered
- name: local/deploy-staging            # Reference with metadata override
override:
capability:
human_approval_required: true # Require approval in this skill context
  # ── Include other skills ─────────────────────────────────
  # All tools from included skills become part of this skill.
  # Included skills must already be registered in the system.
  # Individual tools from included skills can have metadata overridden.
includes:
- local/file-ops           # built-in file + directory + search tools
- local/git-ops            # built-in git tools
- name: local/github       # MCP-based GitHub tools, with per-tool overrides
tool_overrides:
- tool: local/create-github-issue
override:
capability:
write_scope: [github:issues:org-only]
  # ── MCP Server Tools ─────────────────────────────────────
  # Connect to MCP servers and expose their tools.
  # Tools discovered from MCP servers are auto-registered in the
  # Tool Registry if not already present.
mcp_servers:
- name: linear
command: "npx @anthropic/mcp-linear"
env:
LINEAR_API_KEY: "${LINEAR_API_KEY}"
      # Optional: override inferred capability metadata per tool
overrides:
- tool: create_issue
writes: true
write_scope: [linear:issues]
- tool: list_issues
read_only: true
  # ── Agent Skills (SKILL.md folders) ──────────────────────
  # Each Agent Skill folder is loaded as a composite tool.
  # The agent discovers it via metadata, activates it by
  # loading SKILL.md instructions, and follows them.
agent_skills:
- path: ./skills/code-review-checklist
sandbox_policy: none
  # ── Built-in Tool Groups ─────────────────────────────────
  # Opt-in to built-in tool groups provided by CleverAgents.
builtins:
- group: shell_operations
  # ── Anonymous Tools ──────────────────────────────────────
  # Inline tool definitions for one-off, skill-specific operations.
  # Same format as a named tool YAML body but without a name.
  # These are NOT registered in the Tool Registry and are NOT reusable.
anonymous_tools:
- description: "One-off cleanup for legacy migration artifacts"
input_schema:
type: object
properties:
directory: { type: string }
capability:
writes: true
checkpointable: true
checkpoint_scope: file
code: |
import os, glob
directory = params["directory"]
removed = []
for f in glob.glob(os.path.join(ctx.sandbox.root, directory, "*.legacy")):
os.remove(f)
removed.append(f)
return {"removed": removed, "count": len(removed)}

Here is an example of a simpler skill that wraps only built-in tools, suitable for common reuse:

# File: skills/file-ops.yaml
cleveragents:
  version: "3.0"
skill:
name: local/file-ops
description: "File and directory operations"
builtins:
- group: file_operations      # read, write, edit, delete, move, copy
- group: directory_operations  # create, list, delete dirs

And an example of a skill that is purely MCP-based:

# File: skills/github.yaml
cleveragents:
  version: "3.0"
skill:
name: local/github
description: "GitHub operations via MCP"
mcp_servers:
- name: github
command: "npx @anthropic/mcp-github"
env:
GITHUB_TOKEN: "${GITHUB_TOKEN}"
overrides:
- tool: create_issue
writes: true
write_scope: [github:issues]
checkpointable: false
- tool: create_pull_request
writes: true
write_scope: [github:pulls]
checkpointable: false
- tool: list_repos
read_only: true
- tool: get_file_contents
read_only: true

Skill Hierarchy and Composition

Skills can include other skills via the includes field. When a skill includes another, all tools from the child skill (and transitively, all tools from any skills it includes) become part of the parent skill's flattened tool set.

local/full-stack-dev
  ├── includes: local/file-ops
  │     └── builtins: file_operations, directory_operations
  ├── includes: local/git-ops
  │     └── builtins: git_operations
  ├── includes: local/github
  │     └── mcp_servers: github (create_issue, create_pr, list_repos, ...)
  ├── agent_skills: code-review-checklist
  └── tools: local/run-migrations, local/deploy-staging  (named tool refs)
Flattened tool set available to actors referencing local/full-stack-dev:
read_file, write_file, edit_file, delete_file, move_file, copy_file,
create_directory, list_directory, delete_directory,
git_status, git_diff, git_log, git_blame,
create_issue, create_pr, list_repos, get_file_contents,
code-review-checklist (agent skill),
local/run-migrations, local/deploy-staging (named tools)

Rules for skill composition:

1. Circular includes are forbidden. The system validates the include graph at registration time and rejects cycles.
2. Tool name conflicts: If two included skills provide tools with the same name, the conflict is resolved by qualification — the tool must be referenced as <skill_name>.<tool_name> (e.g., local/github.create_issue vs local/linear.create_issue). Direct tools (defined in the skill itself) take precedence over included tools.
3. Included skills must be registered before the including skill can be added. The agents skill add command validates this.
4. Depth is unlimited but the flattened tool set is computed at registration time and cached. Deep hierarchies do not incur runtime overhead.

Skill Registration and Management

Skills are managed through the agents skill CLI commands. Named tools referenced by skills must first be registered via agents tool add (see the Tools section):

# First, register any named tools the skill will reference
agents tool add --config ./tools/run-migrations.yaml local/run-migrations
agents tool add --config ./tools/deploy-staging.yaml local/deploy-staging
# Then register the skill (which references those tools by name)
agents skill add --config ./skills/devops-toolkit.yaml local/devops-toolkit
# Update an existing skill (re-reads the config file, overwrites registration)
agents skill add --config ./skills/devops-toolkit.yaml --update local/devops-toolkit
# List all registered skills
agents skill list
# Show details for a skill (tools, includes, metadata)
agents skill show local/devops-toolkit
# List all tools provided by a skill (flattened, including from child skills)
agents skill tools local/devops-toolkit
# Remove a skill
agents skill remove local/devops-toolkit

Once registered, a skill is available to be referenced by any actor configuration. Skills persist in the database (local or server) and follow the same namespace rules as actors, tools, and actions.

Actor References to Skills and Tools

Actors reference skills by name to make collections of tools available for LLM tool-calling. Additionally, actor graphs can include tool nodes that directly reference named tools or define anonymous inline tools (see Nodes in the Graph in the Actor section).

The actor's configuration lists which skills it should have access to:

# File: actors/code-assistant.yaml
cleveragents:
  version: "3.0"
  default_actor: code_assistant
actors:
code_assistant:
type: llm
config:
actor: anthropic/claude-3-opus
temperature: 0.3
system_prompt: |
You are a code assistant with access to file, git, and GitHub tools.
Current task: {{ context.task_description }}
    # Reference skills by fully-qualified name.
    # All tools from these skills become available to this actor.
skills:
- local/file-ops
- local/git-ops
- local/github
  # Or reference a single composite skill that includes all of the above
full_stack_assistant:
type: llm
config:
actor: anthropic/claude-3-opus
system_prompt: |
You are a full-stack development assistant.
<span style="color: cyan; font-weight: 600;">skills</span>:
  - local/full-stack-dev    # includes file-ops, git-ops, github, etc.

Server-qualified skill references: When connected to multiple servers, skills can be disambiguated with a server prefix, same as actors:

skills:
  - dev:freemo/custom-analysis     # from dev server, personal namespace
  - prod:cleverthis/deploy-tools   # from prod server, org namespace
  - local/file-ops                 # local skill

Skill Registry

To enable plan validation, permission enforcement, and discovery at scale, CleverAgents maintains two registries that work together:

1. Tool Registry — a persistent catalog of all independently registered tools (described in the Tools section above). Managed via agents tool add/remove/list/show.

2. Skill Registry — a persistent catalog of all registered skills and their flattened tool sets. The Skill Registry composes its tool sets by resolving named tool references from the Tool Registry, incorporating anonymous inline tools, and merging tools from included child skills.

@startuml
skinparam classAttributeIconSize 0
skinparam classFontSize 13
skinparam defaultFontSize 12

class SkillRegistry {
  - skillIndex : Map<String, SkillRecord>
  --
  + add(config_path) : SkillRecord
  + update(name, config_path) : SkillRecord
  + remove(name) : void
  + lookup(name) : SkillRecord
  + list(filters) : SkillRecord[]
  + tools(name) : ToolDescriptor[]
  + validate_plan(plan) : ValidationResult
  + refresh(name) : void
}

class SkillRecord {
  + name : String
  + description : String
  + config_path : String
  + includes : List<String>
  + tool_refs : List<String>
  + anonymous_tools : List<ToolDef>
  + flattened_tools : List<ToolDescriptor>
  + overrides : Map<String, Object>
  + capability_summary : CapabilitySummary
}

SkillRegistry "1" *-- "0..*" SkillRecord : indexes >

note right of SkillRegistry
  **Populated by:**
  - agents skill add CLI command
  - Dynamic refresh on MCP notifications

  **Depends on:**
  - Tool Registry (resolve named tool references)

  **Consumed by:**
  - Actor activation
  - Plan validation
  - Permission enforcement
  - Agent context injection
end note
@enduml

Both registries persist in the database (local SQLite or server). MCP server tools are refreshed dynamically when notifications/tools/list_changed events are received.

Session

What a Session Is

A session is a user's interactive thread with CleverAgents across time.

A session should:

• maintain conversational continuity,
• store plan references,
• persist memory (if enabled),
• provide a UI anchor (CLI invocation, TUI workspace, web session).

Session and Memory Persistence

The notes include a known issue: conversation history can be lost between CLI invocations depending on connection string configuration, implying the system needs a stable memory service backend.

Therefore, CleverAgents should specify:

• sessions have stable IDs
• sessions can be resumed
• session storage backend is configured explicitly
• if session persistence is disabled, the UX should be explicit about it (no silent history loss)

Server

What a Server Is

A server is an optional mode that enables:

• multi-user access
• shared org namespaces (<username>/ and <orgname>/)
• persistent plan records
• remote plan execution
• permissioning and governance

Single-user local mode is the default (so setup is easy), but the architecture anticipates server mode for shared skills and org-level actions.

Client-Only vs Server Mode

Mode	Description	Plan Execution
Client-only	No server connection. All data in local database.	Always local
Server mode	Connected to a CleverAgents server. Namespaced items sync.	Local or server

It is possible to run a client with no server at all. Server is optional.

Plan Execution Location

Where a plan executes depends on the project type:

Project Type	Client Execution	Server Execution
Local (has local-only resources)	✅ Yes	❌ No (server can't access local resources)
Remote (all resources remotely accessible)	✅ Yes	✅ Yes

When acting on local projects, the client must be running because only the client can access local resources.

Remote projects can execute on either:

• The client (if user prefers local execution)
• The server (for long-running plans, since client may be transient)

Server Execution Benefits

Server execution is useful when:

• Plans take a long time to execute
• Client may disconnect (laptop closes, network issues)
• Multiple team members need to monitor plan progress
• Centralized logging and auditing required

No Plan Queuing

Plans are not queued. When a plan is used on projects and executed, it runs immediately. There is no worker queue or delayed execution model.

Multi-user Risks and Prompt Injection

Prompt injection isn't critical in single-user mode but becomes important for multi-user server environments.

Server mode must include:

• permission boundaries
• prompt sanitization / safe templating
• resource access controls
• auditing

Permissions

Permissions exist at multiple layers:

1) Namespace-level permissions

• Who can create/edit org entities (actions, actors, tools, skills, resources, projects)?
• Who can run them?

2) Project-level permissions

• Who can modify project resources?
• Who can apply changes?

3) Plan-level permissions

• Can this plan write?
• Does it require approvals?
• Can it access restricted skills?

4) Skill-level permissions

• Some skills should require:

  • explicit user approval per call, or
  • elevated role membership.

5) Resource-level permissions

• Who can register/modify/remove resources in a namespace?

• Who can link a resource to a project?

• Per-resource access control:

  • Some resources may be restricted to specific roles (e.g., production databases)
  • Read-only vs. read-write access can be enforced per resource per project (via project-level overrides)
  • Sensitive resources can require explicit approval before sandbox creation or tool binding

Approval Gates by Phase (Recommended)

A simple and powerful governance model:

• Strategize: generally safe, read-only → minimal restrictions
• Execute: writes occur but sandboxed → moderate restrictions
• Apply: writes are real → strict restrictions + optional mandatory review

This aligns with the four-phase model's safety rationale.

Resources

A resource is a namespaced, independently registered entity representing anything that a plan can reason about or manipulate — git repositories, filesystems, databases, APIs, documents, and more. Resources are first-class citizens in CleverAgents, managed through the agents resource CLI commands and stored in the Resource Registry.

CleverAgents extends the MCP resource concept to support both read AND write operations (MCP resources are read-only). Unlike MCP's flat resource model, CleverAgents resources form a directed acyclic graph (DAG) with parent/child relationships, support physical vs virtual distinction for content identity tracking, and are governed by a resource type system that constrains their structure and behavior.

Resources are registered independently of projects. Projects link to resources from the Resource Registry — a resource can be linked to multiple projects, enabling shared resources across teams and workflows.

What a Resource Is

A resource has:

• Name and namespace: Resources follow the same <namespace>/<name> naming convention as actors, tools, skills, and other entities (e.g., local/api-repo, cleverthis/staging-db).
• Resource type: Every resource has a type (e.g., git, fs-mount, git-branch, fs-file) that determines its properties, CLI arguments, allowed parent/child relationships, sandbox strategy, and handler implementation.
• Physical or virtual nature: Resources are either physical (a specific, concrete manifestation) or virtual (an abstract identity linking equivalent physical resources). This is determined by the resource type.
• Value/properties: Type-specific properties (a file path, a URL, a connection string, a commit hash, etc.).
• Parent/child relationships: Resources form a DAG. A resource can have multiple parents and multiple children, subject to type constraints.
• Capabilities: Whether the resource is readable, writable, sandboxable, and checkpointable.

Resource Types

A resource type is a schema-level definition that constrains a category of resources. Resource types define:

• CLI arguments: What arguments agents resource add <type> accepts (for user-addable types), including which are required vs optional and validation rules.
• Physical or virtual: Whether instances of this type are physical or virtual resources.
• Allowed parent types: What resource types are valid parents.
• Allowed child types: What resource types are valid children, and whether they are auto-discovered or manually linkable.
• Auto-discovery behavior: What child resources are automatically created when an instance of this type is registered (e.g., registering a git-checkout resource auto-discovers a git child and an fs-directory child for the worktree root; the git child in turn auto-discovers remotes, branches, commits, and tree entries).
• User addable: Whether users can create instances of this type directly via agents resource add <type>. Types with user_addable: false are only generated as auto-discovered children of other resources.
• Sandbox strategy: The default sandboxing approach for instances of this type.
• Handler: The resource handler implementation that provides read/write/sandbox/checkpoint operations.

Built-in Resource Types

Built-in types are organized into three layers: git (version control structure), git-checkout (a composition that bridges git metadata with a local directory), and filesystem (physical files on disk). Virtual types link equivalent physical resources across these layers through content/identity matching. There are 24 built-in types total (15 physical + 9 virtual), of which 4 are user-addable (git-checkout, git, fs-mount, fs-directory).

Each table below includes the full parent/child relationship constraints. Allowed Parents lists what types may be a parent of this type, with cardinality (how many parents of that type are allowed) and whether the relationship is required or optional. Allowed Children lists what types may be children, with cardinality and whether they are required or optional. 0..* means zero or more, 1 means exactly one (required), 0..1 means zero or one (optional).

Physical types — Git layer (version control structure — every instance is a concrete manifestation in a specific repository):

Type	User Addable	Sandbox	Allowed Parents	Allowed Children	Description
git	yes	none	git-checkout (0..1, optional)	git-remote (0.., optional), git-branch (0.., optional), git-tag (0.., optional), git-commit (0.., optional), git-stash (0.., optional), git-submodule (0.., optional)	A git repository — the object database, refs, and full history. Accessible via a local .git directory path or a remote URL. Every git resource is a specific, concrete repo instance (local or hosted on a remote server).
git-remote	no	none	git (1, required)	(none)	A remote URL configured on a git repo (e.g., origin → https://github.com/org/repo). Auto-discovered child of git. Optionally linked as a child of a remote virtual resource when the same URL appears in multiple repos.
git-branch	no	(inherits)	git (1, required)	git-commit (0..*, optional)	A named branch ref (e.g., main, feature/auth). Auto-discovered child of git. Optionally linked as a child of a branch virtual resource when the same branch name + HEAD exists in multiple repos.
git-tag	no	(inherits)	git (1, required)	(none)	A tag ref — lightweight or annotated (e.g., v1.0.0). Auto-discovered child of git. Optionally linked as a child of a tag virtual resource when the same tag name + target exists in multiple repos.
git-commit	no	(inherits)	git-branch (1..*, required), git (1, required)	git-tree (1, required)	A specific commit object. Auto-discovered child of git-branch (a commit can belong to multiple branches). Each commit contains exactly one root git-tree. Also a direct child of git for ancestry traversal. Optionally linked as a child of a commit virtual resource when the same commit hash exists in multiple repos (e.g., shared history between fork and upstream).
git-tree	no	(inherits)	git-commit (1..*, required)	git-tree-entry (0.., optional), git-tree (0.., optional)	A tree object — a directory listing at a specific commit. Each tree contains entries (blobs and subtrees). Auto-discovered child of git-commit. Trees are recursive: a tree can contain subtrees as children. Optionally linked as a child of a tree virtual resource when the same tree hash exists across commits/repos.
git-tree-entry	no	(inherits)	git-tree (1, required)	(none)	A blob entry in a tree — a specific file's content at a specific path and mode. Auto-discovered child of git-tree. Represents the leaf of git's content-addressable storage. Optionally linked as a child of a file virtual resource when byte-identical content with the same name and permissions exists in the filesystem layer.
git-stash	no	(inherits)	git (1, required)	(none)	A stash entry (e.g., stash@{0}). Auto-discovered child of git. Represents work-in-progress saved via git stash.
git-submodule	no	(inherits)	git (1, required)	(none)	A submodule reference — a pointer to another git repository at a specific commit and path. Auto-discovered child of git. Optionally linked as a child of a submodule virtual resource when the same submodule URL + path exists in multiple repos.

Physical types — Git checkout (composition: git metadata + local worktree directory):

Type	User Addable	Sandbox	Allowed Parents	Allowed Children	Description
git-checkout	yes	git_worktree	(none — always a top-level resource)	git (1, required), fs-directory (1, required)	A locally checked-out git repository. The composition type — auto-discovers a git child (the repo's object database, branches, tags, commits, trees, and remotes) and an fs-directory child (the worktree root directory, e.g., /home/user/projects/my-app). Most users register this type. The worktree root IS a directory, not a mount point, so git-checkout composes with fs-directory directly.

Physical types — Filesystem layer (files on disk, used by git checkouts and standalone directories alike):

Type	User Addable	Sandbox	Allowed Parents	Allowed Children	Description
fs-mount	yes	copy_on_write	(none — always a top-level resource)	fs-directory (1, required)	A physical mount point on the local system (e.g., /mnt/data, /home). Represents the mount itself — the filesystem type (ext4, btrfs, etc.) is a property. The root directory is a required auto-discovered fs-directory child. Used for registering entire mount points or storage volumes.
fs-directory	yes	copy_on_write	git-checkout (0..1, optional), fs-mount (0..1, optional), fs-directory (0..1, optional)	fs-directory (0.., optional), fs-file (0.., optional), fs-symlink (0.., optional), fs-hardlink (0.., optional)	A directory on the filesystem. Can be the root child of fs-mount (mount point root), the worktree root child of git-checkout, a subdirectory child of another fs-directory, or a standalone user-registered directory. Optionally linked as a child of a directory virtual resource when equivalent directory content exists elsewhere. When user-addable, accepts --path flag.
fs-file	no	(inherits)	fs-directory (1, required)	(none)	A regular file on the local filesystem. Auto-discovered child of fs-directory. Optionally linked as a child of a file virtual resource when byte-identical content with the same name and permissions exists elsewhere (in another fs-file or a git-tree-entry).
fs-symlink	no	(inherits)	fs-directory (1, required)	(none)	A symbolic link on the local filesystem. Auto-discovered child of fs-directory. Optionally linked as a child of a symlink virtual resource when a symlink with the same name and target exists elsewhere.
fs-hardlink	no	(inherits)	fs-directory (1, required)	(none)	A hard link on the local filesystem — a file with link count > 1. Auto-discovered child of fs-directory. The system tracks hard link relationships by inode to avoid treating the same underlying data as distinct resources.

Virtual types (abstract identity types that link equivalent physical resources — never user-addable, no sandbox):

Virtual types use simple names that mirror their physical counterparts. A virtual resource answers the question: "where else does this same thing exist?" Two physical resources share a virtual parent when they are equivalent by the virtual type's criteria (same content, same name, same permissions, same hash, etc.).

Type	Allowed Children	Description
file	fs-file (0.., optional), git-tree-entry (0.., optional)	Cross-layer file identity. Links physical fs-file and git-tree-entry resources that represent the same file — identical content bytes, filename, and permissions. Answers: "this file in the working tree and this blob in git's tree are the same file."
directory	fs-directory (0.., optional), git-tree (0.., optional)	Cross-layer directory identity. Links physical fs-directory and git-tree resources whose recursive contents are equivalent. Answers: "this directory on disk matches this tree object in git."
symlink	fs-symlink (0.., optional), git-tree-entry (0.., optional)	Cross-layer symlink identity. Links physical fs-symlink and git-tree-entry (mode 120000) resources with the same name and target.
commit	git-commit (0..*, optional)	Cross-repo commit identity. Links git-commit resources across different repos that share the same commit hash — typically a fork and its upstream. Answers: "this commit exists in both repos."
branch	git-branch (0..*, optional)	Cross-repo branch identity. Links git-branch resources across different repos with the same branch name and HEAD commit hash.
tag	git-tag (0..*, optional)	Cross-repo tag identity. Links git-tag resources across different repos with the same tag name and target object.
remote	git-remote (0..*, optional)	Cross-repo remote identity. Links git-remote resources across different repos that point to the same URL. Answers: "these repos share the same upstream."
submodule	git-submodule (0..*, optional)	Cross-repo submodule identity. Links git-submodule resources across different repos with the same submodule URL and path.
tree	git-tree (0..*, optional)	Cross-repo/cross-commit tree identity. Links git-tree resources across different commits or repos that have the same tree hash — identical directory structure and contents.

Key design notes:

• Virtual types are never user-addable — they are created and maintained automatically by the system when equivalent physical resources are detected.
• Virtual types have no sandbox strategy because they represent abstract identities, not concrete locations. Tools always operate on the physical children.
• Physical types list virtual parents in their Allowed Parents column (via the "optionally linked" descriptions). For example, an fs-file can be linked as a child of a file virtual resource when its content matches a git-tree-entry. This is how the DAG connects physical and virtual layers.
• git represents a specific git repository instance — whether hosted remotely (e.g., on GitHub's servers) or locally (a .git directory on disk). It is physical because it is a concrete manifestation that exists somewhere, not an abstract identity. A git resource can be created from a remote URL alone (accessing the object database via the git protocol) or from a local .git directory.
• git-checkout is the type most users register for repos they've cloned. It composes a git child (repo metadata) and an fs-directory child (the worktree root directory). The worktree root is a directory on an existing filesystem — NOT a separate mount point — so git-checkout links directly to fs-directory, not fs-mount. This means the worktree's files use the same fs-directory and fs-file types regardless of how they were created.
• git-commit has a git-tree child (the root tree object), which in turn has git-tree-entry and nested git-tree children. This properly models git's internal object structure: commits point to trees, trees contain entries (blobs) and subtrees.
• fs-mount represents a physical mount point, not a directory. The filesystem type (ext4, btrfs, etc.) is a property on the mount. The root directory is an auto-discovered fs-directory child. Use fs-mount for registering mount points and storage volumes. Use fs-directory (user-addable) for registering arbitrary directories.
• fs-directory is user-addable, allowing users to register standalone directories (e.g., a build output folder, a config directory) without wrapping them in an fs-mount. An fs-directory registered standalone or as a child of fs-mount/git-checkout uses the same type and auto-discovers the same fs-file, fs-symlink, fs-hardlink, and nested fs-directory children.
• git-tree-entry represents a blob in git's tree — a specific file's content at a specific path and mode. The corresponding file on disk (if checked out) is an fs-file. The file virtual type bridges these two when their content, name, and permissions are identical.
• 4 user-addable types: git-checkout (local repo), git (remote or local metadata-only), fs-mount (mount point), fs-directory (arbitrary directory).

Built-in Type Hierarchy

The parent-child relationships between built-in resource types form three layers — git structure, git checkout (composition), and filesystem — bridged by virtual identity types:

@startuml
skinparam defaultFontSize 11
skinparam objectFontSize 11
skinparam packageStyle rectangle

package "GIT-CHECKOUT (composition)" as GC #LightBlue {
  object "git-checkout" as gc {
    /home/user/projects/myapp
  }
}

package "GIT STRUCTURE" as GS #LightYellow {
  object "git" as git {
    repo object DB
  }
  object "git-remote" as remote {
    origin
  }
  object "git-tag" as tag {
    v1.0.0
  }
  object "git-submodule" as submod {
    lib/shared
  }
  object "git-branch" as branch {
    main
  }
  object "git-stash" as stash {
    stash@0
  }
  object "git-commit" as commit {
    a1b2c3d
  }
  object "git-tree" as tree {
    e8f1... root
  }
  object "git-tree-entry" as entry1 {
    README.md
  }
  object "git-tree" as subtree {
    src/ subtree
  }
  object "git-tree-entry" as entry2 {
    src/app.ts
  }
  object "git-tree-entry" as entry3 {
    src/main.ts
  }
}

package "FILESYSTEM (worktree)" as FS #LightGreen {
  object "fs-directory" as fsroot {
    worktree root
  }
  object "fs-directory" as srcdir {
    src/
  }
  object "fs-file" as readme {
    README.md
  }
  object "fs-file" as app {
    app.ts
  }
  object "fs-file" as main {
    main.ts
  }
}

package "STANDALONE FS-DIRECTORY" as SFS #Wheat {
  object "fs-directory" as sfs {
    /opt/deploy/myapp
  }
  object "fs-directory" as ssrcdir {
    src/
  }
  object "fs-file" as sreadme {
    README.md
  }
  object "fs-file" as sapp {
    app.ts
  }
  object "fs-file" as smain {
    main.ts
  }
  object "fs-symlink" as symlink {
    link.txt
  }
}

package "STANDALONE FS-MOUNT" as SM #LightCoral {
  object "fs-mount" as mount {
    /mnt/data
  }
  object "fs-directory" as mountroot {
    root: /
  }
}

package "VIRTUAL LAYER (abstract identities)" as VL #Lavender {
  object "file" as vfile {
    app.ts @ sha256:9f8e...
  }
  object "directory" as vdir {
    src/ @ merkle:3d4f...
  }
  object "commit" as vcommit {
    a1b2c3d
  }
  object "branch" as vbranch {
    main @ a1b2c3d
  }
  object "remote" as vremote {
    github.com/org/repo
  }
  object "tree" as vtree {
    e8f1...9d2a
  }
}

gc --> git
gc --> fsroot

mount --> mountroot

git --> remote
git --> tag
git --> submod
git --> branch
git --> stash

branch --> commit
commit --> tree
tree --> entry1
tree --> subtree
subtree --> entry2
subtree --> entry3

fsroot --> srcdir
fsroot --> readme
srcdir --> app
srcdir --> main

sfs --> ssrcdir
sfs --> sreadme
sfs --> symlink
ssrcdir --> sapp
ssrcdir --> smain

app ..> vfile : equivalent
sapp ..> vfile : equivalent
entry2 ..> vfile : equivalent
srcdir ..> vdir : equivalent
ssrcdir ..> vdir : equivalent
subtree ..> vdir : equivalent
commit ..> vcommit : equivalent
branch ..> vbranch : equivalent
remote ..> vremote : equivalent
tree ..> vtree : equivalent
@enduml

Reading the diagram:

• Top left: A git-checkout resource decomposes into a git child (left — the repository's full structure) and an fs-directory child (center — the worktree root directory at /home/user/projects/myapp). The worktree root is a directory, not a mount point.
• Git structure (left column): The git child contains git-remote (origin), git-tag (v1.0.0), git-submodule (lib/shared), git-stash (stash@{0}), and git-branch (main). The branch contains git-commit objects, each commit contains a root git-tree, and trees contain git-tree-entry (blobs) and nested git-tree (subtrees). This properly models git's internal object structure.
• Filesystem (center/right): The worktree's fs-directory contains src/ (an fs-directory), README.md (an fs-file), and files including fs-symlink resources. A standalone fs-directory (/opt/deploy/myapp) with the same structure. A standalone fs-mount (/mnt/data) with a root fs-directory child.
• Virtual layer (boxed area): Shows how virtual types link equivalent physical resources:
  • A file virtual resource links fs-file and git-tree-entry resources that have the same content, filename, and permissions — bridging the filesystem and git layers.
  • A directory virtual resource links fs-directory and git-tree resources with the same recursive content.
  • A commit virtual resource links git-commit resources across repos with the same commit hash.
  • A branch virtual resource links git-branch resources across repos with the same name and HEAD.
  • A remote virtual resource links git-remote resources across repos with the same URL.
  • A tree virtual resource links git-tree resources across repos/commits with the same tree hash.

Key separations:

• git vs git-checkout: A git resource represents a specific repository instance (local or remote). It can exist without any local directory (created from a remote URL — the repo exists on the remote server). A git-checkout always has both a git child and an fs-directory child — it represents a locally cloned repo with files on disk.
• git-tree-entry vs fs-file: A git-tree-entry is a blob entry in git's tree (path + content hash + mode). An fs-file is a physical file on disk. When a repo is checked out, both exist and typically have matching content — the file virtual type links them when content, name, and permissions match.
• git-tree vs fs-directory: A git-tree is a tree object in git's object database (a directory listing at a commit). An fs-directory is a physical directory on disk. The directory virtual type links them when their recursive contents match.
• git-commit → git-tree → git-tree-entry: This chain properly models git internals. Commits point to a root tree, trees contain entries (blobs) and subtrees. The old design skipped the tree level; the current design preserves it.
• fs-mount vs fs-directory: An fs-mount is a mount point (e.g., /mnt/data). The filesystem type (ext4, btrfs, etc.) is a property. The root directory is an fs-directory child. An fs-directory is a directory — it can be a child of fs-mount, git-checkout, or another fs-directory, or it can be registered standalone by the user.
• git-checkout composes with fs-directory, not fs-mount: A git checkout's worktree is a directory on an existing filesystem, not a separate mount point. This is why git-checkout's required child is fs-directory (the worktree root), not fs-mount.
• Virtual types bridge physical equivalents: file bridges fs-file + git-tree-entry (cross-layer). directory bridges fs-directory + git-tree (cross-layer). commit, branch, tag, remote, submodule, and tree link the same git structural element across different repos.

Concrete Example: A Made-Up Project

Consider a web application called "Acme Dashboard" with three registered resources:

1. local/acme-app — a checked-out git repo at /home/alice/projects/acme-dashboard (type: git-checkout)
2. local/acme-upstream — a git repo accessed via remote URL, not cloned (type: git)
3. local/acme-deploy — a standalone directory at /opt/deploy/acme-dashboard containing a production build snapshot (type: fs-directory)

Registration:

# 1) Checked-out git repo (has local files on disk)
agents resource add git-checkout local/acme-app \
  --path /home/alice/projects/acme-dashboard --branch main
# 2) Git repo via remote URL (no local checkout — metadata only)
agents resource add git local/acme-upstream </span>
--url git@github.com:acmecorp/dashboard.git
# 3) Standalone directory (not a git repo — just files on disk)
agents resource add fs-directory local/acme-deploy </span>
--path /opt/deploy/acme-dashboard

What gets auto-discovered for each:

local/acme-app (type: git-checkout) discovers two children — a git and an fs-directory (the worktree root):

@startwbs
* local/acme-app\n(git-checkout / physical)
** local/acme-app:repo\n(git / physical)
*** acme-app:repo:origin\n(git-remote)\ngit@github.com:acmecorp/dashboard.git
*** acme-app:repo:v1.0.0\n(git-tag)
*** acme-app:repo:lib/shared\n(git-submodule @ c4d5e6f)
*** acme-app:repo:stash@0\n(git-stash)
*** acme-app:repo:main\n(git-branch)
**** main:a7f3e21\n(git-commit)
***** main:a7f3e21:tree\n(git-tree / root)
****** a7f3e21:README.md\n(git-tree-entry)
****** a7f3e21:package.json\n(git-tree-entry)
****** a7f3e21:src/\n(git-tree / subtree)
******* a7f3e21:src/app.ts\n(git-tree-entry)
******* a7f3e21:src/api.ts\n(git-tree-entry)
******* a7f3e21:src/utils.ts\n(git-tree-entry)
*** acme-app:repo:develop\n(git-branch)
**** develop:b2c4d8e\n(git-commit)
***** develop:b2c4d8e:tree\n(git-tree)
****** b2c4d8e:src/\n(git-tree)
******* b2c4d8e:src/app.ts\n(git-tree-entry)
******* b2c4d8e:src/api.ts\n(git-tree-entry / modified)
** local/acme-app:worktree\n(fs-directory / physical)\n/home/alice/projects/acme-dashboard/
*** worktree:src/\n(fs-directory)
**** worktree:src/app.ts\n(fs-file)
**** worktree:src/api.ts\n(fs-file)
**** worktree:src/utils.ts\n(fs-file)
*** worktree:package.json\n(fs-file)
*** worktree:README.md\n(fs-file)
*** worktree:docs -> ../docs\n(fs-symlink)
@endwbs

The git-checkout cleanly separates two concerns: the git child contains version control structure (remotes, branches, tags, stashes, submodules, commits, trees, and tree entries — git's full object model), while the fs-directory child is the worktree root directory containing the actual files on disk. Note how git's internal structure is fully modeled: git-commit → git-tree (root tree object) → git-tree-entry (blobs) and nested git-tree (subtrees). The worktree root is a directory (fs-directory), not a mount point — git-checkout does not own an fs-mount resource because a git checkout's worktree is just a directory on an existing filesystem. When content matches (as it does for a clean checkout), virtual types link the fs-file and git-tree-entry resources.

local/acme-upstream (type: git, remote URL — NOT checked out) discovers:

@startwbs
* local/acme-upstream\n(git / physical)\ngit@github.com:acmecorp/dashboard.git
** acme-upstream:origin\n(git-remote)
** acme-upstream:v1.0.0\n(git-tag)
** acme-upstream:main\n(git-branch)
*** main:a7f3e21\n(git-commit)
**** main:a7f3e21:tree\n(git-tree)
***** a7f3e21:src/\n(git-tree)
****** a7f3e21:src/app.ts\n(git-tree-entry)
****** a7f3e21:src/api.ts\n(git-tree-entry)
** acme-upstream:develop\n(git-branch)
*** ...\n(remaining structure)
@endwbs

A standalone git resource has full access to branches, tags, commits, trees, and tree entries — everything in the git object database — but no fs-directory child and no fs-file resources. There are no files on the local disk (the repo exists on GitHub's servers). Tools that need local file access cannot bind to it.

This is the key difference from git-checkout: a git resource represents a specific repo instance. Plans can reason about history, diffs between branches, remote relationships — without a local clone. A git-checkout adds the local worktree directory on top.

local/acme-deploy (type: fs-directory, standalone) discovers:

@startwbs
* local/acme-deploy\n(fs-directory / physical)\n/opt/deploy/acme-dashboard/
** acme-deploy:src/\n(fs-directory)
*** acme-deploy:src/app.ts\n(fs-file)
*** acme-deploy:src/api.ts\n(fs-file)
*** acme-deploy:src/utils.ts\n(fs-file)
** acme-deploy:package.json\n(fs-file)
** acme-deploy:README.md\n(fs-file)
@endwbs

A standalone fs-directory — no git metadata, no branches, no commits, no tree entries. Just a directory containing files and subdirectories. Uses the same fs-directory and fs-file types as the git checkout's worktree root.

Virtual resource linking across all three:

After all three resources are registered, the system detects equivalent physical resources and creates virtual parents to link them:

@startuml
skinparam defaultFontSize 10
skinparam objectFontSize 10
skinparam packageStyle rectangle
left to right direction

package "PHYSICAL: local/acme-app" as P1 #LightBlue {
  object "fs-directory" as acmeWtSrc {
    worktree:src/
  }
  object "fs-file" as acmeWtApp {
    worktree:src/app.ts
  }
  object "fs-file" as acmeWtUtils {
    worktree:src/utils.ts
  }
  object "fs-file" as acmeWtApi {
    worktree:src/api.ts
  }
  object "git-tree" as acmeGitSrc {
    main:a7f3e21:src/
  }
  object "git-tree-entry" as acmeGitApp {
    main:src/app.ts
  }
  object "git-tree-entry" as acmeGitUtils {
    main:src/utils.ts
  }
  object "git-tree-entry" as acmeGitApi {
    main:src/api.ts
  }
  object "git-commit" as acmeCommit {
    main:a7f3e21
  }
  object "git-branch" as acmeBranch {
    main
  }
  object "git-tag" as acmeTag {
    v1.0.0
  }
  object "git-remote" as acmeRemote {
    origin
  }
  object "git-tree" as acmeTree {
    main:a7f3e21:tree
  }
  object "git-submodule" as acmeSubmod {
    lib/shared
  }
}

package "PHYSICAL: local/acme-deploy" as P2 #LightGreen {
  object "fs-directory" as deploySrc {
    src/
  }
  object "fs-file" as deployApp {
    src/app.ts
  }
  object "fs-file" as deployUtils {
    src/utils.ts
  }
  object "fs-file" as deployApi {
    src/api.ts
  }
}

package "PHYSICAL: local/acme-upstream" as P3 #LightYellow {
  object "git-tree" as upstreamSrc {
    main:a7f3e21:src/
  }
  object "git-tree-entry" as upstreamApp {
    main:src/app.ts
  }
  object "git-tree-entry" as upstreamUtils {
    main:src/utils.ts
  }
  object "git-tree-entry" as upstreamApi {
    main:src/api.ts
  }
  object "git-commit" as upstreamCommit {
    main:a7f3e21
  }
  object "git-branch" as upstreamBranch {
    main
  }
  object "git-tag" as upstreamTag {
    v1.0.0
  }
  object "git-remote" as upstreamRemote {
    origin
  }
  object "git-tree" as upstreamTree {
    main:a7f3e21:tree
  }
}

package "VIRTUAL LAYER\n(auto-created by equivalence)" as VL #Lavender {
  object "directory" as vDir {
    src/ (merkle:3d4f...)
  }
  object "file" as vAppTs {
    app.ts (sha256:9f8e...)
  }
  object "file" as vUtilsTs {
    utils.ts (sha256:a2b1...)
  }
  object "file" as vApiTs {
    api.ts (sha256:e1d3...)
  }
  object "commit" as vCommit {
    a7f3e21
  }
  object "branch" as vBranch {
    main @ a7f3e21
  }
  object "tag" as vTag {
    v1.0.0
  }
  object "remote" as vRemote {
    github.com:acmecorp/dashboard.git
  }
  object "tree" as vTree {
    e8f1...9d2a
  }
  object "submodule" as vSubmod {
    lib/shared
  }
}

acmeWtSrc ..> vDir
deploySrc ..> vDir
acmeGitSrc ..> vDir
upstreamSrc ..> vDir

acmeWtApp ..> vAppTs
deployApp ..> vAppTs
acmeGitApp ..> vAppTs
upstreamApp ..> vAppTs

acmeWtUtils ..> vUtilsTs
deployUtils ..> vUtilsTs
acmeGitUtils ..> vUtilsTs
upstreamUtils ..> vUtilsTs

acmeWtApi ..> vApiTs
deployApi ..> vApiTs
acmeGitApi ..> vApiTs
upstreamApi ..> vApiTs

note "NOT linked: develop:b2c4d8e:src/api.ts\n(different content on develop branch)" as N1

acmeCommit ..> vCommit
upstreamCommit ..> vCommit

acmeBranch ..> vBranch
upstreamBranch ..> vBranch

acmeTag ..> vTag
upstreamTag ..> vTag

acmeRemote ..> vRemote
upstreamRemote ..> vRemote

acmeTree ..> vTree
upstreamTree ..> vTree

acmeSubmod ..> vSubmod
@enduml

How the directory virtual type works:

The directory virtual resource for src/ exists because the src/ directory has identical recursive content across multiple physical locations. Its children include both fs-directory resources (physical directories on disk) and git-tree resources (tree objects in git's object database). The system detects directory equivalence by computing a Merkle hash over the sorted child content hashes. If someone adds a file to the deploy directory but not the git checkout's worktree, the Merkle hashes diverge, and local/acme-deploy:src/ is unlinked from the directory virtual parent.

How the file virtual type works:

The file virtual type links physical resources that represent the same file — identical content bytes, filename, and permissions. It bridges across layers: an fs-file on disk and a git-tree-entry in git's tree are linked when they have matching content. This is the primary cross-layer bridge. When content diverges (e.g., an uncommitted edit), the virtual link is broken.

How commit works across repos:

When two git resources share history (e.g., a fork and its upstream), the same commit hash will appear in both repos' branches. The commit virtual type links these — the commit object a7f3e21 in the local repo and a7f3e21 in the upstream repo are the same commit, and the virtual parent captures this identity.

Divergence scenario:

If a developer edits src/api.ts in the deploy directory (/opt/deploy/acme-dashboard/src/api.ts), the system detects the content hash change and:

1. Unlinks local/acme-deploy:src/api.ts from file: api.ts (sha256:e1d3...8a9b) (content no longer matches).
2. Unlinks local/acme-deploy:src/ from directory: src/ (merkle:3d4f...a2b1) (directory contents no longer identical — the Merkle hash has changed).
3. The git checkout's worktree files and git-tree-entry resources remain linked (their content hasn't changed).
4. If the edit makes the deploy file match some other known content hash, a new virtual link may be created.

Additional resource types (databases, APIs, cloud infrastructure, etc.) can be added as custom resource types via agents resource type add.

Custom Resource Types

Custom resource types are defined in YAML configuration files and registered via agents resource type add. Once registered, a custom type automatically becomes available as a new subcommand under agents resource add.

# File: resource-types/database.yaml
cleveragents:
  version: "3.0"
resource_type:
name: local/database
description: "A SQL database (PostgreSQL, MySQL, SQLite, etc.)"
physical_or_virtual: physical
user_addable: true
  # CLI arguments for agents resource add local/database
cli_arguments:
- name: connection-string
type: string
required: true
description: "Database connection string (e.g., postgresql://host/dbname)"
validation:
pattern: "^(postgresql|mysql|sqlite)://"
- name: schema
type: string
required: false
description: "Default schema to use"
- name: read-only
type: boolean
required: false
default: false
description: "Whether the database should be treated as read-only"
  # Sandbox and handler
sandbox_strategy: transaction_rollback
handler: DatabaseHandler
checkpointable: true
  # Allowed parent types (empty means can be top-level)
allowed_parent_types: []
  # Child types
child_types:
- type: local/db-schema
auto_discover: true
manual_link: false
description: "Discovered database schemas"
- type: local/db-table
auto_discover: true
manual_link: false
description: "Discovered tables within schemas"
  # Capabilities
capabilities:
readable: true
writable: true
sandboxable: true
checkpointable: true

When this type is registered:

agents resource type add --config ./resource-types/database.yaml local/database
# Now available: agents resource add local/database <NAME> --connection-string CONN [--schema SCHEMA] [--read-only]

The user_addable field determines whether the type appears as a subcommand. Types with user_addable: false are only auto-generated as children — for example, git-remote, git-branch, git-commit, and git-tree-entry are never created directly by users but are discovered when a git (or git-checkout) resource is registered.

The Resource DAG

Resources form a directed acyclic graph (DAG), not a simple tree. A resource can have multiple parents and multiple children, subject to type constraints. The diagram below shows how a git-checkout, a standalone git repo (remote), a standalone fs-directory, and virtual resources interconnect:

@startuml
skinparam defaultFontSize 10
skinparam objectFontSize 10
skinparam packageStyle rectangle

package "GIT-CHECKOUT: local/app" as GCO #LightBlue {
  object "git-checkout" as gco {
    local/app
  }
  object "git" as gcoGit {
    local/app:repo
  }
  object "git-remote" as gcoRemote {
    origin
  }
  object "git-tag" as gcoTag {
    v1.0.0
  }
  object "git-branch" as gcoBranch {
    main
  }
  object "git-commit" as gcoCommit {
    a1b2c3d
  }
  object "git-tree" as gcoTree {
    e8f1... root
  }
  object "git-tree-entry" as gcoEntry1 {
    README.md
  }
  object "git-tree" as gcoSubtree {
    src/
  }
  object "git-tree-entry" as gcoEntry2 {
    src/app.ts
  }
  object "git-tree-entry" as gcoEntry3 {
    src/main.ts
  }
  object "fs-directory" as gcoFs {
    local/app:worktree
  }
  object "fs-directory" as gcoSrcDir {
    src/
  }
  object "fs-file" as gcoReadme {
    README.md
  }
  object "fs-file" as gcoApp {
    app.ts
  }
  object "fs-file" as gcoMain {
    main.ts
  }
}

package "GIT (remote): local/upstream" as GR #LightYellow {
  object "git" as gr {
    local/upstream
  }
  object "git-remote" as grRemote {
    origin
  }
  object "git-tag" as grTag {
    v1.0.0
  }
  object "git-branch" as grBranch {
    main
  }
  object "git-commit" as grCommit {
    a1b2c3d
  }
  object "git-tree" as grTree {
    e8f1... root
  }
  object "git-tree-entry" as grEntry1 {
    README.md
  }
  object "git-tree" as grSubtree {
    src/
  }
  object "git-tree-entry" as grEntry2 {
    src/app.ts
  }
  object "git-tree-entry" as grEntry3 {
    src/main.ts
  }
}

package "STANDALONE FS-DIRECTORY: local/deploy" as SFD #LightGreen {
  object "fs-directory" as sfd {
    /opt/deploy/myapp
  }
  object "fs-directory" as sfdSrcDir {
    src/
  }
  object "fs-file" as sfdReadme {
    README.md
  }
  object "fs-file" as sfdApp {
    app.ts
  }
  object "fs-file" as sfdMain {
    main.ts
  }
}

package "VIRTUAL LAYER" as VL #Lavender {
  object "file" as vFile {
    app.ts@v1
  }
  object "directory" as vDir {
    src/@v1
  }
  object "commit" as vCommit {
    a1b2c3d
  }
  object "branch" as vBranch {
    main@a1b...
  }
  object "tag" as vTag {
    v1.0.0
  }
  object "remote" as vRemote {
    github.com/org/repo
  }
  object "tree" as vTree {
    e8f1...9d2a
  }
}

' Physical hierarchy
gco --> gcoGit
gco --> gcoFs
gcoGit --> gcoRemote
gcoGit --> gcoTag
gcoGit --> gcoBranch
gcoBranch --> gcoCommit
gcoCommit --> gcoTree
gcoTree --> gcoEntry1
gcoTree --> gcoSubtree
gcoSubtree --> gcoEntry2
gcoSubtree --> gcoEntry3
gcoFs --> gcoSrcDir
gcoFs --> gcoReadme
gcoSrcDir --> gcoApp
gcoSrcDir --> gcoMain

gr --> grRemote
gr --> grTag
gr --> grBranch
grBranch --> grCommit
grCommit --> grTree
grTree --> grEntry1
grTree --> grSubtree
grSubtree --> grEntry2
grSubtree --> grEntry3

sfd --> sfdSrcDir
sfd --> sfdReadme
sfdSrcDir --> sfdApp
sfdSrcDir --> sfdMain

' Virtual equivalence links
gcoApp ..> vFile
sfdApp ..> vFile
gcoEntry2 ..> vFile
grEntry2 ..> vFile
gcoSrcDir ..> vDir
sfdSrcDir ..> vDir
gcoSubtree ..> vDir
grSubtree ..> vDir
gcoCommit ..> vCommit
grCommit ..> vCommit
gcoBranch ..> vBranch
grBranch ..> vBranch
gcoTag ..> vTag
grTag ..> vTag
gcoRemote ..> vRemote
grRemote ..> vRemote
gcoTree ..> vTree
grTree ..> vTree
@enduml

Key properties of the DAG:

1. Multiple parents: A git-tree-entry has a git-tree parent (git structure) and potentially a file virtual parent (content identity). An fs-file has an fs-directory parent (filesystem hierarchy) and potentially a file virtual parent. An fs-directory can be a child of git-checkout (as its worktree root), a child of fs-mount (as the mount root), a child of another fs-directory (as a subdirectory), a child of a directory virtual (identity), or a standalone user-registered resource.
2. Multiple children: A git-checkout has a git child and an fs-directory child. A git resource has git-remote, git-branch, git-tag, git-stash, git-submodule, and git-commit children. A git-commit has a git-tree child. A git-tree has git-tree-entry and nested git-tree children.
3. Cycles are forbidden: The graph is always a DAG. The system validates this when links are created.
4. Type constraints: Not any resource can be a child of any other — the parent's resource type defines which child types are allowed (see the Allowed Parents / Allowed Children columns in the built-in types tables).
5. Cross-layer bridge: Virtual resources link equivalent physical resources from different layers (git structure, filesystem, different repos). The file virtual type bridges fs-file + git-tree-entry. The directory virtual type bridges fs-directory + git-tree. The commit, branch, tag, remote, submodule, and tree virtual types link the same git structural element across different repositories.

Physical vs Virtual Resources

Resources are either physical or virtual, a distinction determined by their resource type.

Physical resources are specific, concrete manifestations. Each physical resource is a particular instance that exists somewhere — a file at a particular path on a particular machine, a git repository at a particular URL on a particular server, a commit in a particular repo. Physical resources can be directly read and written by tools. Most resources are physical.

Virtual resources represent an abstract identity that links equivalent physical resources. They answer the question: "where else does this same thing exist?" A file virtual resource links all the physical fs-file and git-tree-entry resources that have the same content, filename, and permissions. A commit virtual resource links git-commit resources across repos that share the same commit hash. Virtual resources have no location of their own and cannot be directly read or written.

Rules for the physical/virtual boundary:

1. The children of a virtual resource can be physical resources, other virtual resources, or a combination of both.
2. A physical resource's parents can be either physical or virtual resources.
3. Not all physical resources need a virtual parent. Virtual resource linking is optional and only applies when equivalence tracking is meaningful.

Equivalence linking:

Two physical resources share a virtual parent when they are equivalent by the virtual type's criteria. Each virtual type has its own equivalence semantics:

• file: Physical files (fs-file or git-tree-entry) share a file parent when they have the same content bytes (SHA-256), the same filename, and the same permissions. This is the primary cross-layer bridge — it links filesystem files with git blob entries. Example: local/app:worktree:src/main.py (an fs-file) and local/app:repo:main:a1b...:src/main.py (a git-tree-entry) both have the same content in a clean checkout, so they share a file virtual parent.

• directory: Physical directories (fs-directory) and git trees (git-tree) share a directory parent when their full recursive contents are equivalent. This bridges the filesystem and git layers — the src/ directory on disk and the src/ subtree in git's tree object can be linked when their contents match.

• symlink: Physical symlinks (fs-symlink) and git tree entries with symlink mode (git-tree-entry mode 120000) share a symlink parent when they have the same name and target.

• commit: Physical commits (git-commit) in different repos share a commit parent when they have the same commit hash. This is common when repos share history (e.g., a fork and its upstream).

• branch: Physical branches (git-branch) in different repos share a branch parent when they have the same name and the same HEAD commit hash. A push or local commit on one repo causes divergence.

• tag: Physical tags (git-tag) in different repos share a tag parent when they have the same tag name and target object.

• remote: Physical remotes (git-remote) in different repos share a remote parent when they point to the same URL. Answers: "these repos share the same upstream."

• submodule: Physical submodules (git-submodule) in different repos share a submodule parent when they have the same submodule URL and path.

• tree: Physical tree objects (git-tree) in different commits or repos share a tree parent when they have the same tree hash — identical directory structure and contents.

Divergence detection:

When a physical resource's content changes (e.g., a file is edited, a directory gains a new file, a branch advances), the system detects that it may no longer match its virtual parent's identity. At that point:

• If the edit causes the physical resource to diverge from all other physical siblings under the same virtual parent, the physical resource is unlinked from that virtual parent.
• If the edit makes it match a different virtual resource's identity, it may be re-linked.
• Content equivalence is tracked via content hashing: SHA-256 for files, Merkle hashes for directories, tree hashes for git trees, commit hashes for commits, HEAD+name for branches, name+target for tags, URL for remotes.
• Cascading divergence: When a file link breaks, the parent directory virtual resource is also re-evaluated (since its identity depends on all child identities).

This enables powerful queries like:

• "What physical locations exist for this file content?" → Find all physical resources sharing the file virtual parent.
• "What tools can edit this file?" → Find all tools with resource bindings compatible with the physical resource types.
• "Has this file been modified in any location?" → Check if all physical siblings still share the same file virtual parent.
• "Are these two repos in sync?" → Check if their branches share a branch virtual parent.
• "Which directories are identical across deployments?" → Find directory virtual resources with multiple physical children.
• "What submodules are shared across projects?" → Find submodule virtual resources with multiple physical children.

Resource Registration (CLI)

Resources are created via agents resource add <type> with type-specific arguments. Auto-discovered children are created automatically:

# Register a checked-out git repository (most common)
agents resource add git-checkout local/api-repo --path /home/user/projects/api-service --branch main
# Auto-discovers: git child (repo metadata, remotes, branches, commits, tree entries)
#                 + fs-directory child (worktree root directory, subdirectories, files)
# Register a git repo via remote URL (no local checkout — exists on remote server)
agents resource add git local/upstream --url git@github.com:org/upstream.git
# Auto-discovers: remotes, branches, tags, commits, trees, tree entries, stashes, submodules
#                 (no fs-directory — not checked out locally)
# Register a standalone directory (not a git repo — just files on disk)
agents resource add fs-directory local/docs --path /opt/docs/api-reference
# Auto-discovers: subdirectories, files, symlinks, hardlinks
# Register a standalone filesystem mount (entire volume)
agents resource add fs-mount local/data-volume --mount-path /mnt/data
# Auto-discovers: root fs-directory, subdirectories, files, symlinks, hardlinks
# Link resources to projects (resources must be registered first)
agents project link-resource local/api-service local/api-repo
agents project link-resource local/api-service local/docs --read-only

Auto-Discovery

When a resource is registered, its resource type's handler auto-discovers child resources. This process:

1. Scans the resource to identify children (e.g., a git-checkout handler creates a git child and an fs-directory child for the worktree root; a git handler lists remotes, branches, tags, commits, stashes, and submodules; a git-commit handler creates a root git-tree child; a git-tree handler lists tree entries and subtrees; an fs-mount handler creates a root fs-directory and discovers its contents; an fs-directory handler discovers subdirectories, files, symlinks, and hardlinks).
2. Creates child resource records in the Resource Registry with auto-generated names (e.g., local/api-repo:repo for the git child, local/api-repo:worktree for the worktree root fs-directory child, local/api-repo:repo:main for a branch).
3. Reuses existing resources: If a discovered child matches an already-registered resource (same type + same value/location), the existing resource is linked as a child rather than creating a duplicate. For example, if local/docs (an fs-directory resource at /home/user/projects/api-service) is already registered and the git-checkout handler discovers its worktree root at the same path, it links to the existing local/docs resource instead of creating a new one.
4. Links virtual resources: When auto-discovery detects that a physical resource's content matches an existing virtual resource (via content hashing), it links the physical resource as a child of the virtual resource.

Auto-discovered children are marked as auto: true in the DAG, distinguishing them from manually linked children. Auto-discovered links cannot be manually unlinked (they are managed by the handler), while manually created links can be freely managed.

Auto-discovery runs:

• At registration time (agents resource add)
• On refresh (when the system detects changes, e.g., new commits, new files)
• On demand (when a tool accesses the resource and the handler detects staleness)

Resource Capabilities

Each resource declares its capabilities, derived from its resource type:

Capability	Description
readable	Whether the resource can be read
writable	Whether the resource can be modified
sandboxable	Whether the resource supports sandbox isolation
checkpointable	Whether the resource supports checkpoint/rollback

These capabilities are used by the tool execution flow to validate that a tool's resource binding is compatible — a tool declaring access: read_write on a resource slot cannot be bound to a read-only resource.

Resource Registry

CleverAgents maintains a Resource Registry — a persistent catalog of all registered resources and their DAG relationships:

@startuml
skinparam classAttributeIconSize 0
skinparam classFontSize 13
skinparam defaultFontSize 12

class ResourceRegistry {
  - resourceIndex : Map<String, ResourceRecord>
  - typeIndex : Map<String, ResourceTypeRecord>
  --
  + add(type, name, properties) : ResourceRecord
  + update(name, properties) : ResourceRecord
  + remove(name) : void
  + lookup(name) : ResourceRecord
  + list(filters) : ResourceRecord[]
  + tree(name, depth) : DAG_subtree
  + link_child(parent, child) : void
  + unlink_child(parent, child) : void
  + refresh(name) : void
  + find_by_content(hash) : ResourceRecord[]
  + find_virtual_parent(resource) : ResourceRecord
}

class ResourceRecord {
  + name : String
  + type : String
  + physical_or_virtual : PhysVirt
  + properties : Map<String, Object>
  + capabilities : Capabilities
  + parents : List<ResourceRecord>
  + children : List<ResourceRecord>
  + content_hash : String
  + linked_projects : List<String>
  + created_at : DateTime
  + updated_at : DateTime
}

class ResourceTypeRecord {
  + name : String
  + description : String
  + source : String
  + physical_or_virtual : PhysVirt
  + user_addable : Boolean
  + cli_arguments : List<CLIArgument>
  + allowed_parent_types : List<String>
  + child_types : Map<String, ChildTypeConfig>
  + sandbox_strategy : String
  + handler : String
  + capabilities : Capabilities
  + config_path : String
}

enum PhysVirt {
  physical
  virtual
}

ResourceRegistry "1" *-- "0..*" ResourceRecord : indexes resources >
ResourceRegistry "1" *-- "0..*" ResourceTypeRecord : indexes types >
ResourceRecord --> PhysVirt
ResourceTypeRecord --> PhysVirt

note right of ResourceRegistry
  **Populated by:**
  - agents resource add CLI
  - Auto-discovery during registration
  - Content-identity linking

  **Consumed by:**
  - Tool binding resolution
  - Sandbox creation
  - Change tracking
  - Project linking
  - Plan validation
end note
@enduml

The Resource Registry persists in the database (local SQLite or server). It works alongside the Tool Registry and Skill Registry.

Resource Sandbox Strategy

Each resource defines its own sandbox strategy, determined by its resource type. This is critical because:

1. The same resource may be accessed through different tools and skills
2. Different resource types require different sandboxing approaches
3. Some resources cannot be sandboxed at all

Resource Type	Sandbox Strategy	Rollback Mechanism
git-checkout	git_worktree	Git reset/checkout
git	none	N/A (represents a repo instance — not directly sandboxable)
fs-mount	copy_on_write or overlay	Restore from snapshot
fs-directory	copy_on_write	Restore from snapshot
Custom database types	transaction_rollback	Transaction rollback
Custom API types	none (often not sandboxable)	N/A

The sandbox strategy is inherited by child resources from their parent unless the child type defines its own. For example, git-branch, git-commit, git-tree, and git-tree-entry all inherit from their git ancestor. fs-file, fs-symlink, and fs-hardlink inherit from their fs-directory parent.

Lazy Sandboxing

Resources are sandboxed lazily when accessed, not upfront. This is different from indexing — resources are indexed immediately when registered, but sandboxes are only created when execution needs to modify a resource:

1. A project may link many resources (e.g., git repo + databases + cloud accounts)
2. A plan may only need to modify one resource
3. Only the accessed resources are sandboxed
4. Each plan/child plan has its own sandbox containing only edited resources

This is efficient for large projects where most resources remain untouched.

Resource Access Tracking

The system tracks which tools access which resources through the tool-resource binding system. This tracking happens at multiple levels:

1. Declaration-time: Tool YAML declares resource slots with type and access mode (see Resource Bindings in the Tools section).
2. Activation-time: When a tool is activated for a plan, its resource slots are bound to specific resources. The system records these bindings.
3. Execution-time: Every tool invocation logs which bound resources were actually accessed, what operations were performed (read/write/delete), and what paths or objects were touched.

This enables:

• Accurate sandbox scoping: Only sandbox resources that will actually be modified.
• Rollback feasibility analysis: Know exactly which resources were modified and whether they support rollback.
• Security auditing: Complete record of which tools accessed which resources and how.
• Cross-resource impact analysis: Determine if changes to a resource affect tools bound to sibling or child resources.
• Virtual resource consistency: Detect when a physical resource diverges from its virtual parent.

Unified Resource Abstraction Layer

CleverAgents provides a unified abstraction that allows tools to work with any resource type through a consistent interface. This enables:

1. Resource-agnostic tools: A tool like read_content(path) works whether the path refers to a file, database record, or API endpoint.
2. Consistent sandbox semantics: All resources support the same sandbox lifecycle (create, read, write, checkpoint, rollback).
3. Pluggable resource handlers: New resource types can be added by registering custom resource types without modifying existing tools.
4. Unified change tracking: All resource modifications flow into the same ChangeSet model.

Resource Handler Interface

Every resource type provides a handler that implements this interface:

class ResourceHandler(Protocol):
    """Handler for a specific resource type."""
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">read</span>(self, path: <span style="color: cyan;">str</span>, sandbox: Sandbox) -&gt; Content:
    <span style="color: #66cc66;">&quot;&quot;&quot;Read content from the sandboxed resource.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">write</span>(self, path: <span style="color: cyan;">str</span>, content: Content, sandbox: Sandbox) -&gt; Change:
    <span style="color: #66cc66;">&quot;&quot;&quot;Write content and return the Change record.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">delete</span>(self, path: <span style="color: cyan;">str</span>, sandbox: Sandbox) -&gt; Change:
    <span style="color: #66cc66;">&quot;&quot;&quot;Delete resource and return the Change record.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;"><span style="color: cyan;">list</span></span>(self, pattern: <span style="color: cyan;">str</span>, sandbox: Sandbox) -&gt; <span style="color: cyan;">list</span>[<span style="color: cyan;">str</span>]:
    <span style="color: #66cc66;">&quot;&quot;&quot;List paths matching pattern.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">diff</span>(self, path: <span style="color: cyan;">str</span>, sandbox: Sandbox) -&gt; <span style="color: cyan;">str</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Generate diff between sandbox and original state.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">supports_operation</span>(self, operation: OperationType) -&gt; <span style="color: cyan;">bool</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Check if this resource supports the given operation.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">discover_children</span>(self, resource: ResourceRecord) -&gt; <span style="color: cyan;">list</span>[ResourceRecord]:
    <span style="color: #66cc66;">&quot;&quot;&quot;Auto-discover child resources (called at registration and refresh).&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">content_hash</span>(self, path: <span style="color: cyan;">str</span>, sandbox: Sandbox) -&gt; <span style="color: cyan;">str</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Compute content hash for identity tracking.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">create_sandbox</span>(self, resource: ResourceRecord) -&gt; Sandbox:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a sandbox for this resource using its type&#x27;s strategy.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">create_checkpoint</span>(self, sandbox: Sandbox) -&gt; Checkpoint:
    <span style="color: #66cc66;">&quot;&quot;&quot;Create a checkpoint within the sandbox.&quot;&quot;&quot;</span>
    ...

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">rollback_to</span>(self, sandbox: Sandbox, checkpoint: Checkpoint) -&gt; <span style="color: magenta; font-weight: 600;">None</span>:
    <span style="color: #66cc66;">&quot;&quot;&quot;Roll back sandbox state to a checkpoint.&quot;&quot;&quot;</span>
    ...

Built-in Resource Handlers

Resource Type	Handler	Read	Write	Delete	Sandbox Strategy
git-checkout	GitCheckoutHandler	✓	✓	✓	git_worktree
git	GitHandler	✓	✗	✗	none
git-commit, git-tree, git-tree-entry	GitObjectHandler	✓	✗	✗	(inherits)
git-branch, git-tag, git-stash	GitRefHandler	✓	✓	✓	(inherits)
git-remote, git-submodule	GitConfigHandler	✓	✗	✗	(inherits)
fs-mount	FilesystemHandler	✓	✓	✓	copy_on_write
fs-directory	FilesystemHandler	✓	✓	✓	copy_on_write
fs-file, fs-symlink, fs-hardlink	FilesystemHandler	✓	✓	✓	(inherits)

Additional handlers are provided by custom resource types when they are registered.

Resource Path Resolution

Paths in tool invocations are resolved through a resource routing system that uses tool-resource bindings to determine the target resource:

sequenceDiagram
    participant Tool as Tool Invocation
    participant Router as Resource Router
    participant Handler as GitCheckoutHandler

    Tool->>Router: edit_file(path='src/main.py', ...)
    Router->>Router: Check tool's resource bindings
    Note right of Router: slot 'repo' bound to<br/>local/api-repo (git-checkout)
    Router->>Router: Resolve path within bound resource
    Note right of Router: local/api-repo:worktree:src/main.py
    Router->>Handler: Route to resource handler
    Handler->>Handler: Resolve path to sandbox worktree
    Handler->>Handler: Operate on sandboxed state
    Handler-->>Tool: Return Change record

When a tool has multiple resource slots bound, the path scheme or slot name disambiguates:

# Explicit slot reference
path://repo/src/main.py      → routes to the "repo" slot's bound resource
path://docs/api/readme.md    → routes to the "docs" slot's bound resource
Default: unqualified paths route to the tool's primary resource slot
src/main.py                  → routes to the first (or only) resource slot

Code Intelligence & Context Discovery

Overview

CleverAgents employs a sophisticated multi-layered indexing and discovery system that enables agents to efficiently navigate and understand codebases of any scale. This system goes far beyond simple text search, providing semantic understanding of code structure, dependencies, and relationships through a combination of indexed embeddings, vector search, and an RDF-based graph store.

Critical Design Decision: All indexing happens immediately when resources are added to projects or when code changes. There is no "on-demand" indexing during agent execution. This ensures that agents always have instant access to search capabilities without any indexing delays. The computational cost is paid once upfront, not repeatedly during agent operations.

Key Design Principles:

1. Pluggable Architecture: Every component can be extended or replaced
2. Progressive Enhancement: System works with basic text search, enhances with advanced features
3. Eager Indexing: Indices are built immediately when resources are added and kept continuously up-to-date
4. Agent Awareness: Agents understand available indices through skills
5. Real-time Synchronization: Indices update immediately as code changes

Architecture Components

1. Multi-Modal Indexing Engine

The indexing engine operates across three complementary modalities:

IndexingEngine:
  modalities:
    # Traditional text-based indexing
    text_index:
      type: "full_text_search"
      backend: "tantivy" | "elasticsearch" | "sqlite_fts"
      features:
        - Token-based search
        - Regex patterns
        - Language-aware tokenization
        - File path indexing
    # Semantic understanding via embeddings
vector_index:
type: "embedding_search"
backend: "faiss" | "qdrant" | "weaviate" | "pgvector"
models:
- code: "codegen-6B-multi"
- docs: "instructor-xl"
- cross-modal: "clip-code"
features:
- Function-level embeddings
- Class-level embeddings
- Module-level embeddings
- Documentation embeddings
- Cross-language similarity
    # Structural understanding via graph
graph_index:
type: "rdf_knowledge_graph"
backend: "blazegraph" | "stardog" | "apache_jena" | "neo4j"
ontology: "CodeOntology"
features:
- AST-based relationships
- Dependency graphs
- Call graphs
- Inheritance hierarchies
- Data flow analysis

2. RDF-Based Code Knowledge Graph

The graph store represents code as a rich semantic network using RDF (Resource Description Framework) triples. This enables sophisticated queries about code structure and relationships.

Core Ontology Design:

# CodeOntology - Core vocabulary for code representation
@prefix code: <https://cleveragents.ai/ontology/code#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
# Core Classes
code:Module a rdfs:Class ;
rdfs:comment "A code module (file, package, namespace)" .
code:Class a rdfs:Class ;
rdfs:comment "A class or similar construct" .
code:Function a rdfs:Class ;
rdfs:comment "A function, method, or procedure" .
code:Variable a rdfs:Class ;
rdfs:comment "A variable, constant, or field" .
code:Type a rdfs:Class ;
rdfs:comment "A type definition" .
# Core Properties
code:contains a rdf:Property ;
rdfs:domain code:Module ;
rdfs:range code:Entity ;
rdfs:comment "Module contains entity" .
code:imports a rdf:Property ;
rdfs:domain code:Module ;
rdfs:range code:Module ;
rdfs:comment "Module imports another module" .
code:extends a rdf:Property ;
rdfs:domain code:Class ;
rdfs:range code:Class ;
rdfs:comment "Class inheritance relationship" .
code:calls a rdf:Property ;
rdfs:domain code:Function ;
rdfs:range code:Function ;
rdfs:comment "Function calls another function" .
code:references a rdf:Property ;
rdfs:domain code:Entity ;
rdfs:range code:Entity ;
rdfs:comment "Entity references another entity" .
code:hasParameter a rdf:Property ;
rdfs:domain code:Function ;
rdfs:range code:Parameter ;
rdfs:comment "Function has parameter" .
code:returns a rdf:Property ;
rdfs:domain code:Function ;
rdfs:range code:Type ;
rdfs:comment "Function return type" .
# Annotations
code:hasDocstring a rdf:Property ;
rdfs:domain code:Entity ;
rdfs:range xsd:string .
code:hasComplexity a rdf:Property ;
rdfs:domain code:Function ;
rdfs:range xsd:integer ;
rdfs:comment "Cyclomatic complexity" .
code:hasTestCoverage a rdf:Property ;
rdfs:domain code:Entity ;
rdfs:range xsd:decimal ;
rdfs:comment "Test coverage percentage" .

Example Knowledge Graph Fragment:

# Concrete example: Authentication module
<file:///src/auth/auth_manager.py> a code:Module ;
    code:imports <file:///src/core/user.py> ;
    code:imports <file:///src/utils/crypto.py> ;
    code:contains <class://AuthManager> .
<class://AuthManager> a code:Class ;
code:hasMethod <method://AuthManager.authenticate> ;
code:hasMethod <method://AuthManager.validate_token> ;
code:extends <class://BaseManager> ;
code:hasDocstring "Manages user authentication and session tokens" .
<method://AuthManager.authenticate> a code:Function ;
code:hasParameter <param://username> ;
code:hasParameter <param://password> ;
code:returns <type://AuthToken> ;
code:calls <method://CryptoUtils.hash_password> ;
code:calls <method://UserDB.find_user> ;
code:hasComplexity 8 ;
code:hasTestCoverage 0.95 .

Advanced Graph Queries:

# Find all functions that manipulate user authentication
PREFIX code: <https://cleveragents.ai/ontology/code#>
SELECT ?function ?module
WHERE {
    ?function a code:Function ;
              code:calls*/code:references ?entity .
    ?entity rdfs:label ?label .
    FILTER(CONTAINS(LCASE(?label), "auth") || CONTAINS(LCASE(?label), "user"))
    ?module code:contains ?function .
}
# Find circular dependencies
SELECT ?module1 ?module2
WHERE {
?module1 code:imports+ ?module2 .
?module2 code:imports+ ?module1 .
FILTER(?module1 != ?module2)
}
# Find most complex untested functions
SELECT ?function ?complexity
WHERE {
?function a code:Function ;
code:hasComplexity ?complexity ;
code:hasTestCoverage ?coverage .
FILTER(?complexity > 10 && ?coverage < 0.5)
}
ORDER BY DESC(?complexity)
LIMIT 10

3. Intelligent Context Assembly Pipeline

The context assembly pipeline leverages all three indices to build optimal context for each agent:

class ContextAssemblyPipeline:
    def assemble_context(self, 
                        query: str, 
                        actor_type: str,
                        resource_scope: List[Resource],
                        max_tokens: int) -> Context:
    <span style="opacity: 0.7;"># Stage 1: Query Understanding</span>
    intent = self.analyze_query_intent(query)
    entities = self.extract_entities(query)  <span style="opacity: 0.7;"># Classes, functions, concepts</span>
    
    <span style="opacity: 0.7;"># Stage 2: Multi-Modal Search</span>
    results = SearchResults()
    
    <span style="opacity: 0.7;"># Text search for exact matches</span>
    <span style="color: magenta; font-weight: 600;">if</span> intent.needs_exact_match:
        text_results = self.text_index.search(
            query=query,
            filters={<span style="color: #66cc66;">&quot;resources&quot;</span>: resource_scope},
            limit=100
        )
        results.add(text_results)
    
    <span style="opacity: 0.7;"># Vector search for semantic similarity</span>
    <span style="color: magenta; font-weight: 600;">if</span> intent.needs_semantic_match:
        query_embedding = self.embed_query(query, actor_type)
        vector_results = self.vector_index.search(
            embedding=query_embedding,
            filters={<span style="color: #66cc66;">&quot;resources&quot;</span>: resource_scope},
            limit=50
        )
        results.add(vector_results)
    
    <span style="opacity: 0.7;"># Graph traversal for structural relationships</span>
    <span style="color: magenta; font-weight: 600;">if</span> entities:
        graph_results = self.graph_index.traverse(
            start_nodes=entities,
            patterns=self.get_patterns_for_actor(actor_type),
            max_depth=3,
            limit=50
        )
        results.add(graph_results)
    
    <span style="opacity: 0.7;"># Stage 3: Relevance Ranking</span>
    ranked_results = self.rank_by_relevance(
        results=results,
        actor_type=actor_type,
        query_intent=intent
    )
    
    <span style="opacity: 0.7;"># Stage 4: Context Optimization</span>
    context = self.optimize_context(
        ranked_results=ranked_results,
        max_tokens=max_tokens,
        strategy=self.get_strategy_for_actor(actor_type)
    )
    
    <span style="color: magenta; font-weight: 600;">return</span> context

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">get_patterns_for_actor</span>(self, actor_type: <span style="color: cyan;">str</span>) -&gt; <span style="color: cyan;">List</span>[GraphPattern]:
    <span style="color: #66cc66;">&quot;&quot;&quot;Different actors need different traversal patterns&quot;&quot;&quot;</span>
    patterns = {
        <span style="color: #66cc66;">&quot;strategist&quot;</span>: [
            <span style="color: #66cc66;">&quot;module_dependencies&quot;</span>,      <span style="opacity: 0.7;"># Understand architecture</span>
            <span style="color: #66cc66;">&quot;interface_boundaries&quot;</span>,     <span style="opacity: 0.7;"># Find API surfaces</span>
            <span style="color: #66cc66;">&quot;test_coverage_gaps&quot;</span>        <span style="opacity: 0.7;"># Identify risks</span>
        ],
        <span style="color: #66cc66;">&quot;executor&quot;</span>: [
            <span style="color: #66cc66;">&quot;implementation_details&quot;</span>,   <span style="opacity: 0.7;"># Get full function bodies</span>
            <span style="color: #66cc66;">&quot;local_dependencies&quot;</span>,       <span style="opacity: 0.7;"># Find what to change</span>
            <span style="color: #66cc66;">&quot;usage_patterns&quot;</span>           <span style="opacity: 0.7;"># Understand call sites</span>
        ],
        <span style="color: #66cc66;">&quot;reviewer&quot;</span>: [
            <span style="color: #66cc66;">&quot;change_impact_analysis&quot;</span>,   <span style="opacity: 0.7;"># What could break</span>
            <span style="color: #66cc66;">&quot;similar_patterns&quot;</span>,        <span style="opacity: 0.7;"># Consistency checks</span>
            <span style="color: #66cc66;">&quot;test_relationships&quot;</span>       <span style="opacity: 0.7;"># Verification paths</span>
        ]
    }
    <span style="color: magenta; font-weight: 600;">return</span> patterns.get(actor_type, [<span style="color: #66cc66;">&quot;general_traversal&quot;</span>])

4. Plugin Architecture for Extensibility

The system is designed for extensibility at every level:

PluginSystem:
  # Language-specific analyzers
  analyzers:
    python:
      class: "PythonAnalyzer"
      features:
        - AST parsing via ast module
        - Type inference via mypy
        - Import resolution
        - Docstring extraction
<span style="color: cyan; font-weight: 600;">typescript</span>:
  <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;TypeScriptAnalyzer&quot;</span>
  <span style="color: cyan; font-weight: 600;">features</span>:
    - TSC-based parsing
    - Type extraction
    - Module resolution
    - JSDoc parsing

<span style="color: cyan; font-weight: 600;">rust</span>:
  <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;RustAnalyzer&quot;</span>
  <span style="color: cyan; font-weight: 600;">features</span>:
    - rust-analyzer integration
    - Lifetime analysis
    - Trait resolution
    - Macro expansion

    # Custom analyzer example
custom_dsl:
class: "CustomDSLAnalyzer"
config:
grammar: "path/to/grammar.peg"
semantic_rules: "path/to/rules.yaml"
  # Index backend providers
backends:
graph:
- name: "blazegraph"
class: "BlazegraphBackend"
scalability: "billions of triples"
features: ["SPARQL", "reasoning", "geospatial"]
  - <span style="color: cyan;">name</span>: <span style="color: #66cc66;">&quot;neo4j&quot;</span>
    <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;Neo4jBackend&quot;</span>
    <span style="color: cyan; font-weight: 600;">scalability</span>: <span style="color: #66cc66;">&quot;enterprise&quot;</span>
    <span style="color: cyan; font-weight: 600;">features</span>: [&quot;Cypher&quot;, &quot;APOC&quot;, &quot;GDS&quot;]
  
  - <span style="color: cyan;">name</span>: <span style="color: #66cc66;">&quot;custom_graph&quot;</span>
    <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;MyCustomGraphDB&quot;</span>
    <span style="color: cyan; font-weight: 600;">config</span>:
      <span style="color: cyan; font-weight: 600;">connection</span>: <span style="color: #66cc66;">&quot;custom://localhost:7687&quot;</span>

<span style="color: cyan; font-weight: 600;">vector</span>:
  - <span style="color: cyan;">name</span>: <span style="color: #66cc66;">&quot;faiss&quot;</span>
    <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;FaissBackend&quot;</span>
    <span style="color: cyan; font-weight: 600;">scalability</span>: <span style="color: #66cc66;">&quot;100M vectors&quot;</span>
    <span style="color: cyan; font-weight: 600;">features</span>: [&quot;GPU acceleration&quot;, &quot;HNSW&quot;]
  
  - <span style="color: cyan;">name</span>: <span style="color: #66cc66;">&quot;qdrant&quot;</span>
    <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;QdrantBackend&quot;</span>
    <span style="color: cyan; font-weight: 600;">scalability</span>: <span style="color: #66cc66;">&quot;distributed&quot;</span>
    <span style="color: cyan; font-weight: 600;">features</span>: [&quot;filtering&quot;, &quot;payloads&quot;, &quot;snapshots&quot;]

  # Embedding model providers
embedders:
- name: "openai"
class: "OpenAIEmbedder"
models: ["text-embedding-3-large", "text-embedding-3-small"]
- <span style="color: cyan;">name</span>: <span style="color: #66cc66;">&quot;local&quot;</span>
  <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;LocalEmbedder&quot;</span>
  <span style="color: cyan; font-weight: 600;">models</span>: [&quot;all-MiniLM-L6-v2&quot;, &quot;instructor-xl&quot;]

- <span style="color: cyan;">name</span>: <span style="color: #66cc66;">&quot;custom&quot;</span>
  <span style="color: cyan; font-weight: 600;">class</span>: <span style="color: #66cc66;">&quot;MyFineTunedEmbedder&quot;</span>
  <span style="color: cyan; font-weight: 600;">model_path</span>: <span style="color: #66cc66;">&quot;path/to/model&quot;</span>

5. Agent Skills for Code Intelligence

Agents interact with the code intelligence system through a specialized skill (local/code-intelligence) that contains tools for semantic search, dependency analysis, and refactoring:

# Actor references the code intelligence skill
actors:
  code_explorer:
    type: llm
    config:
      actor: anthropic/claude-3-opus
    skills:
      - local/code-intelligence   # Provides search_code_semantically,
                                  # analyze_dependencies, suggest_refactoring_targets

The local/code-intelligence skill uses anonymous inline tools to wrap the code intelligence subsystem. These tools are defined inline because they are tightly coupled to the skill's specific implementation and not reused elsewhere (if any were needed in multiple contexts, they would be registered independently via agents tool add and referenced by name):

# File: skills/code-intelligence.yaml
skill:
  name: local/code-intelligence
  description: "Semantic code search, dependency analysis, and refactoring recommendations"
anonymous_tools:
- name: search_code_semantically
description: "Find code similar to a concept using vector search, enriched with dependency context"
input_schema:
type: object
properties:
query: { type: string }
scope: { type: string, default: "all" }
limit: { type: integer, default: 10 }
required: [query]
capability:
read_only: true
code: |
results = ctx.code_intelligence.vector_search(
query=params["query"],
scope=params.get("scope", "all"),
limit=params.get("limit", 10)
)
for result in results:
result.context = ctx.code_intelligence.get_dependencies(
entity=result.entity_id, depth=2
)
return results
- <span style="color: cyan;">name</span>: analyze_dependencies
  <span style="color: cyan; font-weight: 600;">description</span>: <span style="color: #66cc66;">&quot;Analyze module dependencies using graph store&quot;</span>
  <span style="color: cyan; font-weight: 600;">input_schema</span>:
    <span style="color: cyan; font-weight: 600;">type</span>: object
    <span style="color: cyan; font-weight: 600;">properties</span>:
      <span style="color: cyan; font-weight: 600;">module_path</span>: { type: string }
    <span style="color: cyan; font-weight: 600;">required</span>: [module_path]
  <span style="color: cyan; font-weight: 600;">capability</span>:
    <span style="color: cyan; font-weight: 600;">read_only</span>: <span style="color: magenta; font-weight: 600;">true</span>
  <span style="color: cyan; font-weight: 600;">code</span>: <span style="color: #66cc66;">|</span>
    module = params[&quot;module_path&quot;]
    deps = ctx.code_intelligence.graph_query(f&#x27;&#x27;&#x27;
        PREFIX code: &lt;https://cleveragents.ai/ontology/code#&gt;
        SELECT ?dep ?type WHERE {{
            &lt;{module}&gt; code:imports* ?dep . ?dep a ?type .
        }}
    &#x27;&#x27;&#x27;)
    return {
        &quot;direct_deps&quot;: len([d for d in deps if d.distance == 1]),
        &quot;transitive_deps&quot;: len(deps),
        &quot;circular_deps&quot;: ctx.code_intelligence.find_circular_deps(module),
        &quot;dependency_graph&quot;: deps
    }

- <span style="color: cyan;">name</span>: suggest_refactoring_targets
  <span style="color: cyan; font-weight: 600;">description</span>: <span style="color: #66cc66;">&quot;Combine text, graph, and vector search to find refactoring targets&quot;</span>
  <span style="color: cyan; font-weight: 600;">input_schema</span>:
    <span style="color: cyan; font-weight: 600;">type</span>: object
    <span style="color: cyan; font-weight: 600;">properties</span>:
      <span style="color: cyan; font-weight: 600;">scope</span>: { type: string }
    <span style="color: cyan; font-weight: 600;">required</span>: [scope]
  <span style="color: cyan; font-weight: 600;">capability</span>:
    <span style="color: cyan; font-weight: 600;">read_only</span>: <span style="color: magenta; font-weight: 600;">true</span>
  <span style="color: cyan; font-weight: 600;">code</span>: <span style="color: #66cc66;">|</span>
    todos = ctx.code_intelligence.text_search(
        pattern=&quot;(TODO|FIXME|HACK):&quot;, scope=params[&quot;scope&quot;]
    )
    complex_functions = ctx.code_intelligence.graph_query(&#x27;&#x27;&#x27;
        SELECT ?func ?complexity ?coverage WHERE {
            ?func a code:Function ;
                  <span style="color: cyan; font-weight: 600;">code</span>:hasComplexity ?complexity ;
                  <span style="color: cyan; font-weight: 600;">code</span>:hasTestCoverage ?coverage .
            FILTER(?complexity &gt; 15 || ?coverage &lt; 0.3)
        } ORDER BY DESC(?complexity)
    &#x27;&#x27;&#x27;)
    smells = []
    for pattern in [&quot;duplicate code&quot;, &quot;long method&quot;, &quot;large class&quot;]:
        smells.extend(ctx.code_intelligence.find_similar_code(
            pattern=pattern, threshold=0.8
        ))
    return {
        &quot;high_priority&quot;: complex_functions[:5],
        &quot;technical_debt&quot;: todos,
        &quot;code_smells&quot;: smells,
        &quot;suggested_order&quot;: ctx.code_intelligence.rank_by_impact(
            complex_functions + todos + smells
        )
    }

6. Real-time Index Synchronization

The system maintains index freshness through immediate, proactive updates:

class IndexSynchronizer:
    def __init__(self):
        self.file_watcher = FileSystemWatcher()
        self.git_monitor = GitChangeMonitor()
        self.incremental_indexer = IncrementalIndexer()
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_resource_added</span>(self, resource: Resource, project: Project):
    <span style="color: #66cc66;">&quot;&quot;&quot;When a resource is added to a project, index it immediately&quot;&quot;&quot;</span>
    <span style="opacity: 0.7;"># Full initial indexing - happens once when resource is added</span>
    <span style="color: magenta; font-weight: 600;">with</span> self.progress_reporter(<span style="color: #66cc66;">f&quot;Indexing {resource.name}&quot;</span>) <span style="color: magenta; font-weight: 600;">as</span> progress:
        files = self.scan_resource(resource)
        total = len(files)
        
        <span style="opacity: 0.7;"># Parallel indexing for performance</span>
        <span style="color: magenta; font-weight: 600;">with</span> ThreadPoolExecutor(max_workers=cpu_count()) <span style="color: magenta; font-weight: 600;">as</span> executor:
            futures = []
            
            <span style="color: magenta; font-weight: 600;">for</span> i, file <span style="color: magenta; font-weight: 600;">in</span> enumerate(files):
                future = executor.submit(self.index_file_complete, file)
                futures.append(future)
                progress.update(i / total)
            
            <span style="opacity: 0.7;"># Wait for all indexing to complete</span>
            <span style="color: magenta; font-weight: 600;">for</span> future <span style="color: magenta; font-weight: 600;">in</span> futures:
                future.result()
    
    <span style="opacity: 0.7;"># Now set up watchers for incremental updates</span>
    self.setup_watchers(resource)
    
    <span style="opacity: 0.7;"># Mark resource as indexed and ready</span>
    resource.indexing_status = <span style="color: #66cc66;">&quot;ready&quot;</span>
    self.notify_agents_index_ready(resource)
    
    <span style="opacity: 0.7;"># CRITICAL: Agents can now immediately search this resource</span>
    <span style="opacity: 0.7;"># No &quot;warming up&quot; period - indices are complete and ready</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">index_file_complete</span>(self, file_path: <span style="color: cyan;">str</span>):
    <span style="color: #66cc66;">&quot;&quot;&quot;Comprehensive initial indexing of a file&quot;&quot;&quot;</span>
    <span style="opacity: 0.7;"># Parse file once</span>
    ast = self.parse_file(file_path)
    
    <span style="opacity: 0.7;"># Update all indices immediately</span>
    self.update_text_index(file_path, ast)
    self.update_vector_embeddings(file_path, ast)
    self.update_graph_triples(file_path, ast)
    
    <span style="opacity: 0.7;"># Extract and index all metadata</span>
    self.index_symbols(file_path, ast)
    self.index_dependencies(file_path, ast)
    self.index_complexity_metrics(file_path, ast)

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">setup_watchers</span>(self, project: Project):
    <span style="opacity: 0.7;"># File system watching for immediate updates</span>
    self.file_watcher.watch(
        path=project.root_path,
        events=[<span style="color: #66cc66;">&quot;create&quot;</span>, <span style="color: #66cc66;">&quot;modify&quot;</span>, <span style="color: #66cc66;">&quot;delete&quot;</span>],
        callback=self.on_file_change
    )
    
    <span style="opacity: 0.7;"># Git monitoring for batch updates (using linked git resource)</span>
    git_resource = project.get_linked_resource(type=<span style="color: #66cc66;">&quot;git-checkout&quot;</span>)
    self.git_monitor.watch(
        repo=git_resource,
        events=[<span style="color: #66cc66;">&quot;commit&quot;</span>, <span style="color: #66cc66;">&quot;merge&quot;</span>, <span style="color: #66cc66;">&quot;rebase&quot;</span>],
        callback=self.on_git_change
    )

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_file_change</span>(self, event: FileEvent):
    <span style="opacity: 0.7;"># Quick incremental update</span>
    <span style="color: magenta; font-weight: 600;">if</span> event.type <span style="color: magenta; font-weight: 600;">in</span> [<span style="color: #66cc66;">&quot;create&quot;</span>, <span style="color: #66cc66;">&quot;modify&quot;</span>]:
        <span style="opacity: 0.7;"># Parse changed file</span>
        ast = self.parse_file(event.path)
        
        <span style="opacity: 0.7;"># Update indices</span>
        self.update_text_index(event.path, ast)
        self.update_vector_embeddings(event.path, ast)
        self.update_graph_triples(event.path, ast)
    
    <span style="color: magenta; font-weight: 600;">elif</span> event.type == <span style="color: #66cc66;">&quot;delete&quot;</span>:
        self.remove_from_indices(event.path)

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">on_git_change</span>(self, event: GitEvent):
    <span style="opacity: 0.7;"># Batch update for git operations</span>
    changed_files = event.get_changed_files()
    
    <span style="opacity: 0.7;"># Optimize batch processing</span>
    <span style="color: magenta; font-weight: 600;">with</span> self.batch_updater() <span style="color: magenta; font-weight: 600;">as</span> updater:
        <span style="color: magenta; font-weight: 600;">for</span> file <span style="color: magenta; font-weight: 600;">in</span> changed_files:
            updater.queue_update(file)
        
        <span style="opacity: 0.7;"># Process in parallel</span>
        updater.execute(parallel=<span style="color: magenta; font-weight: 600;">True</span>)

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">update_graph_triples</span>(self, file_path: <span style="color: cyan;">str</span>, ast: AST):
    <span style="opacity: 0.7;"># Generate RDF triples from AST</span>
    triples = []
    
    <span style="opacity: 0.7;"># Module-level triples</span>
    module_uri = self.uri_for_file(file_path)
    <span style="color: magenta; font-weight: 600;">for</span> import_stmt <span style="color: magenta; font-weight: 600;">in</span> ast.imports:
        imported_uri = self.resolve_import(import_stmt)
        triples.append((module_uri, <span style="color: #66cc66;">&quot;code:imports&quot;</span>, imported_uri))
    
    <span style="opacity: 0.7;"># Function-level triples</span>
    <span style="color: magenta; font-weight: 600;">for</span> func <span style="color: magenta; font-weight: 600;">in</span> ast.functions:
        func_uri = self.uri_for_function(func)
        triples.append((module_uri, <span style="color: #66cc66;">&quot;code:contains&quot;</span>, func_uri))
        triples.append((func_uri, <span style="color: #66cc66;">&quot;a&quot;</span>, <span style="color: #66cc66;">&quot;code:Function&quot;</span>))
        triples.append((func_uri, <span style="color: #66cc66;">&quot;code:hasComplexity&quot;</span>, func.complexity))
        
        <span style="opacity: 0.7;"># Call relationships</span>
        <span style="color: magenta; font-weight: 600;">for</span> call <span style="color: magenta; font-weight: 600;">in</span> func.calls:
            called_uri = self.resolve_call(call)
            triples.append((func_uri, <span style="color: #66cc66;">&quot;code:calls&quot;</span>, called_uri))
    
    <span style="opacity: 0.7;"># Update graph store</span>
    self.graph_store.update_triples(triples)

7. Fallback to Traditional Search

When advanced features are unavailable, the system gracefully degrades:

class FallbackSearchProvider:
    def search(self, query: str, resources: List[Resource]) -> SearchResults:
        # Try advanced search first
        try:
            if self.vector_index.is_available():
                return self.vector_search(query, resources)
        except ServiceUnavailable:
            pass
    <span style="opacity: 0.7;"># Fallback to graph search</span>
    <span style="color: magenta; font-weight: 600;">try</span>:
        <span style="color: magenta; font-weight: 600;">if</span> self.graph_index.is_available():
            <span style="color: magenta; font-weight: 600;">return</span> self.graph_search(query, resources)
    <span style="color: magenta; font-weight: 600;">except</span> ServiceUnavailable:
        <span style="color: magenta; font-weight: 600;">pass</span>
    
    <span style="opacity: 0.7;"># Ultimate fallback: grep-like text search</span>
    <span style="color: magenta; font-weight: 600;">return</span> self.basic_text_search(query, resources)

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">basic_text_search</span>(self, query: <span style="color: cyan;">str</span>, resources: <span style="color: cyan;">List</span>[Resource]):
    <span style="opacity: 0.7;"># Use ripgrep or similar for fast text search</span>
    results = []
    
    <span style="color: magenta; font-weight: 600;">for</span> resource <span style="color: magenta; font-weight: 600;">in</span> resources:
        matches = ripgrep.search(
            pattern=query,
            path=resource.path,
            context_lines=3
        )
        
        <span style="color: magenta; font-weight: 600;">for</span> match <span style="color: magenta; font-weight: 600;">in</span> matches:
            results.append(SearchResult(
                file=match.file,
                line=match.line,
                content=match.content,
                score=1.0  <span style="opacity: 0.7;"># Basic scoring</span>
            ))
    
    <span style="color: magenta; font-weight: 600;">return</span> results

Index Lifecycle

The system follows a clear lifecycle for index management:

Index Lifecycle:
  1_resource_added:
    trigger: "agents resource add / agents project link-resource"
    action: "Immediate full indexing"
    duration: "Depends on size (10K files ~1 minute)"
    result: "All indices ready for instant search"
2_code_changed:
trigger: "File modification detected"
action: "Immediate incremental update"
duration: "Milliseconds per file"
result: "Indices stay synchronized"
3_resource_removed:
trigger: "agents project unlink-resource / agents resource remove"
action: "Immediate index cleanup"
duration: "Seconds"
result: "No stale data in indices"
4_maintenance:
trigger: "Scheduled or manual"
action: "Reindex for consistency"
duration: "Background process"
result: "Indices optimized and verified"
Key Guarantees:

"No search happens on stale data"
"No 'index building' delays during agent execution"
"Changes visible in search immediately"
"Initial indexing is a one-time cost per resource"

Integration with Context Tiers

The Code Intelligence system directly feeds into the three-tier context architecture:

Context Tier Integration:
  hot_tier:
    source: "Real-time results from code intelligence"
    content:
      - Currently edited files
      - Direct dependencies
      - Immediately relevant functions
warm_tier:
source: "Indexed embeddings and graph queries"
content:
- Recent search results
- Cached graph traversals
- Vector similarity matches
- Active decision contexts
cold_tier:
source: "Historical indices and compressed data"
content:
- Previous plan analyses
- Archived dependency graphs
- Historical refactoring patterns
- Learned codebase conventions

Performance Characteristics

The system maintains pre-computed indices for instant search performance:

Performance Metrics:
  initial_indexing_speed:
    text_index: "10,000 files/minute"
    vector_index: "1,000 files/minute (with GPU)"
    graph_index: "5,000 files/minute"
query_performance:
text_search: "< 100ms for 1M files"
vector_search: "< 200ms for 10M embeddings"
graph_traversal: "< 500ms for 3-hop queries"
storage_requirements:
text_index: "~10% of source size"
vector_index: "~1GB per 100K functions"
graph_store: "~100MB per 10K files"
scalability:
max_files: "No hard limit (tested to 10M files)"
max_graph_size: "1B+ triples"
max_vectors: "100M+ embeddings"

Progressive Enhancement Path

Organizations can adopt Code Intelligence features progressively. At each stage, existing resources are reindexed to take advantage of new capabilities:

adoption_stages:
  stage_1_basic:
    features: ["text search", "file watching"]
    requirements: ["ripgrep", "sqlite"]
    initial_setup: "Index all text content on resource add"
    benefit: "Instant exact-match search"
stage_2_semantic:
features: ["vector embeddings", "similarity search"]
requirements: ["embedding model", "vector DB"]
initial_setup: "Generate embeddings for all code (one-time cost)"
benefit: "Instant semantic similarity search"
stage_3_structural:
features: ["RDF graph", "relationship queries"]
requirements: ["graph database", "language analyzers"]
initial_setup: "Parse and build complete knowledge graph"
benefit: "Instant relationship queries"
stage_4_intelligent:
features: ["ML-driven ranking", "automated analysis"]
requirements: ["GPU", "training data"]
initial_setup: "Pre-compute ML features and rankings"
benefit: "Instant intelligent suggestions"
stage_5_custom:
features: ["Domain-specific ontologies", "Custom analyzers"]
requirements: ["Domain expertise", "Custom development"]
initial_setup: "Build domain-specific indices"
benefit: "Instant domain-aware intelligence"

This Code Intelligence & Context Discovery system ensures that CleverAgents can efficiently work with codebases of any size, providing agents with the contextual understanding they need to make intelligent decisions about code changes, refactoring, and feature development.

Summary of Timing:

• Indexing: Eager (happens immediately when resources are added/changed)
• Searching: Instant (because indices are pre-computed and ready)
• Sandboxing: Lazy (only when execution needs to modify a resource)
• Context Assembly: Real-time (but fast because it queries ready indices)

This design ensures agents never wait for index building during execution, providing a responsive and predictable experience even on massive codebases.

Context

Note: This section describes the high-level context management system. For details on how context is discovered and indexed, see the Code Intelligence & Context Discovery section above.

Context in CleverAgents is not "dump all files into an LLM." It is a system that:

• finds relevant information from resources,
• injects appropriate subsets into each actor/node,
• and scales to large repositories.

Current Reality and Planned Improvements

key concepts:

• There is a global context concept,
• But nodes only see what is injected into their prompts,
• It's functional but not yet elegant,
• A more advanced automated system is planned.

Tiered Context Architecture (Hot/Warm/Cold)

The system uses a sophisticated three-tier memory architecture that enables working with massive codebases without holding everything in memory:

• Hot context (hot cache) The small set of immediately relevant chunks injected into the current actor prompt. When working on a 50,000 file codebase, hot context focuses on the immediate task (e.g., 10-20 files for a specific refactoring).

• Warm context Recent decisions and their contexts from this plan tree - quickly accessible. Includes indexed embeddings, vector search results, and graph store representations. Maintains the decision chain that led to the current work.

• Cold storage Historical decisions from past plans on this codebase - queryable but not in active memory. Long-term storage in SQLite or caching systems containing prior summaries, older plan artifacts, and historical patterns (e.g., "last time we refactored auth, we also had to update these services").

Promotion/demotion behavior:

• System analyzes current query
• Promotes relevant data upward (cold → warm → hot)
• Demotes stale data out of hot to keep prompts tight
• Preserves complete context snapshots for every decision

This architecture leverages the key insight that software development is inherently local - even in huge codebases, individual changes typically touch a bounded set of files. The Decision Tree captures these localities.

Actor-Specific Context Views

A key missing feature identified in the notes is per-actor context views, filtering, relevance, and actor-aware context limits.

The intended direction:

• global context exists at plan level,
• each actor gets a "view" of that context tuned to their role,
• memory may be shared or per-plan depending on design choices.

Actor Context View Service (Proposed)

A dedicated module/service that:

• maintains actor-specific "context views,"
• tracks actor memory and relevance,
• enforces actor-specific limits (tokens, file types, etc.).

Initial Context vs Deep Context

A practical approach mentioned:

• Initial context is high-level (repo tree, language, overview),
• Then the system searches for relevant details iteratively via RAG.

This strongly suggests CleverAgents should define:

• an "initial context recipe" per project type (codebase vs documents vs infra),
• iterative context refinement loops during strategize/execute.

Behavior

Automation Profiles

Automation profiles determine which phase transitions happen automatically and which require human approval.

Overview

An automation profile is a named collection of boolean flags that controls which tasks are automated vs. require human approval. Profiles follow the same <namespace>/<name> naming convention as actors, tools, skills, and other entities. Profiles are managed via the agents automation-profile CLI commands.

Automatable Tasks

Each automation profile specifies a true/false value for each of the following automatable tasks:

Flag	Description	When true	When false
auto_strategize	Automatically enter Strategize after plan use	Strategize begins immediately	System pauses; user must run agents plan execute to start Strategize
auto_execute	Automatically proceed from Strategize to Execute	Execute begins when Strategize completes	System pauses after Strategize; user reviews strategy and runs agents plan execute
auto_apply	Automatically proceed from Execute to Apply	Apply begins when Execute completes	System pauses after Execute; user reviews diffs and runs agents plan apply
auto_decisions_strategize	Automatically make decisions during Strategize	Strategy actor makes all decisions autonomously	System pauses at each decision point for user input
auto_decisions_execute	Automatically make decisions during Execute	Execution actor makes all decisions autonomously	System pauses at each decision point for user input
auto_validation_fix	Automatically attempt to fix validation failures	Execution actor self-fixes failing required validations within strategy bounds	System pauses on validation failure; user must provide guidance
auto_strategy_revision	Automatically revise strategy when Execute cannot solve within constraints	System re-runs Strategize for affected subtree	System pauses and asks user whether to revise the strategy
auto_child_plans	Automatically spawn and execute child plans	Child plans are created and executed without pausing	System pauses before spawning each child plan for approval
auto_retry_transient	Automatically retry on transient failures (network, timeout, rate-limit)	System retries automatically with backoff	System pauses and asks user to retry or abort
auto_checkpoint_restore	Automatically restore from checkpoint on failure	System rolls back to last checkpoint and retries	System pauses; user decides whether to restore or intervene
require_sandbox	Require sandbox isolation for Execute phase	Execute must run in a sandbox (worktree, container, etc.)	Sandbox is optional; Execute may modify resources directly
require_checkpoints	Require checkpointing during Execute	Tools must create checkpoints before writes	Checkpointing is optional
allow_unsafe_tools	Allow execution of tools marked as unsafe	Unsafe tools can be invoked	Unsafe tools are blocked; only safe tools are allowed

Built-in Automation Profiles

CleverAgents ships with six built-in automation profiles. Built-in profiles use no namespace prefix.

Flag	locked-down	manual	supervised	trusted	autonomous	full-auto
auto_strategize	-	-	✓	✓	✓	✓
auto_execute	-	-	-	✓	✓	✓
auto_apply	-	-	-	-	-	✓
auto_decisions_strategize	-	-	✓	✓	✓	✓
auto_decisions_execute	-	-	-	✓	✓	✓
auto_validation_fix	-	-	-	✓	✓	✓
auto_strategy_revision	-	-	-	-	✓	✓
auto_child_plans	-	-	-	✓	✓	✓
auto_retry_transient	-	-	✓	✓	✓	✓
auto_checkpoint_restore	-	-	-	-	✓	✓
require_sandbox	✓	✓	✓	✓	✓	-
require_checkpoints	✓	✓	✓	✓	✓	-
allow_unsafe_tools	-	-	-	-	-	✓

locked-down: Maximum human control. Every phase transition, every decision, every child plan requires explicit human approval. Sandbox and checkpoints are mandatory. Unsafe tools are blocked. Use for: critical production systems, first-time exploration of an unfamiliar codebase, high-risk infrastructure changes.

manual: Human drives all phase transitions but decisions within Strategize and Execute are still manual. Similar to locked-down but serves as the default starting point. Use for: new users learning the system, sensitive projects, regulatory environments.

supervised: Strategize runs automatically with autonomous decisions, but the system pauses before Execute for human review of the strategy. Transient failures are retried automatically. Use for: projects where you trust the planning but want to review before execution begins.

trusted: Strategize and Execute run automatically with autonomous decisions. Validation failures are self-fixed. Child plans are spawned automatically. The system pauses only before Apply for human review of the final diffs. Use for: day-to-day feature development, routine refactoring, test generation.

autonomous: Everything runs automatically except Apply. The system can even revise its own strategy if execution hits a wall, and restore from checkpoints on failure. Use for: well-understood projects, batch operations, tasks with strong invariant coverage.

full-auto: Complete end-to-end automation including Apply. No sandbox or checkpoint requirements. Unsafe tools are allowed. Use for: low-risk routine tasks (dependency updates, documentation generation, formatting), CI/CD pipeline integration, trusted batch operations with rollback capabilities.

Profile Precedence

Automation profiles are determined using this precedence (highest to lowest):

1. Plan-level: Explicitly set via --automation-profile on agents plan use
2. Action-level: Set on the action via --automation-profile on agents action create
3. Project-level: Set via agents config set automation-profile <PROFILE> --project <PROJECT>
4. Global-level: Set via agents config set automation-profile <PROFILE>

The effective profile for a plan is resolved at the moment of agents plan use. Once resolved, the profile is locked to that plan — subsequent changes to project or global profiles do not affect running plans.

Child Plan Profile Inheritance

Child plans inherit the parent plan's effective automation profile. If the parent's profile is changed explicitly after creation, new child plans use the new profile while already-running child plans retain their original profile.

Custom Automation Profiles

Custom profiles are created via YAML configuration files and registered with agents automation-profile add:

# File: profiles/careful-auto.yaml
name: local/careful-auto
description: "Autonomous execution with mandatory sandbox and manual apply"
flags:
  auto_strategize: true
  auto_execute: true
  auto_apply: false
  auto_decisions_strategize: true
  auto_decisions_execute: true
  auto_validation_fix: true
  auto_strategy_revision: false
  auto_child_plans: true
  auto_retry_transient: true
  auto_checkpoint_restore: true
  require_sandbox: true
  require_checkpoints: true
  allow_unsafe_tools: false

agents automation-profile add --config ./profiles/careful-auto.yaml

Semantic Escalation

Even when a profile sets a task to automatic, the system may still escalate to the user when confidence is low. Semantic escalation is orthogonal to automation profiles — it provides a safety net that works within any profile:

class AutonomyController:
    def assess_decision_confidence(self, decision, context):
        factors = {
            'past_success_rate': self.get_historical_success(decision.type),
            'codebase_familiarity': self.get_familiarity_score(context.project),
            'risk_assessment': self.evaluate_risk(decision),
            'invariant_complexity': self.analyze_invariants(decision)
        }
    confidence = self.compute_confidence(factors)
    
    <span style="color: magenta; font-weight: 600;">if</span> confidence &lt; self.threshold:
        <span style="opacity: 0.7;"># Even in autonomous profiles, critical decisions escalate</span>
        <span style="color: magenta; font-weight: 600;">return</span> RequestHumanGuidance(decision, factors)
    
    <span style="color: magenta; font-weight: 600;">return</span> ProceedAutonomously(decision)

Progressive Trust Building

New users typically follow this progression:

1. Start with manual to understand system behavior
2. Move to supervised as confidence in the planning phase builds
3. Adopt trusted for routine development tasks
4. Enable autonomous for well-understood projects with strong invariant coverage
5. Use full-auto for low-risk batch operations or CI/CD integration

Validation and Guardrails

Plan generation validation

The validation logic is stubbed and must be implemented. The spec should require:

• validate action schema
• validate actor availability
• validate required skills exist
• validate permission policy
• validate rollback feasibility (if enabled)
• validate project resource accessibility

This prevents "plan runs with fake providers" and other surprises.

Cost / rate limits

Future concerns:

• API call limits
• cost caps

So CleverAgents should define:

• per-plan budgets
• per-session budgets
• per-org budgets
• per-actor max tool calls / max retries

Correcting Plans (Core Feature)

Correcting plans is where CleverAgents becomes more than "a fancy prompt runner."

The Goal

When a plan makes a wrong decision early, we want to:

• correct the decision,
• recompute only the affected subtree,
• preserve unaffected work.

This is explicitly described: "redo everything below that decision, not the entire code base."

Decision Tree Representation

Every plan records (see Decision Data Model section):

• decisions (choice points) - created during Strategize, including invariant_enforced, subplan_spawn, and subplan_parallel_spawn decisions
• dependencies (which later work depended on that decision)
• child plans spawned because of that decision - populated during Execute
• artifacts generated under that branch

This makes plan runs auditable and correctable.

Two Correction Modes

1. Revert-from-history correction (--mode=revert)

• Find the decision point in the tree
• Roll back all changes (code and non-code) to that point
• Re-run from that decision point forward
• Keep old execution artifacts for comparison
• Potentially expensive if high up in the tree

2. Add-at-end correction (--mode=append)

• Leave history intact
• Append a new plan at the end that fixes the outcome
• Cheaper and safer sometimes
• Does not rewrite history

Correction Flow (Revert Mode)

When user requests correction at Decision B:

1. Mark for Correction

   
   Decision B.superseded_by = new_decision_id
   

2. Identify Downstream Impact

   • Recursively collect all decisions that depend on Decision B (including invariant_enforced, subplan_spawn, and subplan_parallel_spawn decisions)
   • Collect all child plans spawned from those decisions
   • These form the "affected subtree"

3. Rollback Resources

   • For each affected decision's artifacts_produced:
     • Rollback to the checkpoint before that artifact was created
   • For affected child plans:
     • Rollback their sandboxes entirely

4. Preserve for Comparison

   • Archive the original subtree's artifacts
   • Create a CorrectionAttempt record linking old and new

5. Re-execute from Decision Point

   • Restore context to Decision B.context_snapshot
   • User provides new guidance/correction
   • Re-run execution from that point
   • New decisions get is_correction: true, corrects_decision_id: B

6. Apply as Normal

   • The corrected plan goes through normal Apply gating
   • Diff shows changes from the correction

History Cleanup

History can only be flagged for cleanup after a plan is Applied.

Once a plan is applied:

• It can no longer be rolled back
• Old correction artifacts can be archived or deleted based on retention policy
• The decision tree is preserved for audit purposes

CLI Commands for Correction

# View decision tree
agents plan tree <plan_id>
agents --format=json plan tree <plan_id>  # For visualization tools
# Inspect a specific decision
agents plan explain <decision_id>
# Shows: question, chosen option, alternatives, rationale, downstream impact
# Correct via revert-and-replay
agents plan correct <decision_id> --mode=revert --guidance "<what the decision should be>"
# Re-executes from that point with the new guidance
# Correct via append (add fix at end)  
agents plan correct <decision_id> --mode=append --guidance "<description of the fix>"
# Creates a new child plan to fix the outcome without rewriting history
# Compare old vs new after correction
agents plan diff --correction <correction_attempt_id>

Correction Safety

Corrections always:

• Create a new attempt revision (increment plan.attempt)
• Preserve old artifacts for diff/compare
• Run execute in sandbox again
• Require apply gating again
• Never modify already-applied changes

This keeps history reproducible and prevents accidental destructive edits.

Human-in-the-Loop Collaboration

Even though the direction is "more autonomous," the transcript explicitly recognizes that real workflows require engineers to collaborate with the system, editing code while it works, and using better UX integration (TUI/web/IDE).

So CleverAgents should aim for:

• visibility: what is it doing now?
• interruptibility: pause/cancel/retry
• editability: allow user to modify strategy before execute
• reconciliation: detect if user changed sandbox files mid-run and handle it

UI / Interaction Model

CLI-first + TUI + Web App + IDE

The system intends to be CLI-first, with:

• a TUI built using Textual,
• which can generate a web app "for free,"
• and later an IDE plugin that embeds the TUI in the IDE.

This implies a "single UI codebase" model:

• same underlying view logic,
• multiple frontends.

Plan Tree Visualization

The TUI should show:

• plan list
• plan details
• plan tree (ASCII)
• diff view
• approvals

And should later allow exporting the tree as image (PNG) or JSON for other visualization tools.

Additional Recommended Sections (Missing from Draft but Important)

Storage and Persistence

CleverAgents should define where each concept lives:

• Actions: stored in a registry (local files or server DB)
• Actors: stored similarly (config files + DB indexing)
• Projects: stored locally or on server
• Plans: stored in plan DB with full logs
• Context indexes: vector store / graph store / SQLite
• Artifacts: filesystem or object storage

Observability

To debug large plans:

• every phase should emit events
• every actor call should log prompt/context references
• every tool call should log resource access (with parent skill noted)
• every checkpoint should be recorded

Security Model

• sandbox isolation
• resource-level ACLs
• prompt injection mitigations (server mode)
• secret management (API keys, DB credentials)
• audit logs for apply

Extensibility

• plugin system for skills
• custom node types
• action templates
• actor templates (noted as missing currently)

Summary of Key Intended Behaviors (If You Only Read One Section)

• Plans follow Action → Strategize → Execute → Apply with automation profiles controlling transitions and decision automation.
• Strategize is read-only and produces a strategy + blueprint.
• Execute happens in a sandbox, can spawn child plans, and should support checkpoints/rollback when enabled.
• Apply commits changes from sandbox to real project after review/validation.
• Actors are hierarchical: an actor can be a single agent or an entire graph.
• Graph nodes can be actors or tools (tools are provided by referenced skills, which aggregate tools from MCP servers, Agent Skills folders, built-ins, and custom code).
• Context should evolve toward hot/warm/cold tiers and actor-specific context views.
• The system is designed for large tasks where the user can correct a decision and only recompute downstream work, visualizable as a plan decision tree.

The system can handle Firefox-scale projects not through magic, but through:

• Hierarchical decomposition breaking massive tasks into bounded work
• Persistent decision graphs maintaining context across any scale
• Isolated execution preventing cascading failures
• Semantic validation catching errors before propagation
• Progressive automation building trust through incremental success

Future details to add to this document:

• a canonical JSON/YAML schema for Actions, Actors, Projects, Plans, Skills, and Context Views,
• a CLI command reference (every command, flags, examples),
• and a set of end-to-end example workflows (single project, multi-project, infra task, paper-writing task) consistent with this spec.

Work Remaining to Make CleverAgents Fully Functional

This section describes what remains to be done to bring the current CleverAgents codebase up to the specification.

Last Updated: February 6, 2026

Current State Assessment

What's Implemented

Based on analysis of the current codebase:

1. Plan Lifecycle Foundation - The 4-phase lifecycle (Action → Strategize → Execute → Apply) is partially implemented in plan_lifecycle_service.py
2. Database Models - Models exist for projects, plans, contexts, changes, and actors
3. LangGraph Integration - Graph-based workflow support exists but is not fully connected to the plan workflow
4. Reactive System - A reactive system with stream routing is present
5. Actor System - Actor models and services exist but lack full behavioral definitions
6. Basic Context Analysis - Simple context loading and analysis capabilities
7. Change Tracking - Basic Change and ChangeSet models exist

What's Missing

Critical gaps between the specification and current implementation:

1. No Resource Abstraction - The unified resource layer for files, databases, APIs is entirely missing
2. No Sandboxing - Execute phase writes directly to files without isolation
3. No Decision Tree - No decision tracking, storage, or correction mechanism
4. No Skills/MCP Integration - No skill registry, skill YAML parsing, tool adapters, or MCP protocol support
5. Single-File Limitation - Hard-coded to generate exactly one file per plan
6. Text-Based Code Generation - Still parsing LLM output instead of tool-based approach
7. No Checkpointing - No rollback or checkpoint capabilities
8. No Code Intelligence - Missing indexing, vector search, and RDF graph store
9. No Context Tiers - No hot/warm/cold memory architecture
10. Limited Validation - Basic stub validation instead of semantic checks

Work Items by Priority

1) Implement Tool-Based Resource Modification (Critical Foundation)

Problem

The current system generates code as a single text blob that gets written to one file. The specification requires a modern tool-based approach where LLMs invoke discrete operations on resources.

Implementation Steps

1. Create Resource Abstraction Layer

   
   # New modules needed:
   src/cleveragents/domain/models/resources.py
   src/cleveragents/domain/resources/handlers.py
   src/cleveragents/domain/resources/sandbox.py
   

2. Implement Built-in Tools (via built-in skills)

   
   # Core file operations (provided by built-in skill groups)
   read_file(path: str) -> str
   write_file(path: str, content: str) -> None
   edit_file(path: str, changes: list[Edit]) -> None
   delete_file(path: str) -> None
   move_file(src: str, dst: str) -> None
   create_directory(path: str) -> None
   list_files(pattern: str) -> list[str]
   search_files(pattern: str, content_pattern: str) -> list[Match]
   

3. Connect Tools to ChangeSet

   • Each tool invocation that modifies resources creates a Change record
   • ChangeSet accumulates these changes during execution
   • No more parsing LLM text output for code

Estimated Effort: 2-3 weeks

2) Implement Sandbox Infrastructure (Critical for Safety)

Problem

Execute phase currently writes directly to the project. The specification requires all changes happen in an isolated sandbox that can be reviewed before applying.

Implementation Steps

1. Define Sandbox Interface

   
   class Sandbox(Protocol):
       def create() -> SandboxRef
       def read(path: str) -> Content
       def write(path: str, content: Content) -> Change
       def diff() -> DiffView
       def commit() -> None
       def rollback() -> None
   

2. Implement Sandbox Strategies

   • GitWorktreeSandbox - For git repositories (preferred)
   • FilesystemCopySandbox - For non-git projects
   • TransactionSandbox - For databases
   • NoOpSandbox - For non-sandboxable resources

3. Lazy Sandbox Creation

   • Only create sandboxes when resources are accessed
   • Each plan gets its own sandbox namespace

Estimated Effort: 2 weeks

3) Build Decision Tree System (Core Innovation)

Problem

No decision tracking exists. The specification's key innovation is recording every decision with full context, enabling correction without full re-execution.

Implementation Steps

1. Create Decision Models

   
   -- New tables needed
   CREATE TABLE decisions (
       decision_id TEXT PRIMARY KEY,  -- ULID
       plan_id TEXT NOT NULL,
       parent_decision_id TEXT,
       decision_type TEXT NOT NULL,
       question TEXT NOT NULL,
       chosen_option TEXT NOT NULL,
       alternatives_considered TEXT,  -- JSON array
       context_snapshot TEXT NOT NULL,  -- JSON
       created_at TEXT NOT NULL
   );
   

2. Implement Decision Recording

   • Every choice during Strategize creates a Decision record
   • Capture complete context snapshot with each decision
   • Track downstream dependencies

3. Build Correction Mechanism

   
   agents plan correct <decision_id> --mode=revert --guidance "<new decision>"
   

   - Mark decision as superseded - Recompute only affected subtree - Preserve unaffected work

Estimated Effort: 3 weeks

4) Implement Code Intelligence System (Scalability Enabler)

Problem

Current context is limited to a few hundred characters from a few files. Large codebases require intelligent context discovery.

Implementation Steps

1. Eager Indexing on Resource Add

   
   def on_resource_added(resource: Resource):
       # Index immediately when resource added to project
       index_text_content(resource)      # Full-text search
       generate_embeddings(resource)     # Vector embeddings
       build_knowledge_graph(resource)   # RDF triples
   

2. Three-Index Architecture

   • Text Index: Tantivy/SQLite FTS for exact matches
   • Vector Index: FAISS/Qdrant for semantic search
   • Graph Store: RDF store for relationships

3. Tiered Context System

   • Hot: Current working set (in LLM context)
   • Warm: Recent decisions and search results
   • Cold: Historical data and patterns

Estimated Effort: 4 weeks

5) Complete Actor System with Behavioral Definitions

Problem

Actors exist but don't define behavior beyond model selection. The specification requires actors to be composable graphs with tools, memory, and context policies.

Implementation Steps

1. Extend Actor Configuration

   
   actors:
     my_strategist:
       type: graph
       config:
         provider: anthropic
         model: claude-3-opus
         memory_policy: per_plan
         context_view: architect # High-level view
       skills:
         - local/file-ops             # provides read_file
         - local/code-intelligence    # provides search_code, analyze_dependencies
       routes:
         strategize:
           entry_point: analyze
           nodes:
             - name: analyze
               type: llm
             - name: plan
               type: llm
   

2. Implement Tool Access Policies

   • Strategy actors: read-only tools
   • Execute actors: read/write within sandbox
   • Apply actors: commit tools

3. Actor-Specific Context Views

   • Strategist: Architecture, dependencies, patterns
   • Executor: Implementation details, specific files
   • Reviewer: Diffs, tests, risk analysis

Estimated Effort: 2 weeks

6) Resource System, Resource Types, and Resource Registry

Problem

Resources are currently defined inline within projects. The specification requires resources to be independently registered first-class entities with a type system, DAG relationships (physical/virtual), auto-discovery, and content-identity tracking. Tools need resource bindings to declare and resolve their resource dependencies.

Implementation Steps

1. Resource Type Registry and CLI

   • Implement 24 built-in resource types: 15 physical (git-checkout, git, git-remote, git-branch, git-tag, git-commit, git-tree, git-tree-entry, git-stash, git-submodule, fs-mount, fs-directory, fs-file, fs-symlink, fs-hardlink) and 9 virtual (file, directory, symlink, commit, branch, tag, remote, submodule, tree)
   • Implement resource type YAML parsing and validation for custom types
   • Implement agents resource type add [--update]/remove/list/show commands
   • Implement dynamic CLI subcommand registration (custom types create new agents resource add subcommands)

2. Resource Registry and CLI

   • Implement agents resource add <type>/remove/list/show/tree commands
   • Implement Resource Registry persistence in database
   • Implement DAG parent/child relationships with type constraints and cycle detection
   • Implement agents resource link-child/unlink-child for manual DAG management

3. Auto-Discovery System

   • Implement handler-driven child resource discovery (git-checkout discovers git + fs-directory worktree root; git discovers remotes, branches, tags, commits, stashes, submodules; git-commit discovers root git-tree; git-tree discovers tree entries + subtrees; fs-mount discovers root fs-directory; fs-directory discovers subdirectories, files, symlinks, hardlinks)
   • Implement resource reuse during discovery (link existing resources instead of duplicating)
   • Implement refresh mechanism for keeping discovered children up to date

4. Physical/Virtual Resource Model

   • Implement content hashing for identity tracking
   • Implement virtual resource linking (shared virtual parents for identical physical resources)
   • Implement divergence detection (unlink when content changes)

5. Project-Resource Linking

   • Replace agents project add-resource/remove-resource with link-resource/unlink-resource
   • Implement project-level overrides (read-only, alias)
   • Migrate any existing inline resource data to Resource Registry

6. Resource Handlers

   • Implement GitCheckoutHandler with auto-discovery, sandbox creation, checkpoint support
   • Implement GitHandler for git repository structure discovery (remotes, branches, tags, commits, stashes, submodules)
   • Implement GitObjectHandler for git-commit, git-tree, git-tree-entry (read-only access to git objects)
   • Implement GitRefHandler for git-branch, git-tag, git-stash (read/write ref manipulation)
   • Implement GitConfigHandler for git-remote, git-submodule (read-only config access)
   • Implement FilesystemHandler with auto-discovery for fs-mount, fs-directory, fs-file, fs-symlink, fs-hardlink
   • Implement handler plugin system for custom resource types

Estimated Effort: 5 weeks

7) MCP Integration, Tool System, Skill System, and Tool-Resource Bindings

Problem

No tool or skill abstraction exists. The specification defines tools as independently registered operations managed via agents tool CLI commands, and skills as namespaced collections of tools managed via agents skill CLI commands. Tools can be sourced from MCP servers, Agent Skills folders, built-ins, and custom code. Tools also need resource bindings to declare and resolve their dependencies on resources.

Implementation Steps

1. Tool Registry and CLI

   • Implement tool YAML parsing and validation (including resources section for resource slots)
   • Implement agents tool add [--update]/remove/list/show commands
   • Implement Tool Registry persistence in database
   • Implement anonymous tool support (inline definitions without registration)

2. Skill Registry and CLI

   • Implement skill YAML parsing and validation
   • Implement agents skill add [--update]/remove/list/show/tools commands
   • Implement hierarchical skill composition (includes) with per-tool metadata overrides
   • Implement Skill Registry persistence in database
   • Implement named tool references (resolving from Tool Registry) and anonymous inline tools

3. Tool-Resource Binding System

   • Implement resource slot parsing and validation in tool YAML
   • Implement three binding modes: contextual, static, parameter
   • Implement binding resolution at activation time (contextual/static) and invocation time (parameter)
   • Implement resource type compatibility validation
   • Implement built-in tool implicit resource bindings
   • Inject bound resources into ToolExecutionContext.resources

4. MCP Tool Adapter

   
   class MCPToolAdapter:
       def wrap_mcp_tool(self, tool) -> Tool:
           # Add sandbox interception
           # Add change tracking
           # Add capability metadata
           # Add resource binding support
   

5. Tool Capability Metadata

   
   class ToolCapability:
       read_only: bool
       write_scope: list[str]       # References resource slot names
       checkpointable: bool
       idempotent: bool
       side_effects: list[str]
   

6. External MCP Server Support

   
   # In tool or skill YAML configuration
   mcp_servers:
     - name: github
       command: "npx @anthropic/mcp-github"
       env: {GITHUB_TOKEN: "${GITHUB_TOKEN}"}
   

7. Agent Skills Adapter

   • Implement SKILL.md frontmatter parsing for discovery
   • Implement progressive disclosure (metadata → instructions → resources)
   • Implement sandboxed script execution

8. Metadata Override System

   • Implement shallow-merge override logic for tool capability metadata
   • Support overrides at skill-level (tool refs), include-level (tool_overrides), and actor graph node-level
   • Built-in tool metadata is not overridable

Estimated Effort: 5 weeks

8) Implement Validation and Semantic Error Prevention

Problem

Current validation is a stub. The specification requires multi-layer semantic validation.

Implementation Steps

1. Decision-Time Validation

   • Validate choices during Strategize
   • Check alternatives for feasibility
   • Record validation in decision metadata

2. Execution-Time Guards

   
   # Actor references a skill containing validation tools
   skills:
     - local/semantic-validators   # validate_api_compatibility,
                                   # check_invariants, verify_test_coverage
   

3. Project-Specific Validation

   
   agents project validation add --description "Run tests" --required my-api "pytest"
   agents project validation add --description "Lint check" --required my-api "ruff check ."
   

Estimated Effort: 2 weeks

9) Connect LangGraph to Plan Lifecycle

Problem

The reactive/LangGraph infrastructure exists but isn't connected to the main plan workflow.

Implementation Steps

1. Create Unified Plan Graph

   
   class PlanLifecycleGraph:
       def strategize_subgraph(self) -> StateGraph
       def execute_subgraph(self) -> StateGraph
       def apply_subgraph(self) -> StateGraph
   

2. Wire Phase Transitions

   • use command triggers strategize graph
   • execute command triggers execute graph
   • apply command triggers apply graph

3. Remove Linear Pipeline

   • Replace tell/build/apply with graph execution
   • Maintain backward compatibility at CLI level

Estimated Effort: 1 week

Implementation Roadmap

Phase 1: Foundation (8-10 weeks)

1. Tool-based resource modification (critical foundation)
2. Sandbox infrastructure
3. Decision tree system

Phase 2: Core Systems (10-12 weeks)

4. Code intelligence system
5. Actor system with behavioral definitions
6. Resource system, resource types, and resource registry
7. MCP integration, tool system, skill system, and tool-resource bindings

Phase 3: Validation and Integration (3 weeks)

8. Validation and semantic error prevention
9. Connect LangGraph to plan lifecycle

Phase 4: Production Hardening (4 weeks)

10. Checkpointing and rollback
11. Cost controls and rate limiting
12. Security fixes (remove eval, fix async)

Total Estimated Timeline: 6-7 months

Key Success Metrics

1. Multi-file generation: Can generate a REST API with routes/, models/, tests/
2. Safe execution: All changes happen in sandbox, reviewed before apply
3. Decision correction: Can correct a decision and recompute only affected work
4. Scale to large codebases: Can work with 10K+ file projects efficiently
5. Semantic safety: Catches breaking changes before they're applied

Migration Strategy

The implementation can proceed incrementally:

1. Start with resource abstraction (enables everything else)
2. Add sandboxing to existing execute phase
3. Gradually replace text parsing with tool invocations
4. Build decision tree alongside existing flow
5. Enhance context as indexing comes online

This allows the system to remain functional during development while progressively adding the architectural improvements described in the specification.

CleverAgents Architecture FAQ

Q: How does CleverAgents handle persistent repository knowledge beyond ephemeral context windows?

What exists today architecturally: The specification defines a sophisticated multi-tier memory system that goes far beyond ephemeral context windows. At its core is the Decision Tree structure which provides a durable, queryable record of every choice made during planning, along with the complete context that informed those choices.

How the persistent model works in practice:

When a strategy actor analyzes a codebase during the Strategize phase, it doesn't just make decisions in isolation. Each decision creates a comprehensive Decision record that includes:

context_snapshot:
  hot_context_hash: str # Cryptographic hash of the exact context
  hot_context_ref: str # Pointer to the full stored snapshot
  relevant_resources: list[ResourceRef] # Every file/symbol that influenced this decision
  actor_state_ref: str # Complete LangGraph checkpoint

This means when the system decides "refactor the authentication module to use async patterns," it permanently records:

• Which files were examined to make that decision
• What symbols and dependencies were traced
• The exact code state that was analyzed
• The reasoning chain that led to this choice
• Alternative approaches that were considered but rejected

The three-tier memory architecture enables scale:

1. Hot tier: Immediate working context (what's in the current LLM context window)
2. Warm tier: Recent decisions and their contexts from this plan tree - quickly accessible
3. Cold tier: Historical decisions from past plans on this codebase - queryable but not in active memory

When working on a 50,000 file codebase, the system doesn't need to hold all files in memory. Instead:

• Hot context focuses on the immediate task (e.g., 10-20 files for a specific refactoring)
• Warm context maintains the decision chain that got us here
• Cold context provides historical patterns ("last time we refactored auth, we also had to update these services")

Why this scales to massive codebases:

The key insight is that software development is inherently local - even in huge codebases, individual changes typically touch a bounded set of files. The Decision Tree captures these localities. When converting Firefox to Rust (your example), the system would:

1. Make high-level architectural decisions and enforce invariants (captured as root decision nodes)
2. Decompose into major subsystem conversions (each a subplan_parallel_spawn or subplan_spawn decision spawning child plans)
3. Each subsystem plan makes decisions about its modules, inheriting applicable invariants
4. Module plans make decisions about individual files

At each level, only the relevant context is loaded. The persistent decision graph means we can always reconstruct why we're converting a particular module and what constraints apply from higher-level decisions.

Concrete example of persistence in action:

Plan: Convert Firefox Renderer to Rust
├── [invariant_enforced] "Maintain API compatibility with existing C++ callers"
├── [invariant_enforced] "All converted modules must pass existing C++ test suites"
├── [strategy_choice] Architecture approach: Start with leaf modules, work inward
│   Context: Analyzed module dependency graph, 2,847 modules total
│   Resources: module_graph.json, architecture_docs.md
│   
├── [subplan_parallel_spawn] Phase 1: Convert utility libraries (no external deps)
│   └── [subplan_spawn] Convert string_utils module
│       └── Plan: 01KH29R8WPKPBHRY7Q0NA9XW86
│           ├── [prompt_definition] "Convert string_utils module to Rust"
│           ├── [invariant_enforced] "Maintain API compatibility with existing C++ callers"
│           ├── [implementation_choice] Use Rust's String type, not custom implementation
│           │   Context: Analyzed 47 string_utils.cpp functions
│           │   Resources: string_utils.cpp, string_utils.h, 12 dependent files
│           │   Rationale: Rust's String provides same guarantees with better ergonomics
│           └── ...

Even months later, we can query: "Why did we use Rust's String type?" and get the exact context and reasoning, without reprocessing the entire codebase.

Q: How does the system compute task-specific dependency closures for large-scale operations?

What exists today architecturally: The specification defines multiple mechanisms for computing and maintaining minimal dependency closures. The execution blueprint produced during the Strategize phase doesn't just list steps - it includes a complete dependency graph with explicit scoping for each operation.

How dependency closure computation works:

During the Strategize phase, the strategy actor employs several mechanisms to compute precise dependency closures:

1. Resource-aware analysis: The actor uses specialized skills to trace dependencies:

   
   # Pseudocode of what happens inside a strategy actor
   def compute_closure_for_refactoring(target_module):
       closure = ResourceClosure()
   <span style="opacity: 0.7;"># Direct file dependencies</span>
   closure.add_files(find_imports(target_module))
   closure.add_files(find_includes(target_module))
   
   <span style="opacity: 0.7;"># Symbol dependencies</span>
   <span style="color: magenta; font-weight: 600;">for</span> symbol <span style="color: magenta; font-weight: 600;">in</span> extract_exported_symbols(target_module):
       closure.add_files(find_symbol_usage(symbol, scope=<span style="color: #66cc66;">&#x27;project&#x27;</span>))
   
   <span style="opacity: 0.7;"># Test dependencies</span>
   closure.add_files(find_tests_for_module(target_module))
   
   <span style="opacity: 0.7;"># Build system dependencies</span>
   closure.add_files(find_build_references(target_module))
   
   <span style="color: magenta; font-weight: 600;">return</span> closure
   
   

2. Hierarchical scoping: When spawning child plans (via subplan_spawn or subplan_parallel_spawn), each child plan receives:

   • An explicit relevant_resources list
   • A sandbox_strategy appropriate for those resources
   • Clear boundaries of what it can and cannot modify
   • The parent plan's effective invariant view (already reconciled from action, project, and global scopes)

3. Decision-based tracking: Each subplan_spawn decision records the child plan it creates, and subplan_parallel_spawn decisions group parallel child plans:

   
   # Individual child plan spawn
   decision_type: subplan_spawn
   chosen_option: "Refactor authentication module"
   downstream_plan_ids: ["plan-auth-refactor-123"]
   artifacts_produced: 
     - auth_module_files: ["auth.rs", "auth_test.rs", "auth_types.rs"]
     - api_updates: ["api/v2/login.rs", "api/v2/logout.rs"]
   # Parallel group of child plans
   decision_type: subplan_parallel_spawn
   chosen_option: "Convert utility libraries in parallel"
   # Contains subplan_spawn children, each with their own downstream_plan_ids
   

Concrete example - Converting a subsystem to Rust:

Let's trace how the system handles "Convert Firefox's Network Stack to Rust":

STRATEGIZE PHASE:
1. Analyze network stack structure
   - Identifies 847 C++ files in netwerk/ directory
   - Traces public API surface (237 exported functions)
   - Maps internal dependencies (1,432 internal calls)


Compute minimal closure for Phase 1 (DNS resolver):

Core files: dns_resolver.cpp, dns_cache.cpp, dns_config.cpp (3 files)
Direct dependencies: 12 files in netwerk/base/
Test files: 8 test files specific to DNS
Build files: 2 moz.build files
Total closure: 25 files (not 847!)



Generate execution blueprint with child plans:

[subplan_parallel_spawn] DNS module conversions:

convert-dns-types: Closure of 5 files (type definitions)
convert-dns-cache: Closure of 8 files (cache + tests)
convert-dns-resolver: Closure of 12 files (resolver + integration)

Why this is tractable even for massive codebases:

The system leverages several key insights about real software:

1. Modular boundaries exist: Even in legacy codebases, there are natural boundaries
2. Changes are incremental: We don't convert 50,000 files atomically
3. Dependencies are sparse: Most modules depend on a small fraction of the codebase
4. Interfaces are narrow: Public APIs are much smaller than implementations

The Firefox example would decompose into ~1,000 bounded child plans (grouped via subplan_parallel_spawn decisions where independent), each touching 10-100 files. The parent plan tracks the overall architecture and enforces invariants, while each child plan maintains its focused closure.

How we prevent closure explosion:

• Lazy expansion: Dependencies are traced only as deep as needed for correctness
• Interface-based boundaries: When possible, work against stable interfaces
• Incremental validation: Each child plan validates its changes don't break dependents
• Hierarchical merge strategies: Parent plans resolve conflicts between child plan changes

Q: What mechanisms enforce global consistency during parallel execution across many files?

What exists today architecturally: The sandbox model combined with hierarchical plan execution provides strong guarantees about consistency during parallel execution. This isn't just process isolation - it's semantic isolation with intelligent merge strategies.

How the coordination mechanism prevents compound errors:

1. Complete isolation during execution: Each plan executes in its own sandbox, which means:

   
   Plan A (refactoring auth module):
   - Sandbox A1: Contains only auth/*.cpp, auth_tests/*.cpp
   - Cannot see Plan B's intermediate states
   - Cannot accidentally depend on Plan B's half-done work
   Plan B (updating API endpoints):
   
   Sandbox B1: Contains only api/.cpp, api_tests/.cpp
   Makes changes assuming current auth interface
   Protected from Plan A's intermediate refactoring
   

2. Resource-specific sandbox strategies provide natural coordination:

   
   Git repositories:
     - Strategy: git worktrees
     - Coordination: Git's three-way merge algorithm
     - Conflict detection: Built into Git
     - Rollback: git reset/checkout
   Databases:
   
   Strategy: Transaction isolation
   Coordination: MVCC (multi-version concurrency control)
   Conflict detection: Serialization failures
   Rollback: Transaction abort
   
   Cloud Infrastructure:
   
   Strategy: Terraform workspaces
   Coordination: State locking
   Conflict detection: Resource conflicts in plan
   Rollback: Previous state restoration
   

3. Hierarchical merge resolution: When child plans complete, the parent plan performs intelligent merging:

   
   def merge_subplan_results(subplan_results):
       # Group by resource type
       by_resource = group_by_resource_type(subplan_results)
   <span style="opacity: 0.7;"># Apply resource-specific merge strategies</span>
    <span style="color: magenta; font-weight: 600;">for</span> resource_type, changes <span style="color: magenta; font-weight: 600;">in</span> by_resource:
        <span style="color: magenta; font-weight: 600;">if</span> resource_type == <span style="color: #66cc66;">&#x27;git-checkout&#x27;</span>:
            merge_git_changes(changes)  <span style="opacity: 0.7;"># Three-way merge</span>
        <span style="color: magenta; font-weight: 600;">elif</span> resource_type == <span style="color: #66cc66;">&#x27;fs-mount&#x27;</span>:
            merge_fs_changes(changes)   <span style="opacity: 0.7;"># Copy-on-write reconciliation</span>
        <span style="color: magenta; font-weight: 600;">elif</span> resource_type.startswith(<span style="color: #66cc66;">&#x27;database&#x27;</span>):
            merge_db_changes(changes)   <span style="opacity: 0.7;"># Sequential application</span>
   
   <span style="opacity: 0.7;"># Validate merged state</span>
   run_integration_tests()
   
   

Concrete example - Preventing cascading failures:

Consider refactoring a shared authentication library used by 15 services:

PARALLEL EXECUTION WITHOUT COORDINATION (what we prevent):
- Service A refactors to async auth → breaks Service B
- Service B compensates with workaround → breaks Service C  
- Service C changes error handling → breaks Services D, E, F
- Cascade of failures!
CLEVERAGENTS COORDINATED EXECUTION:
Parent Plan: Refactor auth library
├── [invariant_enforced] "Auth library public API must remain backward compatible during transition"
├── [subplan_spawn] Plan 1: Update auth library interface
│   Sandbox: Only auth library files
│   Output: New interface definition
│

├── Barrier: Wait for Plan 1 completion
│

├── [subplan_parallel_spawn] Plans 2-16: Update each service (in parallel)
│   Each sandbox: Only that service's files
│   Each uses: New interface from Plan 1
│   No inter-service dependencies during execution
│

└── Merge Phase:
- Collect all service updates
- Apply to main branch in order
- Run integration tests
- If conflicts: Parent plan resolves using semantic understanding

Advanced coordination patterns:

1. Optimistic concurrency with semantic conflict resolution:

   
   Two child plans both modify api/user.rs:
   - Plan A: Adds async fn get_user_profile()
   - Plan B: Adds fn validate_user_permissions()
   Merge strategy:
   
   Git merge succeeds (different functions)
   Semantic validation ensures both functions work together
   Parent plan adds integration glue if needed
   

2. Checkpoint-based coordination:

   
   Execution timeline:
   T1: Plan A creates checkpoint before major refactor
   T2: Plan B creates checkpoint before API changes
   T3: Plan A encounters error, rolls back to T1
   T4: Plan B completes successfully
   T5: Plan A retries with knowledge of B's success
   

3. Resource locking for critical sections:

   
   When modifying shared schema files:
   - Acquire exclusive lock on schema resources
   - Make changes atomically
   - Release lock with new version
   - Other plans rebase on new schema
   

Q: How does the system proactively prevent semantic errors before they propagate?

What exists today architecturally: The specification defines multiple layers of proactive error prevention that go far beyond traditional testing. This is a comprehensive defense-in-depth approach that catches semantic errors before they can propagate.

Layer 1: Decision-time validation during Strategize:

Every decision includes semantic validation:

Decision: Refactor payment module to async
alternatives_considered:
  - "Convert to async/await patterns" (chosen)
  - "Use thread pool with channels" (rejected: doesn't integrate with async ecosystem)
  - "Keep synchronous with timeout" (rejected: doesn't solve core latency issue)
confidence_score: 0.85
validation_performed:
  - Checked all payment API consumers can handle async
  - Verified database driver supports async operations
  - Confirmed no regulatory requirement for sync processing

Layer 2: Execution-time semantic guards:

The execution actor uses a tool node that references the independently registered local/validate-api-compat tool:

actors:
  code_executor:
    type: graph
    skills:
      - local/semantic-validators    # Contains validation tools for LLM tool-calling
    nodes:
      - name: semantic_validator
        type: tool
        tool: local/validate-api-compat # Named tool from Tool Registry

The local/validate-api-compat tool (independently registered via agents tool add) performs semantic validation — not just syntax checking. It extracts API signatures, finds breaking changes, checks affected consumers, and either auto-migrates or raises a SemanticError for manual review.

Layer 3: Invariant enforcement through the decision tree:

Invariants are attached at four scopes (global, project, action, plan) and managed via the unified agents invariant command or --invariant flags on creation commands. When a plan enters Strategize, the Invariant Reconciliation Actor (set via --invariant-actor on actions/plans/projects, or globally via agents config set invariant-actor) computes the effective invariant view by applying precedence rules (plan > project > global) to resolve conflicts. Each effective invariant is recorded as an invariant_enforced decision, making them visible, correctable, and auditable:

# The system collects, reconciles, and enforces semantic invariants
class InvariantEnforcer:
    def compute_effective_invariants(self, plan):
        """Compute the effective invariant view using the Invariant Reconciliation Actor."""
        raw = self.collect_all_invariants(plan)
        reconciler = (
            self.get_plan_invariant_actor(plan)
            or self.get_project_invariant_actor(plan)
            or self.get_global_invariant_actor()
        )
        # Apply precedence: plan > project > global
        return reconciler.reconcile(raw, precedence=['plan', 'project', 'global'])
<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">collect_all_invariants</span>(self, plan):
    <span style="color: #66cc66;">&quot;&quot;&quot;Collect invariants from all scopes accessible to this plan.&quot;&quot;&quot;</span>
    invariants = []
    invariants.extend(self.get_global_invariants())
    <span style="color: magenta; font-weight: 600;">for</span> project <span style="color: magenta; font-weight: 600;">in</span> plan.projects:
        invariants.extend(self.get_project_invariants(project))
    invariants.extend(self.get_action_invariants(plan.action))
    invariants.extend(self.get_plan_invariants(plan))
    <span style="color: magenta; font-weight: 600;">return</span> invariants

<span style="opacity: 0.7;"># Example invariants at different scopes:</span>
<span style="opacity: 0.7;"># Global:  &quot;Payment processing must be idempotent&quot;</span>
<span style="opacity: 0.7;"># Project: &quot;Database transactions must complete within 5 seconds&quot;</span>
<span style="opacity: 0.7;"># Action:  &quot;Test files must not import production secrets&quot;</span>
<span style="opacity: 0.7;"># Plan:    &quot;All API calls over TCP must be mocked&quot;</span>

<span style="color: magenta; font-weight: 600;">def</span> <span style="color: cyan; font-weight: 600;">check_invariant_preservation</span>(self, changes, enforced_invariants):
    <span style="color: magenta; font-weight: 600;">for</span> invariant <span style="color: magenta; font-weight: 600;">in</span> enforced_invariants:
        <span style="color: magenta; font-weight: 600;">if</span> <span style="color: magenta; font-weight: 600;">not</span> self.verify_invariant(invariant, changes):
            <span style="color: magenta; font-weight: 600;">return</span> InvariantViolation(invariant, changes)
    <span style="color: magenta; font-weight: 600;">return</span> Success()

Layer 4: Predictive error prevention through pattern matching:

The system learns from past failures:

Error Pattern Database:
  - pattern: "Async conversion in payment module"
    historical_failures:
      - "Race condition in payment confirmation"
      - "Timeout handling breaks idempotency"
    preventive_checks:
      - "Add explicit transaction boundaries"
      - "Verify idempotency keys are preserved"
      - "Check distributed lock acquisition"

Concrete example - Preventing a subtle distributed systems bug:

Scenario: Refactoring a service to use event sourcing:

PROACTIVE CONTAINMENT IN ACTION:


Strategy Phase Semantic Analysis:

Decision: "Convert order service to event sourcing"
Semantic check: "Event sourcing requires eventual consistency"
Identifies: 3 services assume immediate consistency
Adds decision: "Update dependent services for eventual consistency"



Execution Phase Invariant Checking:

Detects: PaymentService.chargeCard() called after OrderCreated event
Semantic issue: Payment before order confirmation violates business rules
Automatic fix: Insert OrderConfirmed event requirement



Validation Node Catches Edge Case:

Discovers: Audit service expects synchronous order numbers
Impact: Async events break compliance reporting
Resolution: Add audit event buffer with guaranteed ordering



Pre-Apply Semantic Verification:

Simulates production event flow
Detects: Under high load, events can arrive out of order
Adds: Event ordering guarantees via vector clocks

Why this prevents issues that traditional testing misses:

Traditional tests check "does the code work?" Our semantic containment asks:

• Does it preserve business invariants?
• Does it maintain architectural patterns?
• Does it respect distributed systems principles?
• Does it handle the edge cases we've seen before?

Integration with Definition of Done (DoD):

Each plan's DoD includes semantic requirements:

definition_of_done:
  must:
    - "All API changes maintain backward compatibility"
    - "No increase in p99 latency"
    - "Audit trail remains complete"
  should:
    - "Improve code coverage by 10%"
    - "Reduce cyclomatic complexity"
  may:
    - "Optimize for memory usage"

The validation nodes enforce these semantics, not just test passage.

Q: How does the system balance human supervision with autonomous operation?

What exists today architecturally: The specification defines a comprehensive automation profile system with 13 individual boolean flags controlling every aspect of human-vs-automated operation. This isn't a binary human/AI split — it's a fine-grained matrix that can be adjusted per task, per project, or per organization.

How the automation profiles work in practice:

locked-down Profile:
  - Every phase transition pauses for human action
  - Every decision point pauses for human input
  - User sees: Context, alternatives, recommendation
  - User provides: Explicit choice or custom guidance
  - Use case: Critical production changes, first-time codebase exploration
supervised Profile:

Strategize runs automatically with autonomous decisions
System pauses before Execute for human review of strategy
Transient failures retried automatically
Use case: Projects where you trust planning but want to review before execution

trusted Profile:

Strategize and Execute run automatically with autonomous decisions
Validation failures self-fixed within strategy bounds
Child plans spawned automatically
System pauses before Apply for human diff review
Use case: Normal feature development, refactoring

autonomous Profile:

Everything automatic except Apply
Can revise its own strategy if execution hits a wall
Restores from checkpoints on failure
Use case: Well-understood projects, batch operations

full-auto Profile:

Complete end-to-end automation including Apply
No sandbox or checkpoint requirements
Unsafe tools allowed
Use case: Routine updates, CI/CD integration

The decision correction mechanism enables progressive automation:

The agents plan correct command is crucial for building trust:

# User observes AI made suboptimal choice
agents plan tree <plan_id>
# Sees: [Decision] "Use REST API for service communication"
# User knows gRPC would be better for this use case
agents plan correct <decision_id> --mode=revert </span>
--guidance "Use gRPC instead of REST. This service requires streaming
updates and binary protocol efficiency. Set up protocol
buffer definitions and generate client/server stubs."
# System:
# 1. Marks original decision as superseded
# 2. Creates new decision with user guidance
# 3. Recomputes ONLY affected downstream decisions
# 4. Preserves all unrelated work

Progressive trust building through automation profiles:

New users typically follow this progression:

1. Start with manual to understand system behavior
2. Move to supervised as confidence in planning builds
3. Adopt trusted for routine development tasks
4. Enable autonomous for well-understood projects
5. Use full-auto for low-risk batch operations

Real autonomy through semantic understanding:

True autonomy isn't about removing humans - it's about the system understanding when it needs help:

class AutonomyController:
    def assess_decision_confidence(self, decision, context):
        factors = {
            'past_success_rate': self.get_historical_success(decision.type),
            'codebase_familiarity': self.get_familiarity_score(context.project),
            'risk_assessment': self.evaluate_risk(decision),
            'invariant_complexity': self.analyze_invariants(decision)
        }
    confidence = self.compute_confidence(factors)
    
    <span style="color: magenta; font-weight: 600;">if</span> confidence &lt; self.threshold:
         <span style="color: magenta; font-weight: 600;">if</span> self.profile.auto_decisions_strategize:
            <span style="opacity: 0.7;"># Even in full automation, critical decisions escalate</span>
            <span style="color: magenta; font-weight: 600;">return</span> RequestHumanGuidance(decision, factors)
    
    <span style="color: magenta; font-weight: 600;">return</span> ProceedAutonomously(decision)

Concrete example - Autonomous handling of a complex refactoring:

Scenario: "Modernize legacy e-commerce system"
INITIAL PLAN (Full Automation Mode):

System analyzes 50,000 line codebase
Identifies modernization opportunities
Creates plan with 47 child plans

AUTONOMOUS EXECUTION WITH SMART ESCALATION:
[subplan_parallel_spawn] Plans 1-15: Update utility functions (executes autonomously)

Confidence: 0.95 (straightforward transformations)
Result: Success

[subplan_spawn] Plan 16: Refactor payment processing

Confidence: 0.4 (critical business logic)
[invariant_enforced] "Preserve exact penny rounding behavior"
Action: ESCALATES to human
Human provides: "Preserve exact penny rounding behavior"
Continues autonomously with constraint

[subplan_parallel_spawn] Plans 17-30: UI component updates (executes autonomously)

Confidence: 0.9 (isolated changes)
Result: Success

[subplan_spawn] Plan 31: Database schema migration

Detects: Would require 6-hour downtime
Action: ESCALATES to human
Human provides: "Use online migration with feature flags"
Re-plans with zero-downtime approach

[subplan_parallel_spawn] Plans 32-47: Complete autonomously

The path to greater autonomy:

The system becomes more autonomous through:

1. Learning from corrections:

   • Every correction teaches the system about user preferences
   • Patterns emerge: "This team always prefers gRPC for microservices"
   • Future decisions incorporate these learnings

2. Building project-specific context:

   • Each successful plan adds to project knowledge
   • System learns codebase patterns, team conventions, business rules
   • Confidence increases with familiarity

3. Hierarchical delegation:

   • Proven child plan patterns become fully autonomous
   • Human focuses on high-level decisions
   • System handles implementation details

4. Semantic safety nets:

   • Comprehensive invariant checking reduces risk
   • Rollback capabilities provide recovery path
   • Humans can trust system won't cause catastrophic failures

Q: What's actually implemented today versus planned for the future?

Concrete implementations in the architecture:

1. Decision Tree with Complete Context Capture

   • Full schema defined
   • Storage model specified
   • Correction mechanism detailed
   • Query patterns established

2. Hierarchical Plan System

   • Spawning mechanism defined (sequential subplan_spawn and parallel subplan_parallel_spawn)
   • Execution semantics specified (child plans are full Plans with their own decision trees)
   • Invariant inheritance from parent plans, projects, and global scope
   • Merge strategies documented
   • Failure handling described

3. Resource-Aware Sandbox Isolation

   • Multiple strategies defined (git worktree, filesystem overlay, transactions)
   • Lazy sandboxing for efficiency
   • Resource-specific merge algorithms
   • Cleanup behavior specified

4. Multi-Layer Error Prevention

   • Decision validation during planning
   • Semantic validation nodes
   • Invariant enforcement
   • Definition of Done checking

5. Graduated Automation Controls

   • Three levels clearly defined
   • Decision correction without full re-execution
   • Confidence-based escalation
   • Progressive trust building

Near-term implementations (architecture complete, engineering straightforward):

1. Memory Tier Management

   • Hot/warm/cold distinction clear
   • Context loading patterns defined
   • Just needs LRU cache and storage backend

2. Cross-Plan Learning

   • Decision history provides training data
   • Pattern extraction is standard ML
   • Confidence scoring is well-understood

3. Cost/Risk Estimation

   • Dedicated estimation actor role defined
   • Historical data provides baselines
   • Standard prediction problem

4. Extended Validation Patterns

   • Pluggable validation architecture
   • Project-specific rules as configuration
   • Industry patterns can be packaged

Research territory (requires innovation but architecture supports):

1. Optimal Context Selection for 100K+ file codebases

   • Current: Heuristic-based selection
   • Research: ML-driven relevance ranking
   • Architecture supports: Any selection algorithm can plug in

2. Automated Invariant Discovery

   • Current: User-defined invariants
   • Research: Mining invariants from code patterns
   • Architecture supports: Invariants are just validation rules

3. Cross-Project Knowledge Transfer

   • Current: Project-specific learning
   • Research: Generalized pattern recognition
   • Architecture supports: Cold tier can span projects

4. Fully Autonomous Recovery Strategies

   • Current: Rollback and retry with guidance
   • Research: Automatic error understanding and fixing
   • Architecture supports: Recovery is just another plan type

Why we can confidently handle Firefox-scale projects:

The architecture doesn't require magical AI breakthroughs. It requires:

• Hierarchical decomposition: ✓ Fully specified
• Bounded context operations: ✓ Dependency closure computation defined
• Parallel execution with isolation: ✓ Sandbox model complete
• Semantic validation: ✓ Multi-layer approach specified
• Progressive automation: ✓ Automation profiles and correction defined

The difference between handling a 1,000 file project and a 100,000 file project is:

• More child plans (hierarchical decomposition handles this)
• Larger cold storage (standard database scaling)
• Better context selection (improves with use but works with heuristics)
• More validation patterns (accumulate over time)

This isn't speculative architecture astronautics - it's applying proven distributed systems principles to AI agent coordination. The innovation is in the integration, not in requiring fundamental breakthroughs.