The 2026-05-10 default-ON flip of IMPLEMENTER_DISPATCHER_PREFETCH / IMPLEMENTER_DISPATCHER_PRECLONE put rich PR context and a pre-cloned worktree in the wrapper's prompt — but the deep `task` tool chain (implementation-worker → tier-dispatcher → tier-qwen-med → task-implementor) re-summarises the prompt at every level, so by the time task-implementor sees its input only BEGIN_PR_DIFF survives. The worker still called git-isolator-util (~82 s wasted) and re-issued Forgejo GETs for data the dispatcher had already fetched. This change introduces a filesystem-mediated handshake that is immune to prompt summarisation. The dispatcher writes two sentinel JSON files per cycle (workspace handoff next to the worktree; PR-context handoff in /tmp/cleveragents-implementer-handoff/) and the worker reads them via two new bash-allowed Python scripts. Missing / malformed / stale sentinels map to empty stdout, which is the worker's signal to fall through to the legacy GET / git-isolator-util. New modules: - tools/_pr_context_sentinel.py: dispatcher-side writer (atomic, with 200 KB per-field truncation and idempotent delete). - tools/implementer_workspace.py: worker-side reader CLI with `discover` and safety-checked `cleanup` subcommands. - tools/implementer_pr_context.py: worker-side reader CLI with `read --field <name>` for every prefetch field. Hooked into: - tools/_pr_clone.py: prepare_pr_worktree writes the workspace sentinel after worktree-add succeeds; WorktreeHandle.cleanup removes both worktree and sentinel; new _resolve_branch_for_sha populates the sentinel's branch field. - tools/dispatch_implementer.py: _prefetch_prompt writes the PR-context sentinel; _cleanup_clone_handle removes it. Two new skills (.opencode/skills/implementer-workspace, .opencode/skills/implementer-pr-context) and a rewrite of every "Pre-fetched section" wording in .opencode/agents/task-implementor.md to call the skills first and fall back to legacy GET only on empty stdout. Tests: 54 new (16 workspace CLI + 21 PR-context CLI + 7 sentinel writer + 10 _pr_clone integration). Full auto_agents suite: 1,123 passed, 3 skipped (was 1,069 before). Co-authored-by: Cursor <cursoragent@cursor.com>
CleverAgents Core
CleverAgents is a Python-first automation platform. It provides a unified agents CLI,
an interactive Textual TUI, embedded runtime, and service orchestration tools while
embracing modern Python tooling.
Highlights
- Unified CLI entry points:
cleveragentsandagents - Interactive TUI (
agents tui) — full-screen Textual app with multi-session tabs, persona switching, slash commands, reference picker, and context-sensitive F1 help - Persona system — YAML-backed personas bind actors, argument presets, and scope
references to named identities; persisted in
~/.config/cleveragents/personas/ - Session management — create, list, export, and import conversation sessions;
full JSON export/import for portability; Markdown transcript export (
--format md) for human-readable sharing - First-run experience —
ActorSelectionOverlayguides new users to pick an actor on first TUI launch; creates a"default"persona automatically - Server mode —
agents server connectconfigures a remote CleverAgents server; Kubernetes Helm chart ink8s/for production deployment - A2A integration — Agent-to-Agent protocol facade wires CLI and TUI to live application services (session, plan, registry, event)
- Permissions screen — TUI overlay for reviewing tool permission requests with unified, side-by-side, and context diff views; session-scoped allow/reject decisions
- Actor thought blocks — expandable reasoning trace widgets rendered inline in the conversation stream with muted styling
- UKO runtime — Universal Knowledge Ontology query interface, inference engine, and graph persistence for ACMS context strategies
- Database resource handler — full CRUD and checkpoint/rollback support for SQLite, PostgreSQL, MySQL, and DuckDB resources
- Estimation lifecycle —
actor.default.estimationconfig key wires an estimation actor into the Strategize-to-Estimate lifecycle hook - Shell danger detection — TUI shell mode (
!prefix) classifies commands by danger level (LOW → CRITICAL) and surfaces a warning overlay before executing destructive, privilege-escalating, or exfiltration-risk commands - Inline permission questions —
PermissionQuestionWidgetrenders single-file permission requests directly in the conversation stream with single-key shortcuts - Invariant reconciliation —
InvariantReconciliationActorruns automatically at every plan phase transition; failures block the transition and emitINVARIANT_VIOLATED - UKO provenance tracking — every typed triple now carries
sourceResource,validFrom, andisCurrentmetadata; a revision chain enables temporal queries - JSON-RPC 2.0 A2A wire format —
A2aRequest/A2aResponsefields renamed to standard JSON-RPC 2.0 names (method,id,result,error) - Fast Typer/Click-based interface with parity for help/version behavior
- Behavior-driven coverage via Behave and Robot Framework
- Nox automation for linting, typing, testing, docs, builds, and benchmarks
- MkDocs-powered documentation with CleverAgents branding
Quick Start
# clone the CleverAgents core repository
git clone https://git.cleverthis.com/cleveragents/core.git
cd core
# install dependencies
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,tests,docs]"
# set up pre-commit hooks and verify tooling
bash scripts/setup-dev.sh
# verify the CLI
agents --help
agents --version
Launch the TUI
# install TUI extra
pip install -e ".[tui]"
# launch the interactive terminal UI
agents tui
Inside the TUI:
- Type a message and press
Enterto chat with the active actor - Press
/to open the slash command overlay (67 commands across 14 groups) - Press
@to open the reference picker and insert file/resource references - Press
!to enter shell mode and run a subprocess command - Press
F1to toggle the context-sensitive help panel - Press
Ctrl+Tto cycle through argument presets for the active persona - Press
Ctrl+Qto quit
Session management
agents session create --actor openai/gpt-4o
agents session list
agents session export --session-id <ID> --output session.json
agents session import --input session.json
Server mode
# connect to a remote CleverAgents server
agents server connect --url https://my-server.example.com --token <TOKEN>
# check connection status
agents server status
Developing
Pre-commit hooks run automatically on every git commit (formatting, linting, type
checking, security scanning). To run checks manually:
# core validation
nox -s format # ruff auto-formatting
nox -s lint # ruff linting
nox -s typecheck # pyright type checking
nox -s unit_tests # behave unit tests
nox -s integration_tests # robot integration tests
# quality & security
nox -s security_scan # bandit security scanning
nox -s dead_code # vulture dead code detection
nox -s complexity # radon complexity analysis
nox -s pre_commit # run all pre-commit hooks
nox -s adr_compliance # verify ADR compliance
For the full quality automation guide, see docs/development/quality-automation.md.
Documentation
nox -s docs
nox -s serve_docs
Tests
Behave feature scenarios live under features/ and Robot suites under robot/. Use the Nox sessions above to execute them in parity with the implementation plan.
Observability
LangSmith tracing is optional and off by default. Enable it by exporting CLEVERAGENTS_LANGSMITH_ENABLED=true along with a project name and API key (CLEVERAGENTS_LANGSMITH_PROJECT, CLEVERAGENTS_LANGSMITH_API_KEY). The settings module automatically mirrors these values to LANGCHAIN_TRACING_V2, LANGCHAIN_PROJECT, and LANGCHAIN_API_KEY, so LangChain/LangGraph agents emit traces without extra wiring. Additional knobs such as CLEVERAGENTS_LANGSMITH_ENDPOINT, CLEVERAGENTS_LANGSMITH_USER_ID, and CLEVERAGENTS_LANGSMITH_TAGS are documented in docs/observability.md.
LLM provider configuration
CleverAgents ships with a LangChain/LangGraph powered provider registry that discovers whichever API keys you export and automatically selects the best available provider. The CLI now uses actors: select an actor with --actor <name> (or set a default via agents actor set-default). Actors embed provider/model choices; built-in actors are seeded from CLEVERAGENTS_DEFAULT_PROVIDER / CLEVERAGENTS_DEFAULT_MODEL, then fall back to the built-in order (openai → anthropic → google → azure → openrouter → groq → together → cohere → gemini).
Required environment variables
| Provider | Primary variables |
|---|---|
| OpenAI | OPENAI_API_KEY |
| Anthropic | ANTHROPIC_API_KEY |
GOOGLE_API_KEY or GOOGLE_GENAI_API_KEY |
|
| Azure OpenAI | AZURE_OPENAI_API_KEY plus AZURE_OPENAI_ENDPOINT/AZURE_OPENAI_DEPLOYMENT |
| OpenRouter | OPENROUTER_API_KEY (+ optional CLEVERAGENTS_OPENROUTER_ORGANIZATION for sanitized headers) |
| Gemini | GEMINI_API_KEY or GOOGLE_GEMINI_API_KEY |
| Cohere | COHERE_API_KEY |
| Groq | GROQ_API_KEY |
| Together | TOGETHER_API_KEY |
Set CLEVERAGENTS_DEFAULT_PROVIDER to pin the global provider (for example export CLEVERAGENTS_DEFAULT_PROVIDER=openai) and CLEVERAGENTS_DEFAULT_MODEL to lock in a model ID. When unset, the registry picks the first configured provider and uses its published default model such as gpt-4o for OpenAI or claude-sonnet-4-20250514 for Anthropic.
Diagnostics and testing shortcuts
agents diagnosticsprints whether the registry can see your credentials and which actor/provider is selected.agents tellandagents buildrequire--actor <name>unless a default actor is set; useagents actor set-default <name>to configure one.- Built-in actors (
<provider>/<model>) are immutable, custom actors must be namedlocal/<id>, and the default actor cannot be removed. Use--unsafewhen adding/updating configs marked unsafe; runtime only warns when invoking unsafe actors. CLEVERAGENTS_TESTING_USE_MOCK_AI=trueforces the in-repo mock provider so Behave/Robot suites never hit external APIs.- The full capability matrix (streaming, tool calls, JSON mode, etc.) is documented in
docs/reference/providers.md.