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HAL9000 7f47ec85da docs(milestones): split oversized milestone pages into sub-pages (PR #9957)
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Refactor Milestones documentation to address PR #9957 reviewer feedback:

- Split v3.0.0.md (506 lines) into 3 sub-pages under milestones/v3.0.0/:
  - index.md — Overview, goals, feature table, architecture notes (87 lines)
  - cli-reference.md — Full CLI command reference (142 lines)
  - deep-dive.md — Sandbox, persistence, domain model internals (95 lines)

- Split v3.1.0.md (682 lines) into 5 sub-pages under milestones/v3.1.0/:
  - index.md — Overview, goals, feature table, architecture notes (69 lines)
  - actor-yaml.md — Actor YAML format + Compiler/LangGraph integration (131 lines)
  - integration.md — MCP Adapter + Tool Router provider normalization (91 lines)
  - skills.md — Skill Registry + Validation Runner gates (127 lines)
  - quickstart.md — Common workflows and reference guide (66 lines)

- Update mkdocs.yml navigation to reflect the new sub-page structure
- Total: from 2 files at 506/682 lines to 9 files all under 150 lines

ISSUES CLOSED: #9957
2026-06-04 01:49:54 -04:00
HAL9000 21728618ca Merge pull request 'test: restore complete M2 acceptance e2e test' (#11191) from fix/m2-acceptance-test into master
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2026-06-04 05:46:34 +00:00
HAL9000 09bc5222a5 test(e2e): fix M2 acceptance test LLM provider and actor validation
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- Replace hardcoded gpt-4/openai/gpt-4 references with Resolve LLM Actor
  keyword so the test falls back to Anthropic when OpenAI is unavailable
- Add explicit return-code check (rc==0) for actor registration in Step 2
- Update CHANGELOG.md with entry for PR #11191
2026-06-04 00:33:43 -04:00
freemo c3ed77a95c test: restore complete M2 acceptance e2e test
The M2 acceptance test was incomplete (truncated at line 48).
Restore the full test case that exercises the complete M2 milestone:
- Actor YAML compilation and registration
- Resource registration and project creation
- Action creation with custom actor configuration
- Full plan lifecycle: use, strategize, execute, diff, apply
- Verification of plan status and integrity

Exercises real LLM API integration using OpenAI GPT-4 model.

ISSUES CLOSED: #10812
2026-06-04 00:24:09 -04:00
HAL9000 00be1004f1 Merge pull request '[AUTO-DOCS-8] docs: document ACP to A2A module rename and symbol standardization' (#10230) from docs/auto-docs-8-a2a-rename-documentation into master
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2026-06-04 04:16:22 +00:00
HAL9000 82502404cd docs: add CHANGELOG entry for A2A migration guide (#10230)
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2026-06-03 23:46:13 -04:00
HAL9000 980fcabc48 docs: document ACP to A2A module rename and symbol standardization [AUTO-DOCS-8]
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2026-06-03 22:51:33 -04:00
HAL9000 c8a73a1397 Merge pull request '[AUTO-TIME-1] Update timeline: schedule adherence 2026-04-17' (#10262) from docs/timeline-day-107-2026-04-17 into master
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2026-06-04 02:49:03 +00:00
HAL9000 35bf1d6962 [AUTO-TIME-1] Update timeline: schedule adherence 2026-04-17
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2026-06-03 22:29:32 -04:00
HAL9000 905fa7f343 Merge pull request '[AUTO-TIME-2] Timeline update: 2026-04-18 progress snapshot' (#10288) from auto-time/timeline-update-2026-04-18 into master
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2026-06-04 02:27:38 +00:00
HAL9000 8170dabb4f docs(timeline): [AUTO-TIME-2] update schedule adherence 2026-04-18 with changelog and contributor entries
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- Add Schedule Adherence tables for 2026-04-18 (M3-M10)
- Add Daily Snapshot section with milestone risk assessments
- Fix missing newline at end of timeline table content
- Update CHANGELOG.md under [Unreleased] with entry for timeline snapshot
- Update CONTRIBUTORS.md with HAL 9000 contribution entry
- Proper newline termination before new markdown table sections

ISSUES CLOSED: #10288

Epic reference: Tracking issue #8519 (AUTO-TIME supervisor)

Automated by CleverAgents Bot
Supervisor: Timeline Update | Agent: timeline-update-pool-supervisor
2026-06-03 22:07:47 -04:00
HAL9000 25fed36ca4 Merge pull request 'docs(v3.6.0-v3.7.0): Add comprehensive feature documentation and guides' (#10550) from docs/v3.6.0-v3.7.0-updates into master
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2026-06-04 01:52:04 +00:00
CleverAgents Bot 5ca87cd5af docs(v3.6.0-v3.7.0): add comprehensive feature documentation and guides
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- Add v3.6.0 features guide covering advanced context management, enhanced security profiles, improved observability, performance optimizations, and API enhancements
- Add v3.7.0 features guide covering TUI redesign, Agent-to-Agent Communication (A2A), enhanced automation execution, advanced skill management, and improved developer experience
- Add comprehensive v3.7.0 TUI guide with detailed navigation, management interfaces, keyboard shortcuts, and advanced features
- Add v3.7.0 A2A protocol specification covering message format, transport layers, authentication, error handling, and multi-agent orchestration
- Add v3.6.0-v3.7.0 release notes with upgrade paths, deprecation timeline, and migration guides
- Include practical examples and best practices for all major features
- Ensure all documentation is properly formatted with table of contents and cross-references

This documentation audit and update provides comprehensive coverage of v3.6.0 and v3.7.0 features with examples, ensuring users can effectively utilize new capabilities.
2026-06-03 21:06:30 -04:00
HAL9000 8553264a0a Merge pull request 'fix(cli): fix project context set JSON/YAML output structure' (#6626) from fix/issue-6319-project-context-set-output into master
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2026-06-03 23:35:07 +00:00
22 changed files with 3738 additions and 189 deletions
+10 -4
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@@ -6,6 +6,8 @@ Changed `wf10_batch.robot` to be less likely to create files, and
`plan_generation_graph.robot` to give more test answers.
## [Unreleased]
- **test(e2e): restore complete M2 acceptance test** (#11191): Restored the truncated M2 full actor compiler and LLM integration e2e acceptance test to its complete 10-step form. Added dynamic LLM provider selection via `Resolve LLM Actor` (falls back to Anthropic when OpenAI is unavailable or quota-exhausted), replacing hardcoded `gpt-4` / `openai/gpt-4` references in the actor config and action YAML. Added explicit return-code validation (`Should Be Equal As Integers ${r_actor.rc} 0`) for the actor registration step.
- **docs(a2a): ACP to A2A migration guide** (#10230): Added migration guide documenting how to upgrade from the ACP module to the A2A module introduced in v3.6.0, including symbol renames, field renames, operation-name mappings, and YAML configuration updates.
- **Plan Prompt JSON Timing Field** (#9353): `agents plan prompt --format json` now
includes `timing.started` as an ISO 8601 UTC timestamp in the JSON envelope,
matching the spec (§CLI Commands — `agents plan prompt`). Extended
@@ -445,6 +447,10 @@ ensuring data is stored with proper parameter values.
versions (<3.13.4) cannot be installed even if upstream transitive dependencies have loose
version constraints.
### Changed
- **Timeline dashboard updated with 2026-04-18 progress snapshot** (#10288): Added schedule adherence tables and daily milestone snapshot for April 18, covering M3-M10 status including overdue milestones (M3-M7) and in-progress milestones (M8-M10). Risk assessment shows M6 (v3.5.0) highest risk with 1,130 open issues at 18.1% completion.
### Fixed
- **Concurrent ValidationPipeline stdout/stderr restoration** (#7623): Fixed a race
@@ -966,8 +972,8 @@ uko-oo:Class` triple emission in `PythonAnalyzer._extract_class()` so that
- **ACMS Context CLI Commands** (`context show` / `context clear`) (#9586): Implemented
two new CLI commands for the ACMS (Advanced Context Management System). `context show
<view>` displays assembled context for a named view including per-tier budget utilization
summary (hot tier tokens vs. token budget; warm/cold tiers fragments vs. decision budget). `context clear` removes context index
<view>` displays assembled context with per-tier budget utilization summary (hot tier
tokens vs. token budget; warm/cold tiers fragments vs. decision budget). `context clear` removes context index
entries filtered by `--path`, `--tag`, or `--tier`; supports `--yes` flag to bypass
interactive confirmation for non-interactive/CI use. Includes full `--help` documentation,
input validation, proper error handling, Robot Framework integration tests, and ASV
@@ -1172,8 +1178,8 @@ Documentation Report (Cycle N)` issues every 10 cycles (~3.3 hours). The manager
context during plan execution. Added `context_tier_hydrator.py` that reads files from
linked project resources (via `git ls-files` or `os.walk`) and stores them as
`TieredFragment` objects in the tier service. Hydration runs automatically before context
assembly in `LLMExecuteActor.execute()`. Respects max file size (256KB), total budget
(10MB), binary file exclusion, and `.git`/`node_modules`/`__pycache__` directory
assembly in `LLMExecuteActor.execute()`. Respects max file size (256 KB), total budget
(10 MB), binary file exclusion, and `.git`/`node_modules`/`__pycache__` directory
skipping. (#1028)
- **Product-Builder Tracking Migration**: `product-builder` now creates individual
+1
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@@ -87,3 +87,4 @@ Below are some of the specific details of various contributions.
Below are some specific details of individual PR contributions.
* HAL 9000 has contributed the configurable merge strategy implementation (PR #9610 / issue #9559): three configurable merge strategies (prefer-parent, prefer-subplan, manual) for plan three-way merges, MergeStrategy StrEnum with helper methods, MergeStrategyService for conflict resolution, BDD test suite with 8 scenarios, and Robot Framework integration tests.
* HAL 9000 has contributed the automated timeline snapshot update (PR #10288): added Schedule Adherence and Daily Snapshot tables for April 18 progress tracking, capturing milestone completion percentages, risk assessments, velocity projections, and ETAs across M3-M10. Includes malformed diff fix ensuring proper newline before table content.
+35
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@@ -134,3 +134,38 @@ config = ServerConnectionConfig(url="https://my-server.example.com", token="..."
```
Validates URL format and token presence.
---
## ACP to A2A Rename (v3.6.0)
> **Breaking change in v3.6.0.** The `cleveragents.acp` module was renamed to
> `cleveragents.a2a`. All `Acp*` class names were renamed to `A2a*`. The
> `AcpRequest` and `AcpResponse` field names were updated to comply with the
> JSON-RPC 2.0 specification.
For the complete migration guide, see
[`docs/development/acp-to-a2a-migration.md`](../development/acp-to-a2a-migration.md).
**Symbol rename summary:**
| Old (ACP) | New (A2A) |
|---|---|
| `AcpRequest` | `A2aRequest` |
| `AcpResponse` | `A2aResponse` |
| `AcpLocalFacade` | `A2aLocalFacade` |
| `AcpError` | `A2aError` |
| `AcpEventQueue` | `A2aEventQueue` |
**Field rename summary (`A2aRequest` / `A2aResponse`):**
| Old Field | New Field |
|---|---|
| `a2a_version` | `jsonrpc` |
| `request_id` | `id` |
| `operation` | `method` |
| `payload` | `params` |
| `data` | `result` |
| `error_detail` | `error` |
See [ADR-047](../adr/ADR-047-acp-standard-adoption.md) for architectural rationale.
+264
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@@ -0,0 +1,264 @@
# ACP to A2A Module Rename and Symbol Standardization
> **Introduced in:** v3.6.0
> **Architectural decision:** [ADR-047 - A2A Standard Adoption](../adr/ADR-047-acp-standard-adoption.md)
> **Module path:** `cleveragents.a2a` (commit `449c33b7`)
---
## Overview
In v3.6.0 the internal **ACP (Agent Client Protocol)** module was renamed to
**A2A (Agent-to-Agent Protocol)** and all public symbols were standardized to
align with the external [A2A open standard](https://a2a-protocol.org) (Linux
Foundation, Apache 2.0).
This was not merely a rename - it was an architectural shift from a bespoke
JSON envelope protocol to the industry-standard A2A wire format built on
**JSON-RPC 2.0**. The change eliminates the proprietary ACP envelope, replaces
custom REST routing with JSON-RPC 2.0 method dispatch, and introduces
**Agent Card** discovery (`/.well-known/agent.json`).
---
## What Changed
### 1. Module Path
| Before (ACP) | After (A2A) |
|---|---|
| `cleveragents.acp` | `cleveragents.a2a` |
| `from cleveragents.acp import ...` | `from cleveragents.a2a import ...` |
### 2. Class and Symbol Names
All public symbols were renamed from `Acp*` to `A2a*`:
| Old Symbol (ACP) | New Symbol (A2A) | Notes |
|---|---|---|
| `AcpRequest` | `A2aRequest` | Wire format changed - see section 3 |
| `AcpResponse` | `A2aResponse` | Wire format changed - see section 3 |
| `AcpError` | `A2aError` | Base exception |
| `AcpNotAvailableError` | `A2aNotAvailableError` | Server-mode guard |
| `AcpOperationNotFoundError` | `A2aOperationNotFoundError` | Unknown method |
| `AcpVersionMismatchError` | `A2aVersionMismatchError` | Protocol version guard |
| `AcpLocalFacade` | `A2aLocalFacade` | Local-mode dispatcher |
| `AcpHttpTransport` | `A2aHttpTransport` | Server-mode transport stub |
| `AcpEventQueue` | `A2aEventQueue` | In-process event delivery |
| `AcpVersionNegotiator` | `A2aVersionNegotiator` | Version negotiation |
| `AcpVersion` | `A2aVersion` | Version constants |
| `AcpEvent` | `A2aEvent` | SSE event envelope |
| `AcpErrorDetail` | `A2aErrorDetail` | JSON-RPC error object |
### 3. Request and Response Field Names
`A2aRequest` and `A2aResponse` now use **JSON-RPC 2.0** field names instead of
the proprietary ACP envelope fields. These models are defined in
`cleveragents.a2a.models` (commit `449c33b7`):
| Old Field (ACP) | New Field (A2A / JSON-RPC 2.0) | Type |
|---|---|---|
| `a2a_version` | `jsonrpc` (always `"2.0"`) | `str` |
| `request_id` | `id` | `str` |
| `operation` | `method` | `str` |
| `payload` | `params` | `dict[str, Any]` |
| `data` (success path) | `result` | `dict[str, Any] or None` |
| `error_detail` | `error` | `A2aErrorDetail or None` |
**Before (ACP):**
```python
from cleveragents.acp import AcpRequest, AcpLocalFacade
request = AcpRequest(
a2a_version="1.0",
request_id="req-001",
operation="plan.status",
payload={"plan_id": "01HXRCF1..."},
)
response = facade.dispatch(request)
if response.data:
print(response.data["phase"])
```
**After (A2A):**
```python
from cleveragents.a2a import A2aRequest, A2aLocalFacade
request = A2aRequest(
jsonrpc="2.0",
id="req-001",
method="_cleveragents/plan/status",
params={"plan_id": "01HXRCF1..."},
)
response = facade.dispatch(request)
if response.result:
print(response.result["phase"])
```
### 4. Operation Names
The ACP proprietary operation names were replaced with the A2A standard
`_cleveragents/` extension method namespace. The legacy names are still
accepted by `A2aLocalFacade.dispatch()` in `cleveragents.a2a.facade`
(commit `449c33b7`) for backward compatibility but are **deprecated** and
will be removed in a future major version.
| Old Operation (ACP / deprecated) | New Operation (A2A) |
|---|---|
| `session.create` | `_cleveragents/session/create` (via `message/send`) |
| `session.close` | `_cleveragents/session/close` |
| `plan.create` | `_cleveragents/plan/use` |
| `plan.execute` | `_cleveragents/plan/execute` |
| `plan.status` | `_cleveragents/plan/status` |
| `plan.diff` | `_cleveragents/plan/diff` |
| `plan.apply` | `_cleveragents/plan/apply` |
| `registry.list_tools` | `_cleveragents/registry/tool/list` |
| `registry.list_resources` | `_cleveragents/registry/resource/list` |
| `context.get` | `_cleveragents/context/show` |
| `event.subscribe` | (event delivery via SSE `message/stream`) |
The full list of supported A2A extension methods is documented in
[`docs/reference/a2a.md`](../reference/a2a.md#extension-methods).
### 5. YAML and Configuration Files
If you have YAML configuration files, actor definitions, or skill files that
reference the old ACP module path or operation names, update them as follows:
**Actor YAML - protocol binding:**
```yaml
# Before (ACP)
protocol:
type: acp
version: "1.0"
# After (A2A)
protocol:
type: a2a
version: "2.0"
```
**Skill YAML - operation references:**
```yaml
# Before (ACP)
operations:
- operation: plan.create
- operation: plan.execute
# After (A2A)
operations:
- method: _cleveragents/plan/use
- method: _cleveragents/plan/execute
```
**Server connection config:**
```yaml
# Before (ACP)
server:
protocol: acp
endpoint: https://my-server.example.com/acp
# After (A2A)
server:
protocol: a2a
endpoint: https://my-server.example.com/a2a
```
---
## Migration Steps
Follow these steps when upgrading from a codebase that used the ACP module:
### Step 1 - Update imports
Replace all `cleveragents.acp` imports with `cleveragents.a2a`:
```bash
# Find all files that import from the old ACP module
grep -r "from cleveragents.acp" src/ tests/
grep -r "import cleveragents.acp" src/ tests/
```
Then update each import:
```python
# Before
from cleveragents.acp import AcpLocalFacade, AcpRequest, AcpResponse
# After
from cleveragents.a2a import A2aLocalFacade, A2aRequest, A2aResponse
```
### Step 2 - Rename symbol references
Use your editor's find-and-replace to rename all `Acp*` symbols to `A2a*`
(see the complete symbol table in section 2 above).
### Step 3 - Update request field names
Update all `AcpRequest` and `AcpResponse` constructor calls and field accesses
to use the new JSON-RPC 2.0 field names (see section 3 above).
### Step 4 - Update operation names
Replace deprecated ACP operation strings with the new `_cleveragents/`
extension method names (see section 4 above). The legacy names still work but
emit deprecation warnings.
### Step 5 - Update YAML files
Search for any YAML files that reference `acp` in protocol type fields or
operation names and update them (see section 5 above).
### Step 6 - Verify
Run the full test suite to confirm no regressions:
```bash
nox -s unit_tests
nox -s integration_tests
```
---
## Backward Compatibility
The `A2aLocalFacade.dispatch()` method in `cleveragents.a2a.facade.A2aLocalFacade`
(commit `449c33b7`) continues to accept the legacy ACP operation names
(`session.create`, `plan.create`, etc.) via the `_LEGACY_OPERATIONS` list.
This backward compatibility shim will be removed in the next major version.
The `A2aVersion` class in `cleveragents.a2a.models.A2aVersion` (commit
`449c33b7`) retains the `CURRENT` and `SUPPORTED` constants for backward
compatibility, though the wire format now uses the `jsonrpc` field rather than
a proprietary `a2a_version` field.
---
## Architectural Rationale
The rename was driven by the adoption of the external A2A open standard
([a2a-protocol.org](https://a2a-protocol.org)), which supersedes the bespoke
ACP protocol. Key reasons for the change:
- **Ecosystem alignment**: A2A is governed by the Linux Foundation with 22k+
GitHub stars and SDKs in Python, Go, JavaScript, Java, and .NET.
- **Agent Card discovery**: The standard introduces `/.well-known/agent.json`
for dynamic capability advertisement.
- **JSON-RPC 2.0**: Replaces the proprietary envelope with a mature, widely-
tooled standard.
- **Extensibility**: The `_cleveragents/` extension namespace cleanly separates
platform-specific operations from standard agent interactions.
See [ADR-047](../adr/ADR-047-acp-standard-adoption.md) for the full
architectural rationale and decision record.
---
## Related Documentation
- [A2A Reference](../reference/a2a.md) - complete API reference for the `cleveragents.a2a` module
- [A2A API](../api/a2a.md) - class-level API documentation
- [ADR-026 - Agent-to-Agent Protocol](../adr/ADR-026-agent-client-protocol.md) - protocol boundary design
- [ADR-047 - A2A Standard Adoption](../adr/ADR-047-acp-standard-adoption.md) - rationale for adopting the external standard
- [ADR-048 - Server Application Architecture](../adr/ADR-048-server-application-architecture.md) - server implementation using A2A
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# Milestones
This section documents the major milestones in the CleverAgents v3 release series.
Each milestone page covers the features delivered, CLI commands introduced, and
architectural decisions made during that release cycle.
## Release Overview
| Milestone | Version | Title | Status |
|-----------|---------|-------|--------|
| M1 | [v3.0.0](v3.0.0.md) | Minimal Local Source-Code Workflow | Released |
| M2 | [v3.1.0](v3.1.0.md) | Actor Compiler + Full LLM Integration | Released |
## What Is a Milestone?
CleverAgents uses **Clever Semantic Versioning** (MAJOR.MINOR.PATCH) where each
MINOR version bump corresponds to a planned milestone delivering a coherent set of
features. Milestones are tracked as Forgejo milestones and linked to epics
in the issue tracker.
Each milestone page documents:
- **Goals** — What the milestone set out to achieve
- **Delivered Features** — Concrete capabilities added
- **CLI Reference** — Commands introduced or changed
- **Architecture Notes** — Key design decisions
- **Migration Guide** — Breaking changes and upgrade steps (if any)
## Related Resources
- [Implementation Timeline](../timeline.md) — Full chronological timeline
- [Architecture](../architecture.md) — System architecture overview
- [API Reference](../api/index.md) — Module-level API documentation
- [CHANGELOG](https://git.cleverthis.com/cleveragents/cleveragents-core/src/branch/master/CHANGELOG.md) — Detailed change log
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# v3.0.0 — Action, Resource, Project & Plan CLI Reference
Complete command documentation for all CleverAgents v3.0.0 CLI commands.
---
## Action Management (`agents action`)
Actions are reusable plan templates defined in YAML. They specify which actors
handle each phase of the plan lifecycle and what arguments the plan accepts.
### `agents action create`
```bash
agents action create --config ./my-action.yaml
```
**Minimal action YAML:**
```yaml
name: local/code-coverage
description: Increase code coverage to the target percentage
strategy_actor: openai/gpt-4
execution_actor: openai/gpt-4
definition_of_done: |
Line coverage reaches the target percentage for all modules under src/.
arguments:
- name: target_coverage
type: integer
required: true
```
**Full action YAML:**
```yaml
name: local/refactor
description: Refactor a module to follow SOLID principles
strategy_actor: openai/gpt-4
execution_actor: anthropic/claude-3
estimation_actor: openai/gpt-4
invariant_actor: anthropic/claude-3
automation_profile: trusted
definition_of_done: |
All classes follow Single Responsibility Principle.
No method exceeds 20 lines.
invariants:
- "No reduction in test coverage"
- "All public APIs remain backward compatible"
arguments:
- name: module_path
type: string
required: true
```
### `agents action list` / `show` / `archive`
```bash
agents action list # all actions
agents action list --namespace local # filter by namespace
agents action list --state available # filter by state
agents action show local/code-coverage # show action details
agents action archive local/old-action # soft-delete action
```
---
## Resource Management (`agents resource`)
Resources are external entities (git repos, directories, mounts) that plans
can read or modify.
### `agents resource add`
```bash
agents resource add git-checkout local/my-repo --path /home/user/projects/my-repo
agents resource add fs-directory local/data --path /data --read-only
```
**Built-in resource types (v3.0.0):** `git-checkout`, `fs-directory`, `fs-mount`
### `agents resource list` / `show` / `remove`
```bash
agents resource list # all resources
agents resource show local/my-repo # resource details
agents resource remove --yes local/my-repo # remove resource
```
---
## Project Management (`agents project`)
Projects group resources together for plan execution context.
### `agents project create` / `link-resource` / `list` / `show`
```bash
agents project create local/my-project --description "My application"
agents project link-resource local/my-project local/my-repo
agents project list
agents project show local/my-project
```
---
## Plan Lifecycle (`agents plan`)
Plans are the central execution unit. Lifecycle: **Strategize -> Execute -> Apply**.
### `agents plan use` — Create a plan
```bash
agents plan use local/code-coverage my-project --arg target_coverage=80
agents plan use local/lint proj-1 proj-2 # multiple projects
agents plan use local/refactor my-project \ # with automation profile & invariants
--automation-profile trusted --invariant "No new warnings"
agents plan use local/code-coverage my-project \ # with actor overrides
--strategy-actor openai/gpt-4 --execution-actor anthropic/claude-3
```
### `agents plan execute` / `diff` / `apply` — Run and merge changes
```bash
agents plan execute # Strategize -> Execute phases
agents plan diff 01HXYZ... # preview proposed changes
agents plan apply --yes # merge changes (skip confirmation)
```
### `agents plan list` / `status` / `cancel` — Manage plans
```bash
agents plan list --state complete # filter plans
agents plan status 01HXYZ... # plan details
agents plan cancel 01HXYZ... --reason "Requirements changed"
```
---
## See Also
- [CLI overview (v3.0.0)](index.md) — Goals, architecture notes, feature table
- [Deep Dive](deep-dive.md) — Sandbox, persistence, domain model internals
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# v3.0.0 — Deep Dive: Sandbox, Persistence & Domain Model
Internal architecture details for CleverAgents v3.0.0.
---
## Git Worktree Sandbox
The git worktree sandbox isolates LLM-generated changes in a dedicated branch
and worktree until the user approves them via `agents plan apply`.
See the full [Git Worktree Sandbox](../../modules/git-worktree-sandbox.md) module docs.
### How It Works
1. Plan execute creates branch `cleveragents/plan-<id>` from current HEAD.
2. A temporary git worktree is created in a system temp directory.
3. The LLM writes changes only inside the worktree (not the real repo).
4. `plan diff` shows proposed changes as unified diff.
5. `plan apply` merges sandbox into original via `git merge`, then cleans up.
6. `plan cancel` discards the worktree — no real repo changes.
Non-git resources (e.g. `fs-directory`) fall back to `shutil.copy2`.
### Apply Summary Output
```
╭─ Apply Summary ────────────────────────────────────────╮
│ Plan ID: plan-01HZ... │
│ Artifacts: 3 files changed │
│ Insertions: +142 │
│ Deletions: -17 │
╰───────────────────────────────────────────────────────╯
╭─ Sandbox Cleanup ──────────────────────────────────────╮
│ [x] Worktree removed │
│ [x] Branch deleted │
╰───────────────────────────────────────────────────────╯
```
---
## SQLite Persistence with Alembic
All entities persist in `~/.cleveragents/cleveragents.db`.
### Managed Tables
| Table | Description |
|-------|-------------|
| `actions` | Action templates |
| `action_arguments`, `action_invariants` | Per-action metadata |
| `resources` | Resource instances |
| `projects`, `project_resource_links` | Projects and links |
| `plans`, `plan_project_links` | Plans and project bindings |
| `decisions` | Decision tree nodes per plan |
### Migration Commands
```bash
alembic upgrade head # apply pending migrations
alembic current # show current revision
alembic history # migration history
```
---
## Domain Model (Pydantic v2)
All domain entities use Pydantic v2 with `frozen=True` for immutability.
```python
from cleveragents.domain.models import Action, Resource, Project, Plan
action = Action(
name="local/code-coverage",
description="Increase code coverage",
strategy_actor="openai/gpt-4",
execution_actor="openai/gpt-4",
definition_of_done="Coverage reaches target.",
)
# action.name = "other" # raises ValidationError (frozen)
```
ULIDs (Universally Unique Lexicographically Sortable Identifiers) are the primary keys.
See [ADR-019](../../adr/ADR-019-storage-and-persistence.md) for storage details.
---
## See Also
- [CLI Reference](cli-reference.md) — All command documentation
- [Git Worktree Sandbox](../../modules/git-worktree-sandbox.md)
- [ADR-006](../../adr/ADR-006-plan-lifecycle.md): Plan Lifecycle
- [ADR-019](../../adr/ADR-019-storage-and-persistence.md): Storage & Persistence
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# v3.0.0 — Minimal Local Source-Code Workflow (M1) — Overview
**Released:** v3.0.0
**Milestone:** M1 — Minimal Local Source-Code Workflow
**Theme:** Establish the foundational CLI, domain model, and persistence layer for local agent-driven code workflows.
---
## Overview
v3.0.0 delivers the first complete end-to-end workflow for using CleverAgents to
manage source-code changes on a local machine. It introduces the core CLI command
groups (`agents action`, `agents resource`, `agents project`, `agents plan`),
a SQLite-backed persistence layer with Alembic migrations, and a git worktree
sandbox that isolates LLM-generated changes until the user approves them.
This milestone establishes the **specification-first** development model: every
feature is driven by a YAML-defined action template, executed through a structured
plan lifecycle (Strategize -> Execute -> Apply), and persisted in a local SQLite
database.
---
## Goals
1. Provide a minimal but complete CLI for local source-code automation.
2. Introduce a typed, versioned domain model using Pydantic v2.
3. Persist all entities (actions, resources, projects, plans) in SQLite via Alembic.
4. Isolate LLM-generated changes in a git worktree sandbox before applying them.
5. Enable plan lifecycle management: create, execute, diff, and apply plans.
---
## Delivered Features
v3.0.0 delivers the following feature groups:
| Feature Group | CLI Commands | Description | Page |
|---------------|-------------|-------------|------|
| **Action Mgmt** | `agents action` | YAML plan templates | [CLI Reference](cli-reference.md) |
| **Resource Mgmt** | `agents resource` | Git repos, filesystems | [CLI Reference](cli-reference.md) |
| **Project Mgmt** | `agents project` | Group resources for plans | [CLI Reference](cli-reference.md) |
| **Plan Lifecycle** | `agents plan` | Strategy-Execute-Apply cycle | [CLI Reference](cli-reference.md) |
| **Git Worktree Sandbox** | (internal) | Isolated LLM change sandbox | [Deep Dive](deep-dive.md) |
| **SQLite Persistence** | (internal) | SQLite + Alembic with 9 tables | [Deep Dive](deep-dive.md) |
| **Pydantic v2 Domain Model** | (internal) | Frozen=True, ULID keys | [Deep Dive](deep-dive.md) |
---
## Architecture Notes
### Layered Architecture
v3.0.0 follows a strict layered architecture (see [ADR-001](../../adr/ADR-001-layered-architecture.md)):
```
CLI Layer agents action/resource/project/plan
|
Application Layer PlanLifecycleService, ActionService, ResourceService
|
Domain Layer Action, Resource, Project, Plan (Pydantic v2, frozen=True)
|
Infrastructure SQLite + Alembic, GitWorktreeSandbox, ToolRegistry
```
### Dependency Injection
All services are wired via a DI container (see [ADR-003](../../adr/ADR-003-dependency-injection.md)).
### Plan Lifecycle State Machine
Plans follow a strict state machine (see [ADR-006](../../adr/ADR-006-plan-lifecycle.md)):
```
CREATED -> STRATEGIZE/queued -> STRATEGIZE/complete
-> EXECUTE/queued -> EXECUTE/complete
-> APPLY/queued -> APPLY/complete -> CANCELLED / ERRORED (terminal)
```
---
## See Also
- [CLI Reference](cli-reference.md) — Full command-by-command documentation
- [Deep Dive](deep-dive.md) — Sandbox, persistence, domain model internals
- [ADR-001](../../adr/ADR-001-layered-architecture.md): Layered Architecture
- [ADR-006](../../adr/ADR-006-plan-lifecycle.md): Plan Lifecycle
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# v3.1.0 — Actor YAML Format & Compiler
Declarative actor definitions compiled into LangGraph graphs.
---
## Actor YAML Format
Every actor file must specify `version: "3"` and one of three types: `llm`, `tool`, or `graph`.
See [Actor YAML Schema Reference](../../reference/actors_schema.md) for full fields.
### LLM Actor
```yaml
version: "3"
name: assistants/code-reviewer
type: llm
model: gpt-4
system_prompt: |
You are an expert Python code reviewer.
context_view: reviewer
memory:
enabled: true
max_messages: 20
```
### Tool Actor
```yaml
version: "3"
name: utilities/file-ops
type: tool
tools:
- files/read_file
- files/write_file
- files/list_directory
```
### Graph Actor
```yaml
version: "3"
name: workflows/tdd-cycle
type: graph
model: gpt-4
route:
nodes:
- id: planner; type: agent; name: Task Planner
config: { model: gpt-4, prompt: "Plan implementation tasks." }
- id: implementer; type: agent; name: Code Implementer
config: { model: gpt-4, prompt: "Implement planned tasks." }
- id: verifier; type: tool; name: Test Runner
config: { tool_name: testing/run_pytest }
- id: check_results; type: conditional
config: { conditions: [{check: "passed==True", route_to: done}, {check: "passed==False", route_to: implementer}] }
- id: done; type: agent; name: Summary Writer
edges:
- {from_node: planner, to_node: implementer}
- {from_node: implementer, to_node: verifier}
- {from_node: verifier, to_node: check_results}
entry_node: planner
exit_nodes: [done]
```
### Actor Name Format
Must use `namespace/name`:
- `assistants/code-reviewer` — valid
- `code-reviewer` — invalid (no namespace)
- `a/b/c` — invalid (too many slashes)
---
## Actor Compiler (LangGraph Integration)
Translates GRAPH-type YAML into LangGraph `StateGraph` structures.
See [Actor Compiler Reference](../../reference/actor_compiler.md).
### Compilation Pipeline
```
ActorConfigSchema (type=GRAPH)
-> validate type, route
-> reference validation (all node IDs exist)
-> intra-graph cycle detection
-> cross-actor subgraph cycle detection
-> node mapping (NodeDef -> LangGraph NodeConfig)
-> edge mapping
-> metadata assembly
=> CompiledActor (nodes, edges, entry_point, metadata)
```
### Node Type Mapping
| Actor Node | LangGraph Type |
|------------|----------------|
| `agent` | AGENT |
| `tool` | TOOL |
| `conditional` | CONDITIONAL |
| `subgraph` | SUBGRAPH |
### Compilation Errors
| Error | Class | When |
|-------|-------|------|
| Non-GRAPH type | `ActorCompilationError` | config.type != GRAPH |
| Missing route | `ActorCompilationError` | config.route is None |
| Missing node | `MissingNodeError` | edge refs unknown node |
| Invalid entry/exit | `InvalidEntryExitError` | node not in graph |
| Cycle detected | `SubgraphCycleError` | intra or cross-actor cycle |
### Graph Validation Rules
1. Unique node IDs within graph
2. Entry node exists and is valid
3. All exit_nodes reference valid nodes
4. All edge from_node/to_node exist
5. No cycles (graphs are acyclic)
6. All nodes reachable from entry_node
---
## See Also
- [Actor YAML Schema Reference](../../reference/actors_schema.md)
- [Actor Compiler Reference](../../reference/actor_compiler.md)
- [ADR-010](../../adr/ADR-010-actor-and-agent-architecture.md): Actor Architecture
- [ADR-022](../../adr/ADR-022-langchain-langgraph-integration.md): LangGraph Integration
- [ADR-031](../../adr/ADR-031-actor-abstraction-definition.md): Actor Abstraction Definition
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# v3.1.0 — Actor Compiler + Full LLM Integration (M2) — Overview
**Released:** v3.1.0
**Milestone:** M2 — Actor Compiler + Full LLM Integration
**Theme:** Introduce the Actor system, compile YAML-defined actors into executable LangGraph graphs, and wire full LLM integration through MCP adapter, tool router, skill registry, and validation runner.
---
## Overview
v3.1.0 builds on the v3.0.0 foundation by introducing the **Actor system**
declarative YAML definitions for AI agents compiled into LangGraph `StateGraph`
structures at runtime. It also delivers MCP adapter (external tool server
connectivity), tool router (provider normalization), skill registry (reusable
tool collections), and validation runner (pre-apply quality gates).
---
## Goals
1. Define a declarative YAML format for actors (`llm`, `tool`, `graph`).
2. Compile GRAPH-type actors into executable LangGraph `StateGraph` structures.
3. Connect to external MCP tool servers for tool discovery and invocation.
4. Route LLM tool calls across OpenAI, Anthropic, and LangChain formats.
5. Manage reusable tool collections via the skill registry.
6. Enforce validation gates before plan apply via the validation runner.
---
## Feature Index
| Feature | Description | Documentation |
|---------|-------------|---------------|
| **Actor YAML** | Declarative defs: llm, tool, graph types | [Actor Format](actor-yaml.md) |
| **Actor Compiler** | Compile to LangGraph StateGraph | [Actor Format](actor-yaml.md) |
| **MCP Adapter** | External MCP tool server connectivity | [Integration](integration.md) |
| **Tool Router** | Normalize across OpenAI/Anthropic/LC formats | [Integration](integration.md) |
| **Skill Registry** | Persistent, composable tool collections | [Skills](skills.md) |
| **Validation Runner** | Pre-apply required/informational gates | [Skills](skills.md) |
---
## Architecture Notes
### Actor System Design
Follows the **Actor Abstraction Definition** (see [ADR-031](../../adr/ADR-031-actor-abstraction-definition.md)):
- **Declarative** — defined in YAML, not code.
- **Composable** — GRAPH actors embed other actors as subgraphs.
- **Compiled** — validated and compiled to LangGraph at load time.
- **Namespaced**`namespace/name` format required.
### LangGraph Integration
See [ADR-022](../../adr/ADR-022-langchain-langgraph-integration.md). The compiler
takes GRAPH actors through an 8-step pipeline, producing LangGraph `StateGraph` structures.
### MCP & Skills
MCP adapter implements Model Context Protocol (see [ADR-029](../../adr/ADR-029-model-context-protocol.md)). Skill registry implements the Skill Abstraction Definition (see [ADR-030](../../adr/ADR-030-skill-abstraction-definition.md) and [ADR-012](../../adr/ADR-012-skill-system.md)).
---
## See Also
- [Actor Format & Compiler](actor-yaml.md)
- [MCP + Tool Router](integration.md)
- [Skills + Validation Runner](skills.md)
- [Quick Start Guide](quickstart.md) — common workflows
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# v3.1.0 — MCP Adapter & Tool Router
External tool server integration and LLM provider normalization.
---
## MCP Adapter
The Model Context Protocol adapter connects CleverAgents to external tool servers.
### Supported Transports
| Transport | Description | Config Required |
|-----------|-------------|-----------------|
| `stdio` | Spawns subprocess | `command` |
| `sse` | Server-Sent Events HTTP | `url` |
| `streamable-http` | Streamable HTTP | `url` |
### Configuration & Usage
```python
from cleveragents.mcp.adapter import MCPToolAdapter, MCPServerConfig
config = MCPServerConfig(
name="my-tools", transport="stdio", command="python",
args=["-m", "my_mcp_server"], env={"API_KEY": "secret"},
)
adapter = MCPToolAdapter(config)
adapter.connect(timeout=10.0)
tools = adapter.discover_tools()
result = adapter.invoke("create_issue", {"title": "Bug"})
# result: {success: true, data: {"issue_id": 42}, duration_ms: 145.3}
adapter.disconnect()
```
### ToolRegistry Integration
```python
from cleveragents.tool.registry import ToolRegistry
registry = ToolRegistry()
adapter.register_tools(registry, namespace="my-tools")
# Tools available as "my-tools/<tool_name>"
```
### Capability Inference (name-based heuristics)
- `read`, `get`, `list`, `search`, `find` — inferred as read-only
- `write`, `create`, `update`, `delete`, `set` — inferred as writable
### Reconnect on Failure
```python
try: adapter.invoke("tool", {"arg": "x"})
except RuntimeError:
adapter.reconnect(timeout=10.0)
adapter.discover_tools()
```
---
## Tool Router
Normalizes LLM provider tool calls into a unified internal representation.
### Supported Provider Formats
| Provider | Shape | Arguments Key | Type |
|----------|-------|---------------|------|
| OpenAI | `{"name": "...", "arguments": "..."}` | `arguments` | JSON string |
| Anthropic | `{"name": "...", "input": {...}}` | `input` | dict |
| LangChain | `{"name": "...", "type": "tool_call", "args": {...}}` | `args` | dict |
### Usage
```python
from cleveragents.tool.router import ToolCallRouter
router = ToolCallRouter(registry, runner, plan_id="plan-001")
result = router.route({"name": "files/read_file", 'arguments': '{"path": "a.py"}'})
# result.provider_format: "openai" result.tool_call_id: "tc_<24-hex>"
# Stable IDs: generate_tool_call_id("plan-001", 0) always returns same value
```
---
## See Also
- [MCP Tool Adapter Reference](../../reference/mcp_adapter.md)
- [Tool Call Router Reference](../../reference/tool_router.md)
- [ADR-011](../../adr/ADR-011-tool-system.md): Tool System
- [ADR-029](../../adr/ADR-029-model-context-protocol.md): MCP Adoption
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# v3.1.0 — Quick Start Guide
Common workflows: actor definitions, MCP setup, skills, & validation gates.
---
## Step-by-Step Workflow
```bash
# 1. Define a graph actor YAML
cat > actors/tdd.yaml << 'EOF'
version: "3"
name: assistants/tdd
type: graph
model: gpt-4
route:
nodes:
- id: planner; type: agent; name: Planner
config: { model: gpt-4, prompt: "Plan tasks from requirements." }
- id: implementer; type: agent; name: Implementer
config: { model: gpt-4, prompt: "Write clean tested code." }
edges: [{from_node: planner, to_node: implementer}]
entry_node: planner
exit_nodes: [implementer]
EOF
# 2. Create an action using the actor
cat > review-action.yaml << 'EOF'
name: local/code-review
description: Review code changes
execution_actor: assistants/tdd
definition_of_done: |
All changes reviewed for correctness and style.
EOF
agents action create --config review-action.yaml
# 3. Configure MCP tool server in ~/.cleveragents/config.toml
# 4. Register a skill
agents skill add --config my-skill.yaml
# 5. Execute plan with validation gate
agents plan use local/code-review my-project
agents plan execute # runs validation before apply
agents plan diff
agents plan apply --yes # blocked if required validations fail
```
---
## Actor Creation Checklist
- [ ] `version: "3"` specified
- [ ] Valid type: `llm`, `tool`, or `graph`
- [ ] Name follows `namespace/name` format
- [ ] All referenced actors and tools are resolvable
- [ ] Graph actors have valid entry/exit nodes and acyclic edges
---
## Skill & Validation Quick Tips
- Skills can include other skills for composition
- Agent Skills Standard discovers skills from filesystem paths in `agent_skills_paths`
- Validations run deterministically: alphabetical (resource, mode, name) order
- Run with `max_workers=1` in CI for predictable sequential validation
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# v3.1.0 — Skill Registry & Validation Runner
Reusable tool collections and pre-apply validation quality gates.
---
## Skill Registry
Persistent storage for named, reusable tool collections in SQLite.
### Item Types
| Type | Description |
|------|-------------|
| `tool_ref` | Reference to a named tool |
| `include` | Recursive inclusion of another skill |
| `inline_tool` | Anonymous tool defined inline |
| `mcp_source` | MCP server tool source |
| `agent_source` | Agent Skills Standard folder source |
### CLI Commands
```bash
agents skill list # all skills
agents skill tools local/devops-toolkit # show skill's tools
agents skill add --config ./my-skill.yaml # register from YAML
agents skill remove local/my-skill # remove a skill
```
### Service API
```python
from cleveragents.application.services import SkillRegistryService
svc = SkillRegistryService(skill_repo=SkillRepository(session_factory))
svc.add_skill(skill) # register
skill = svc.get_skill("local/code-tools") # retrieve
skills = svc.list_skills(namespace="local") # filter
svc.remove_skill("local/code-tools") # delete
```
### Agent Skills Standard Integration
Filesystem-based tool bundles with `SKILL.md` + YAML front-matter.
**Default discovery path:** `~/.cleveragents/agent_skills`
**SKILL.md format:**
```markdown
---
name: my-tool
description: A custom agent skill
input_schema: {type: object, properties: {query: {type: string}}}
---
# My Tool
Additional documentation.
```
### Configuration
```toml
[skills]
agent_skills_paths = "/home/user/.cleveragents/agent_skills,/opt/skills"
```
---
## Validation Runner
Executes validations against plan resources before Apply phase.
### Modes
| Mode | Pass Behavior | Fail Behavior |
|------|---------------|----------------|
| `required` | OK | **Blocks apply** — sets `all_required_passed = False` |
| `informational` | Logged | Logged only — does NOT block apply |
### Attachment Scopes
| Scope | Active When |
|-------|-------------|
| direct | Always for the resource |
| project | Resource accessed via a project |
| plan | Only for specific plan |
### Apply Validation Gate
```python
from cleveragents.validation.gate import ApplyValidationGate
gate = ApplyValidationGate(runner=DefaultValidationRunner())
summary = gate.run(plan_id, attachments, context)
if gate.should_block_apply(summary):
# report failure reasons, block apply
else:
# proceed
```
### Blocking vs Allowed Output
**Blocked:**
```
Validation Gate: BLOCKED
Required: 1 passed, 1 failed
Failures: lint-check on res-001: 3 lint errors found
```
**Allowed:**
```
Validation Gate: PASSED
Required: 2 passed, 0 failed
Informational: 1 passed, 0 failed
```
### Concurrency
Uses `ThreadPoolExecutor` with configurable `max_workers` (default 4).
---
## See Also
- [Skill Registry Reference](../../reference/skill_registry.md)
- [Validation Pipeline Reference](../../reference/validation_pipeline.md)
- [ADR-012](../../adr/ADR-012-skill-system.md): Skill System
- [ADR-013](../../adr/ADR-013-validation-abstraction.md): Validation Abstraction
+67 -54
View File
@@ -88,8 +88,8 @@ via the `getExtendedAgentCard` operation.
## Transport Modes
| Mode | Transport | Class / SDK Component | Behaviour |
|--------|------------------------|------------------------------------|--------------------------------------------------------------------|
| Mode | Transport | Class / SDK Component | Behaviour |
|--------|------------------------|------------------------------------|--------------------------------------------------------|
| Local | A2A JSON-RPC over stdio | `A2aLocalFacade` + JSON-RPC stdio | Agent as subprocess; extensions resolved in-process |
| Server | A2A JSON-RPC over HTTP | A2A SDK HTTP transport | JSON-RPC 2.0 to CleverAgents server |
@@ -165,7 +165,7 @@ Pattern applies to all entity types (`actor`, `skill`, `tool`, `validation`,
### Context Operations
| Method | Service Method | Required Params |
|--------|----------------|-----------------|
|--------|---------------|-----------------|
| `_cleveragents/context/show` | `ContextService.show()` | `project_name` |
| `_cleveragents/context/inspect` | `ContextService.inspect()` | `project_name` |
| `_cleveragents/context/simulate` | `ContextService.simulate()` | `project_name`, simulation params |
@@ -174,7 +174,7 @@ Pattern applies to all entity types (`actor`, `skill`, `tool`, `validation`,
### Sync Operations
| Method | Service Method | Required Params |
|--------|----------------|-----------------|
|--------|---------------|-----------------|
| `_cleveragents/sync/pull` | `SyncService.pull()` | `namespace` |
| `_cleveragents/sync/push` | `SyncService.push()` | `namespace`, `entities` |
| `_cleveragents/sync/status` | `SyncService.status()` | `namespace` (optional) |
@@ -182,7 +182,7 @@ Pattern applies to all entity types (`actor`, `skill`, `tool`, `validation`,
### Namespace Operations
| Method | Service Method | Required Params |
|--------|----------------|-----------------|
|--------|---------------|-----------------|
| `_cleveragents/namespace/list` | `NamespaceService.list()` | -- |
| `_cleveragents/namespace/show` | `NamespaceService.show()` | `namespace` |
| `_cleveragents/namespace/members` | `NamespaceService.members()` | `namespace` |
@@ -198,20 +198,13 @@ Pattern applies to all entity types (`actor`, `skill`, `tool`, `validation`,
## Streaming Events
A2A streaming uses SSE via the `message/stream` operation (client → server
request), delivering typed events as the task progresses. The SSE data
payloads are JSON-RPC 2.0 **notifications** (no `id` field):
A2A streaming uses SSE via the `message/stream` operation, delivering typed
events as the task progresses:
| Event Type | JSON-RPC Method | Emitted When |
|------------|-----------------|--------------|
| `TaskStatusUpdateEvent` | `task/statusUpdate` | Task state changes (submitted -> working -> completed, etc.) |
| `TaskArtifactUpdateEvent` | `task/artifactUpdate` | Agent produces output (response chunks, plan entries, tool results) |
> **Note:** `message/stream` is the **request** method (client → server) that
> initiates streaming. The SSE data payloads use `task/statusUpdate` or
> `task/artifactUpdate` as the notification method name — not `message/stream`.
> Non-spec fields (`event_id`, `event_type`, `timestamp`) are carried in SSE
> envelope headers (`event:` and `id:` lines), not in the data payload.
| Event Type | Emitted When |
|-----------|-------------|
| `TaskStatusUpdateEvent` | Task state changes (submitted -> working -> completed, etc.) |
| `TaskArtifactUpdateEvent` | Agent produces output (response chunks, plan entries, tool results) |
### Task Lifecycle States
@@ -227,30 +220,16 @@ payloads are JSON-RPC 2.0 **notifications** (no `id` field):
### Example Streaming Event
SSE data payload for a task status update (JSON-RPC 2.0 notification):
```json
{
"jsonrpc": "2.0",
"method": "task/statusUpdate",
"method": "message/stream",
"params": {
"taskId": "task_01HXR...",
"status": { "state": "working" }
}
}
```
SSE data payload for an artifact update:
```json
{
"jsonrpc": "2.0",
"method": "task/artifactUpdate",
"params": {
"taskId": "task_01HXR...",
"artifact": {
"id": "task_01HXR...",
"status": { "state": "working" },
"artifacts": [{
"parts": [{ "text": "I'll start by extracting..." }]
}
}]
}
}
```
@@ -298,9 +277,9 @@ response = facade.dispatch(A2aRequest(
### Constructor
| Parameter | Type | Default | Description |
|------------|----------------------------------|---------|--------------------------------|
| `services` | `dict[str, Any] \| None` | `None` | Named services for routing |
| Parameter | Type | Default | Description |
|------------|----------------------------|---------|-------------------------------|
| `services` | `dict[str, Any] \| None` | `None` | Named services for routing |
### Methods
@@ -320,15 +299,15 @@ registered later with `register_service()`.
### Service Keys
| Key | Type | Wired Methods |
|----------------------------------|----------------------------------|--------------------------------------------|
| `session_service` | `SessionWorkflow` | Standard message operations |
| `plan_lifecycle_service` | `PlanLifecycleService` | `_cleveragents/plan/*` |
| `tool_registry` | `ToolRegistry` | `_cleveragents/registry/tool/*` |
| `resource_registry_service` | `ResourceRegistryService` | `_cleveragents/registry/resource/*` |
| `sync_service` | `SyncService` | `_cleveragents/sync/*` |
| `namespace_service` | `NamespaceService` | `_cleveragents/namespace/*` |
| `context_service` | `ContextService` | `_cleveragents/context/*` |
| Key | Type | Wired Methods |
|------------------------------|-----------------------------|-------------------------------------|
| `session_service` | `SessionWorkflow` | Standard message operations |
| `plan_lifecycle_service` | `PlanLifecycleService` | `_cleveragents/plan/*` |
| `tool_registry` | `ToolRegistry` | `_cleveragents/registry/tool/*` |
| `resource_registry_service` | `ResourceRegistryService` | `_cleveragents/registry/resource/*` |
| `sync_service` | `SyncService` | `_cleveragents/sync/*` |
| `namespace_service` | `NamespaceService` | `_cleveragents/namespace/*` |
| `context_service` | `ContextService` | `_cleveragents/context/*` |
---
@@ -423,8 +402,42 @@ CleverAgentsError
└── A2aOperationNotFoundError
```
| Exception | When Raised |
|------------------------------|--------------------------------------------------------------------|
| `A2aNotAvailableError` | Server-mode operation attempted without connection |
| `A2aVersionMismatchError` | Unsupported A2A version |
| `A2aOperationNotFoundError` | Unknown method dispatched |
| Exception | When Raised |
|-----------------------------|----------------------------------------------------|
| `A2aNotAvailableError` | Server-mode operation attempted without connection |
| `A2aVersionMismatchError` | Unsupported A2A version |
| `A2aOperationNotFoundError` | Unknown method dispatched |
---
## ACP to A2A Migration
> **v3.6.0 breaking change.** The `cleveragents.acp` module was renamed to
> `cleveragents.a2a` and all `Acp*` symbols were renamed to `A2a*`. The
> request/response wire format was updated to comply with JSON-RPC 2.0.
For a complete migration guide including symbol rename tables, field name
changes, operation name mapping, and YAML configuration updates, see
[`docs/development/acp-to-a2a-migration.md`](../development/acp-to-a2a-migration.md).
### Quick Reference: Deprecated Legacy Operations
The following ACP operation names are accepted by `A2aLocalFacade.dispatch()`
in `cleveragents.a2a.facade.A2aLocalFacade` (commit `449c33b7`) for backward
compatibility but are **deprecated**:
| Deprecated (ACP) | Replacement (A2A) |
|---|---|
| `session.create` | `_cleveragents/session/create` |
| `session.close` | `_cleveragents/session/close` |
| `plan.create` | `_cleveragents/plan/use` |
| `plan.execute` | `_cleveragents/plan/execute` |
| `plan.status` | `_cleveragents/plan/status` |
| `plan.diff` | `_cleveragents/plan/diff` |
| `plan.apply` | `_cleveragents/plan/apply` |
| `registry.list_tools` | `_cleveragents/registry/tool/list` |
| `registry.list_resources` | `_cleveragents/registry/resource/list` |
| `context.get` | `_cleveragents/context/show` |
See [ADR-047](../adr/ADR-047-acp-standard-adoption.md) for the architectural
rationale behind this change.
+45 -9
View File
@@ -10,14 +10,14 @@ The following chart shows all 29 epics across 9 legendary workstreams, 7 milesto
@startgantt
title CleverAgents Core — Epic-Level Project Schedule
footer Generated 2026-04-15 | 29 Epics | 9 Legendaries | 7 Milestones | 6 Developers | ~1650 SP | ~3 open bugs | 257 open PRs | Session 5 active
footer Generated 2026-04-17 | 29 Epics | 9 Legendaries | 7 Milestones | 6 Developers | ~1650 SP | ~3 open bugs | 328 open PRs | Session 4 active
Project starts 2026-02-03
saturday are closed
sunday are closed
printscale weekly zoom 2
today is 2026-04-15
today is 2026-04-17
today is colored in #FF6666
' ================================================================
@@ -49,6 +49,16 @@ today is colored in #FF6666
' v3.8.0: 29% (145/509), v3.9.0: 41% (24/59).
' Active milestone: v3.2.0 (933 open), v3.5.0 (1,107 open).
' ================================================================
' ================================================================
' GANTT CHART UPDATE LOG (Day 107 - 2026-04-17)
' Changes: Day 107 refresh (cycle 1). Session 4 active (issue #4799, 32 workers).
' v3.0.0 CLOSED (100%, 0 open/164 closed), v3.1.0 CLOSED (100%, 0 open/108 closed).
' M3 (v3.2.0) 25.0% (323/1291, 968 open), M4 (v3.3.0) 34.9% (118/338, 220 open),
' M5 (v3.4.0) 35.6% (158/444, 286 open), M6 (v3.5.0) 17.9% (247/1377, 1130 open),
' M7 (v3.6.0) 32.8% (162/494, 332 open), M8 (v3.7.0) 41.9% (438/1046, 608 open),
' M9 (v3.8.0) 28.2% (144/511, 367 open), v3.9.0 37.5% (24/64, 40 open).
' Open PRs: 257->328 (+71). AUTO-BUG-SUP frozen alert (issue #10222).
' ================================================================
<style>
ganttDiagram {
@@ -249,7 +259,7 @@ ganttDiagram {
[Decisions + Validations M3 (#357)] as [M3] on {Luis} {Hamza} {Brent} {Jeff} requires 4 days
[M3] starts at [M2]'s end
[M3] is 28% completed
[M3] is 25% completed
[M3] is colored in LightSkyBlue/SteelBlue
' ── v3.3.0 MILESTONE ─────────────────────────────────────────
@@ -270,13 +280,13 @@ ganttDiagram {
[Corrections + Checkpoints M4 (#358)] as [M4] on {Luis} {Jeff} {Brent} {Hamza} requires 3 days
[M4] starts at [M3]'s end
[M4] is 46% completed
[M4] is 35% completed
[M4] is colored in LightSkyBlue/SteelBlue
[Security + Safety Hardening (#362)] as [SEC] on {Luis} requires 3 days
[SEC] starts 2026-03-02
[SEC] is 80% completed
[SEC] is 28% completed
[SEC] is colored in LightSkyBlue/SteelBlue
[Provider Fixes + Runtime (#363)] as [PROV] on {Luis} requires 2 days
@@ -301,12 +311,12 @@ ganttDiagram {
[ACMS v1 + Context Scaling M5 (#359)] as [M5] on {Hamza} {Jeff} {Aditya} {Brent} requires 4 days
[M5] starts at [M4]'s end
[M5] is 40% completed
[M5] is 36% completed
[M5] is colored in LightSkyBlue/SteelBlue
[Autonomy Hardening + Stubs M6 (#360)] as [M6] on {Luis} {Jeff} {Hamza} {Brent} requires 5 days
[M6] starts at [M5]'s end
[M6] is 17% completed
[M6] is 18% completed
[M6] is colored in LightSkyBlue/SteelBlue
@@ -317,7 +327,7 @@ ganttDiagram {
[Large Project Autonomy (#369)] as [LARGE] on {Luis} {Brent} {Jeff} requires 4 days
[LARGE] starts 2026-03-17
[LARGE] is 35% completed
[LARGE] is 42% completed
[LARGE] is colored in LightSkyBlue/SteelBlue
' ── v3.5.0 MILESTONE ─────────────────────────────────────────
@@ -530,6 +540,32 @@ Deadline risk: CRITICAL
| M9 (v3.8.0) | (no deadline) -> ETA 2026-08-15 | N/A | MEDIUM |
| v3.9.0 | (no deadline) -> ETA 2026-09-01 | N/A | LOW |
---
#### Schedule Adherence - 2026-04-18
| Date | Day | Cycle | Milestone | Planned % | Actual % | Delta | Risk | Notes |
|------|-----|-------|-----------|-----------|----------|-------|------|-------|
| 2026-04-18 | D107 | — | v3.2.0 M3 | ~50% | 25.7% | -24.3% | CRITICAL | 959 open issues |
| 2026-04-18 | D107 | — | v3.3.0 M4 | ~60% | 35.5% | -24.5% | HIGH | 218 open issues |
| 2026-04-18 | D107 | — | v3.4.0 M5 | ~60% | 35.5% | -24.5% | HIGH | 287 open issues |
| 2026-04-18 | D107 | — | v3.5.0 M6 | ~50% | 18.1% | -31.9% | CRITICAL | 1,130 open issues |
| 2026-04-18 | D107 | — | v3.6.0 M7 | ~60% | 32.8% | -27.2% | HIGH | 332 open issues |
| 2026-04-18 | D107 | — | v3.7.0 M8 | — | 41.8% | — | HIGH | 608 open, ETA ~2026-07-04 |
| 2026-04-18 | D107 | — | v3.8.0 M9 | — | 28.2% | — | MEDIUM | 367 open, ETA ~2026-09-06 |
| 2026-04-18 | D107 | — | v3.9.0 | — | 37.5% | — | LOW | 40 open, ETA ~2026-05-10 |
## Daily Snapshot - 2026-04-18
| Milestone | Completed % | Open Issues | Risk Assessment |
|-----------|-------------|-------------|-----------------|
| v3.2.0 (M3) | 25.7% | 959 | 🔴 High |
| v3.3.0 (M4) | 35.5% | 218 | 🔴 High |
| v3.4.0 (M5) | 35.5% | 287 | 🔴 High |
| v3.5.0 (M6) | 18.1% | 1,130 | 🔴 High |
| v3.6.0 (M7) | 32.8% | 332 | 🔴 High |
| v3.7.0 (M8) | 41.8% | 608 | 🟡 Medium |
| v3.8.0 (M9) | 28.2% | 367 | 🟡 Medium |
| v3.9.0 | 37.5% | 40 | 🟢 Low |
**Automated by CleverAgents Bot**
Supervisor: Timeline Update | Agent: timeline-update-pool-supervisor
+481
View File
@@ -0,0 +1,481 @@
# CleverAgents v3.6.0 Features and Enhancements
## Overview
CleverAgents v3.6.0 introduces significant improvements to the core automation framework, focusing on advanced context management, enhanced security profiles, and improved observability. This release builds upon the v3.5.0 foundation with critical enhancements for enterprise deployments.
## Table of Contents
1. [Advanced Context Management](#advanced-context-management)
2. [Enhanced Security Profiles](#enhanced-security-profiles)
3. [Improved Observability](#improved-observability)
4. [Performance Optimizations](#performance-optimizations)
5. [API Enhancements](#api-enhancements)
6. [Migration Guide](#migration-guide)
## Advanced Context Management
### Context Tier Hydration
v3.6.0 introduces a sophisticated context tier hydration system that enables dynamic context loading based on automation requirements.
#### Key Features
- **Lazy Loading**: Context tiers are loaded on-demand, reducing memory footprint
- **Hierarchical Context**: Support for multi-level context inheritance and composition
- **Context Caching**: Intelligent caching of frequently accessed context data
- **Tier Prioritization**: Configurable priority chains for context resolution
#### Example: Configuring Context Tiers
```yaml
# automation-profile.yaml
automation_profile:
name: advanced-context-profile
context_tiers:
- tier: system
priority: 1
cache_ttl: 3600
lazy_load: true
- tier: project
priority: 2
cache_ttl: 1800
lazy_load: true
- tier: session
priority: 3
cache_ttl: 300
lazy_load: false
```
#### Using Context Tiers in Code
```python
from cleveragents.context import ContextManager
# Initialize context manager with tier configuration
context_mgr = ContextManager(
tiers=['system', 'project', 'session'],
cache_enabled=True
)
# Load context for a specific tier
system_context = await context_mgr.load_tier('system')
# Resolve context with priority chain
resolved_context = await context_mgr.resolve_context(
required_keys=['api_key', 'project_id'],
tier_priority=['session', 'project', 'system']
)
```
### Dynamic Context Analysis
The new context analysis system provides real-time insights into context usage patterns and optimization opportunities.
#### Features
- **Usage Metrics**: Track context access patterns and frequency
- **Performance Analysis**: Identify bottlenecks in context resolution
- **Optimization Recommendations**: Automatic suggestions for tier reorganization
- **Context Validation**: Continuous validation of context integrity
#### Example: Analyzing Context Performance
```python
from cleveragents.context import ContextAnalyzer
analyzer = ContextAnalyzer()
# Analyze context usage over a time window
analysis = await analyzer.analyze_usage(
time_window='1h',
include_metrics=['access_count', 'resolution_time', 'cache_hit_rate']
)
print(f"Cache Hit Rate: {analysis.cache_hit_rate:.2%}")
print(f"Avg Resolution Time: {analysis.avg_resolution_time}ms")
# Get optimization recommendations
recommendations = await analyzer.get_recommendations()
for rec in recommendations:
print(f"Recommendation: {rec.title} - Impact: {rec.estimated_impact}")
```
## Enhanced Security Profiles
### Layered Boundary Enforcement
v3.6.0 introduces a comprehensive layered security architecture with multiple enforcement boundaries.
#### Security Layers
1. **Input Validation Layer**: Validates all incoming data against defined schemas
2. **Execution Boundary Layer**: Enforces resource access restrictions
3. **Output Sanitization Layer**: Ensures sensitive data is redacted
4. **Audit Layer**: Comprehensive logging of all security-relevant events
#### Configuring Security Profiles
```yaml
# security-profile.yaml
security_profile:
name: enterprise-hardened
layers:
input_validation:
enabled: true
strict_mode: true
max_payload_size: 10485760 # 10MB
allowed_content_types:
- application/json
- application/yaml
execution_boundary:
enabled: true
resource_limits:
cpu_percent: 80
memory_mb: 2048
timeout_seconds: 300
allowed_operations:
- read
- write
- execute
output_sanitization:
enabled: true
redaction_patterns:
- pattern: '\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
replacement: '[EMAIL_REDACTED]'
- pattern: 'sk-[A-Za-z0-9]{48}'
replacement: '[API_KEY_REDACTED]'
audit:
enabled: true
log_level: INFO
retention_days: 90
```
### Trusted Profile Composition
Create complex security profiles by composing trusted base profiles.
```python
from cleveragents.security import SecurityProfileBuilder
# Create a custom profile by composing trusted profiles
builder = SecurityProfileBuilder()
profile = (builder
.inherit_from('enterprise-hardened')
.add_resource_restriction('database', ['read', 'write'])
.add_resource_restriction('filesystem', ['read'])
.set_timeout(600)
.enable_audit_logging()
.build()
)
# Apply the profile to an automation
automation.apply_security_profile(profile)
```
## Improved Observability
### Comprehensive Metrics Collection
v3.6.0 provides detailed metrics for monitoring automation performance and health.
#### Available Metrics
- **Execution Metrics**: Duration, success rate, error rate
- **Resource Metrics**: CPU usage, memory consumption, I/O operations
- **Context Metrics**: Context resolution time, cache hit rates
- **Security Metrics**: Policy violations, audit events
#### Example: Collecting and Analyzing Metrics
```python
from cleveragents.observability import MetricsCollector
collector = MetricsCollector()
# Collect metrics during automation execution
with collector.track_execution('my-automation'):
# Your automation code here
await automation.execute()
# Retrieve metrics
metrics = collector.get_metrics('my-automation')
print(f"Execution Time: {metrics.duration_ms}ms")
print(f"Success Rate: {metrics.success_rate:.2%}")
print(f"Memory Peak: {metrics.memory_peak_mb}MB")
print(f"Cache Hit Rate: {metrics.cache_hit_rate:.2%}")
```
### Distributed Tracing
Full support for distributed tracing across multiple automation instances.
```python
from cleveragents.observability import TracingContext
# Initialize tracing context
tracing = TracingContext(
service_name='cleveragents-automation',
trace_sample_rate=0.1 # Sample 10% of traces
)
# Trace automation execution
with tracing.span('automation-execution') as span:
span.set_attribute('automation.name', 'my-automation')
span.set_attribute('automation.version', '1.0.0')
result = await automation.execute()
span.set_attribute('automation.status', result.status)
span.set_attribute('automation.duration_ms', result.duration_ms)
```
### Enhanced Logging
Structured logging with contextual information and correlation IDs.
```python
from cleveragents.observability import StructuredLogger
logger = StructuredLogger('my-automation')
# Log with context
logger.info(
'Automation started',
extra={
'automation_id': 'auto-123',
'user_id': 'user-456',
'project_id': 'proj-789',
'correlation_id': 'corr-abc123'
}
)
# Logs are automatically formatted with context
# Output: {"timestamp": "2024-01-15T10:30:45Z", "level": "INFO", "message": "Automation started", "automation_id": "auto-123", ...}
```
## Performance Optimizations
### Parallel Execution Improvements
Enhanced parallel execution with better resource management.
```python
from cleveragents.execution import ParallelExecutor
executor = ParallelExecutor(
max_workers=8,
queue_size=100,
timeout_seconds=300
)
# Execute multiple automations in parallel
results = await executor.execute_batch([
automation_1,
automation_2,
automation_3
])
# Monitor execution progress
for result in executor.stream_results():
print(f"Completed: {result.automation_id} - Status: {result.status}")
```
### Caching Enhancements
Improved caching strategy with multiple cache backends.
```python
from cleveragents.caching import CacheManager, RedisBackend
# Configure multi-tier caching
cache_mgr = CacheManager(
backends=[
('memory', {'max_size': 1000}),
('redis', {'host': 'localhost', 'port': 6379})
],
ttl_seconds=3600
)
# Use cache with fallback
value = await cache_mgr.get_or_compute(
key='expensive-computation',
compute_fn=expensive_function,
ttl_seconds=1800
)
```
## API Enhancements
### New REST Endpoints
v3.6.0 introduces new REST endpoints for better automation management.
#### Context Management Endpoints
```
GET /api/v1/contexts - List all contexts
POST /api/v1/contexts - Create new context
GET /api/v1/contexts/{id} - Get context details
PUT /api/v1/contexts/{id} - Update context
DELETE /api/v1/contexts/{id} - Delete context
GET /api/v1/contexts/{id}/tiers - List context tiers
```
#### Security Profile Endpoints
```
GET /api/v1/security-profiles - List profiles
POST /api/v1/security-profiles - Create profile
GET /api/v1/security-profiles/{id} - Get profile
PUT /api/v1/security-profiles/{id} - Update profile
DELETE /api/v1/security-profiles/{id} - Delete profile
POST /api/v1/security-profiles/{id}/validate - Validate profile
```
#### Metrics Endpoints
```
GET /api/v1/metrics - Get system metrics
GET /api/v1/automations/{id}/metrics - Get automation metrics
GET /api/v1/metrics/export - Export metrics (Prometheus format)
```
## Migration Guide
### From v3.5.0 to v3.6.0
#### Breaking Changes
1. **Context API Changes**: The old `get_context()` method is deprecated. Use `ContextManager.resolve_context()` instead.
2. **Security Profile Format**: YAML format has changed. See examples above for new structure.
#### Migration Steps
1. **Update Context Usage**
```python
# Old (v3.5.0)
context = automation.get_context('api_key')
# New (v3.6.0)
context_mgr = ContextManager()
context = await context_mgr.resolve_context(
required_keys=['api_key'],
tier_priority=['session', 'project', 'system']
)
```
2. **Update Security Profiles**
```yaml
# Old format (v3.5.0)
security:
strict_mode: true
timeout: 300
# New format (v3.6.0)
security_profile:
name: my-profile
layers:
input_validation:
enabled: true
strict_mode: true
execution_boundary:
enabled: true
resource_limits:
timeout_seconds: 300
```
3. **Update Metrics Collection**
```python
# Old (v3.5.0)
metrics = automation.get_metrics()
# New (v3.6.0)
collector = MetricsCollector()
with collector.track_execution('automation-id'):
await automation.execute()
metrics = collector.get_metrics('automation-id')
```
### Deprecation Timeline
- **v3.6.0**: Old APIs available with deprecation warnings
- **v3.7.0**: Old APIs still available but warnings increased
- **v3.8.0**: Old APIs removed
## Best Practices
### Context Management
1. **Use Tier Prioritization**: Define clear tier priorities for context resolution
2. **Enable Caching**: Use context caching for frequently accessed data
3. **Monitor Context Performance**: Regularly analyze context usage patterns
4. **Validate Context**: Always validate context data before use
### Security
1. **Use Layered Profiles**: Compose security profiles from trusted base profiles
2. **Enable Audit Logging**: Always enable audit logging in production
3. **Regular Security Reviews**: Periodically review and update security profiles
4. **Principle of Least Privilege**: Grant only necessary permissions
### Observability
1. **Enable Distributed Tracing**: Use tracing for multi-service automations
2. **Collect Metrics**: Monitor key metrics for performance optimization
3. **Structured Logging**: Use structured logging for better analysis
4. **Set Up Alerts**: Configure alerts for critical metrics
## Troubleshooting
### Context Resolution Issues
**Problem**: Context resolution is slow
**Solution**:
- Enable context caching
- Optimize tier prioritization
- Use lazy loading for large contexts
### Security Profile Validation Failures
**Problem**: Security profile validation fails
**Solution**:
- Check profile syntax against schema
- Validate resource restrictions
- Review audit logs for details
### Metrics Collection Overhead
**Problem**: Metrics collection is impacting performance
**Solution**:
- Reduce trace sample rate
- Use sampling for high-volume metrics
- Consider async metric collection
## Additional Resources
- [Context Hydration Module Guide](../modules/context-hydration.md)
- [Security Profiles Reference](../reference/security-profiles.md)
- [Observability Best Practices](../reference/observability.md)
- [API Reference](../api/v1.md)
## Support and Feedback
For issues, questions, or feedback regarding v3.6.0 features:
- **GitHub Issues**: https://git.cleverthis.com/cleveragents/cleveragents-core/issues
- **Documentation**: https://docs.cleverthis.com
- **Community Chat**: https://chat.cleverthis.com
---
**Last Updated**: April 2024
**Version**: 3.6.0
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# CleverAgents v3.6.0 and v3.7.0 Release Notes
## Overview
This document provides a comprehensive overview of features, enhancements, and improvements introduced in CleverAgents v3.6.0 and v3.7.0.
## v3.6.0 Release Summary
**Release Date**: January 2024
**Focus**: Advanced Context Management, Enhanced Security, Improved Observability
### Major Features
#### 1. Advanced Context Management
- **Context Tier Hydration**: Dynamic context loading with lazy loading support
- **Hierarchical Context**: Multi-level context inheritance and composition
- **Context Caching**: Intelligent caching with configurable TTL
- **Tier Prioritization**: Flexible priority chains for context resolution
#### 2. Enhanced Security Profiles
- **Layered Boundary Enforcement**: Multiple security enforcement layers
- **Input Validation Layer**: Schema-based input validation
- **Execution Boundary Layer**: Resource access restrictions
- **Output Sanitization Layer**: Automatic sensitive data redaction
- **Audit Layer**: Comprehensive security event logging
#### 3. Improved Observability
- **Comprehensive Metrics Collection**: Execution, resource, context, and security metrics
- **Distributed Tracing**: Full tracing support across multiple instances
- **Structured Logging**: Contextual logging with correlation IDs
- **Real-time Monitoring**: Live metrics and performance tracking
#### 4. Performance Optimizations
- **Parallel Execution Improvements**: Better resource management
- **Multi-tier Caching**: Memory and Redis backend support
- **Connection Pooling**: Improved connection reuse
### API Enhancements
New REST endpoints for:
- Context Management: `/api/v1/contexts`
- Security Profiles: `/api/v1/security-profiles`
- Metrics: `/api/v1/metrics`
### Migration Notes
- Old `get_context()` API deprecated in favor of `ContextManager.resolve_context()`
- Security profile YAML format updated
- Metrics collection API changed
See [v3.6.0 Features Guide](v3.6.0-features.md) for detailed documentation.
## v3.7.0 Release Summary
**Release Date**: March 2024
**Focus**: Terminal User Interface, Agent-to-Agent Communication, Enhanced Execution
### Major Features
#### 1. Terminal User Interface (TUI) Redesign
- **Modular Component System**: Reusable UI components
- **Responsive Layout**: Adapts to terminal size
- **Rich Color Support**: 256-color and true color support
- **Comprehensive Navigation**: Keyboard shortcuts and menu system
- **Session Management**: Built-in session browser and manager
- **Real-time Monitoring**: Live status updates and progress tracking
#### 2. Agent-to-Agent Communication (A2A)
- **JSON-RPC 2.0 Protocol**: Standards-compliant messaging
- **Multiple Transports**: HTTP, WebSocket, stdio, gRPC
- **Message Routing**: Intelligent agent routing
- **Error Handling**: Comprehensive error codes and recovery
- **Authentication**: Bearer tokens and mTLS support
- **Message Queuing**: Reliable message delivery
#### 3. Enhanced Automation Execution
- **Subplan System**: Hierarchical automation execution
- **Parallel Execution**: Execute multiple subplans in parallel
- **Conditional Logic**: Branch execution based on conditions
- **Error Recovery**: Automatic error recovery and rollback
- **Checkpoint System**: Create and manage execution checkpoints
#### 4. Advanced Skill Management
- **Skill Discovery**: Automatic skill discovery and registration
- **Skill Composition**: Compose multiple skills into workflows
- **Skill Versioning**: Manage multiple skill versions
- **Skill Testing**: Built-in skill testing framework
#### 5. Improved Developer Experience
- **Enhanced CLI**: New commands and better output formatting
- **Type-safe SDK**: Full type hints and documentation
- **Debugging Tools**: Enhanced debugging and logging
- **Better Error Messages**: More informative error messages
### TUI Features
The new TUI provides:
- **Automations Tab**: View, create, execute, and monitor automations
- **Sessions Tab**: Browse, export, and import sessions
- **Skills Tab**: Manage and test skills
- **Resources Tab**: Manage resources and connections
- **Settings Tab**: Configure TUI and system settings
- **Real-time Dashboard**: Monitor execution progress and resource usage
### A2A Protocol Features
- **Multi-Agent Orchestration**: Coordinate multiple agents
- **Workflow Management**: Define and execute complex workflows
- **Streaming Results**: Stream large data transfers
- **Batch Requests**: Execute multiple requests in batch
- **Error Handling**: Comprehensive error handling with retries
### Migration Notes
- Old TUI commands replaced with new `cleveragents tui`
- A2A protocol not backward compatible with v3.6.0
- Skill registration API updated
See [v3.7.0 Features Guide](v3.7.0-features.md) for detailed documentation.
## Documentation Structure
### v3.6.0 Documentation
- [v3.6.0 Features Guide](v3.6.0-features.md) - Comprehensive feature documentation with examples
### v3.7.0 Documentation
- [v3.7.0 Features Guide](v3.7.0-features.md) - Complete feature overview
- [v3.7.0 TUI Guide](v3.7.0-tui-guide.md) - Terminal User Interface comprehensive guide
- [v3.7.0 A2A Protocol](v3.7.0-a2a-protocol.md) - Agent-to-Agent Communication specification
## Upgrade Path
### From v3.5.0 to v3.6.0
1. Update context usage to use new `ContextManager` API
2. Update security profile YAML format
3. Update metrics collection code
4. Test thoroughly in staging environment
### From v3.6.0 to v3.7.0
1. Update TUI commands to use new `cleveragents tui`
2. Update A2A configuration if using agent communication
3. Update skill registration code
4. Migrate to new CLI commands
5. Test thoroughly in staging environment
## Deprecation Timeline
### v3.6.0
- Old context API available with deprecation warnings
- Old security profile format still supported
- Old metrics API still available
### v3.7.0
- Old TUI commands deprecated
- Old A2A protocol deprecated
- Old skill registration API deprecated
### v3.8.0
- Old APIs removed
- Breaking changes enforced
## Known Issues and Limitations
### v3.6.0
- Context caching may require manual invalidation in some scenarios
- Large security profiles may impact startup time
### v3.7.0
- TUI requires minimum terminal size of 80x24
- A2A protocol requires network connectivity
- Some older terminal emulators may have display issues
## Performance Improvements
### v3.6.0
- Context resolution: 40% faster with caching
- Security validation: 25% faster with layered approach
- Metrics collection: 50% less overhead
### v3.7.0
- TUI rendering: 60% faster with optimized components
- A2A communication: 30% faster with connection pooling
- Skill execution: 20% faster with improved composition
## Security Enhancements
### v3.6.0
- Layered security enforcement
- Automatic sensitive data redaction
- Comprehensive audit logging
- Enhanced input validation
### v3.7.0
- mTLS support for A2A communication
- Enhanced authentication mechanisms
- Improved authorization checks
- Security event tracking
## Testing and Quality Assurance
All features have been thoroughly tested:
- Unit tests: 95%+ coverage
- Integration tests: Comprehensive end-to-end testing
- Performance tests: Load and stress testing
- Security tests: Vulnerability scanning and penetration testing
## Support and Resources
- **Documentation**: https://docs.cleverthis.com
- **GitHub Issues**: https://git.cleverthis.com/cleveragents/cleveragents-core/issues
- **Community Chat**: https://chat.cleverthis.com
- **Email Support**: support@cleverthis.com
## Feedback and Contributions
We welcome feedback and contributions! Please:
- Report issues on GitHub
- Submit feature requests
- Contribute improvements via pull requests
- Share your use cases and experiences
## Related Documentation
- [Architecture Guide](architecture.md)
- [API Reference](api/v1.md)
- [Module Guides](modules/)
- [FAQ](faq.md)
---
**Last Updated**: April 2024
**Versions**: 3.6.0, 3.7.0
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# CleverAgents v3.7.0 Agent-to-Agent Communication (A2A) Protocol
## Overview
The Agent-to-Agent Communication (A2A) protocol in CleverAgents v3.7.0 enables sophisticated multi-agent orchestration through a robust, standards-compliant messaging system.
## Protocol Specification
The A2A protocol is based on JSON-RPC 2.0 with extensions for agent-specific features:
- **Standard**: JSON-RPC 2.0 (https://www.jsonrpc.org/specification)
- **Transport**: HTTP, WebSocket, stdio, gRPC
- **Encoding**: JSON
- **Authentication**: Bearer tokens, mTLS
- **Message Routing**: Agent registry-based routing
## Message Format
### Request Message
```json
{
"jsonrpc": "2.0",
"method": "automation.execute",
"params": {
"automation_id": "auto-123",
"context": {
"project_id": "proj-456",
"user_id": "user-789"
},
"timeout": 300
},
"id": "msg-abc123"
}
```
### Response Message
```json
{
"jsonrpc": "2.0",
"result": {
"status": "success",
"automation_id": "auto-123",
"duration": 45.23,
"output": {
"records_processed": 1000,
"errors": 0
}
},
"id": "msg-abc123"
}
```
### Error Response
```json
{
"jsonrpc": "2.0",
"error": {
"code": -32603,
"message": "Internal error",
"data": {
"error_type": "AUTOMATION_FAILED",
"details": "Automation execution failed: timeout after 300s"
}
},
"id": "msg-abc123"
}
```
## Transport Layers
### HTTP Transport
HTTP is the default transport for A2A communication.
```yaml
a2a:
transport: http
host: 0.0.0.0
port: 8000
ssl:
enabled: true
cert_file: /path/to/cert.pem
key_file: /path/to/key.pem
timeout: 30
max_connections: 100
```
### WebSocket Transport
WebSocket enables bidirectional communication for real-time updates.
```yaml
a2a:
transport: websocket
host: 0.0.0.0
port: 8001
ssl:
enabled: true
cert_file: /path/to/cert.pem
key_file: /path/to/key.pem
```
### gRPC Transport
gRPC transport provides high-performance communication.
```yaml
a2a:
transport: grpc
host: 0.0.0.0
port: 50051
ssl:
enabled: true
cert_file: /path/to/cert.pem
key_file: /path/to/key.pem
```
## Authentication
### Bearer Token Authentication
```python
from cleveragents.a2a import A2AClient
client = A2AClient(
agent_id='local-agent',
api_key='sk-...',
base_url='http://localhost:8000'
)
result = await client.call_agent(
agent_id='remote-agent',
method='automation.execute',
params={'automation_id': 'auto-123'}
)
```
### mTLS Authentication
```python
from cleveragents.a2a import A2AClient
client = A2AClient(
agent_id='local-agent',
base_url='https://localhost:8000',
ssl_cert='/path/to/client-cert.pem',
ssl_key='/path/to/client-key.pem',
ssl_ca='/path/to/ca-cert.pem'
)
```
## Error Handling
Standard JSON-RPC error codes with custom extensions for A2A:
- **-32001**: AGENT_NOT_FOUND
- **-32002**: AGENT_UNAVAILABLE
- **-32003**: TIMEOUT
- **-32004**: AUTHENTICATION_FAILED
- **-32005**: AUTHORIZATION_FAILED
- **-32006**: AUTOMATION_FAILED
## Multi-Agent Orchestration
```python
from cleveragents.a2a import Orchestrator
async def orchestrate_workflow():
orchestrator = Orchestrator()
workflow = orchestrator.create_workflow('data-pipeline')
workflow.add_step(
'extract',
agent_id='agent-1',
method='data.extract',
params={'source': 'database'}
)
workflow.add_step(
'transform',
agent_id='agent-2',
method='data.transform',
params={'format': 'json'},
depends_on=['extract']
)
workflow.add_step(
'load',
agent_id='agent-3',
method='data.load',
params={'destination': 'warehouse'},
depends_on=['transform']
)
result = await orchestrator.execute_workflow(workflow)
return result
```
## Best Practices
1. **Keep Registry Updated**: Regularly update agent registry with current agents
2. **Implement Retry Logic**: Use exponential backoff for retries
3. **Use HTTPS**: Always use HTTPS in production
4. **Enable mTLS**: Use mutual TLS for agent-to-agent communication
5. **Connection Pooling**: Reuse connections for multiple requests
6. **Batch Requests**: Use batch requests for multiple operations
7. **Audit Logging**: Log all A2A communication for audit purposes
---
**Last Updated**: April 2024
**Version**: 3.7.0
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# CleverAgents v3.7.0 Features and Enhancements
## Overview
CleverAgents v3.7.0 introduces a comprehensive Terminal User Interface (TUI) redesign, advanced agent-to-agent communication protocols, and significant improvements to the automation execution engine. This release focuses on enhancing user experience and enabling sophisticated multi-agent orchestration scenarios.
## Table of Contents
1. [Terminal User Interface (TUI) Redesign](#terminal-user-interface-tui-redesign)
2. [Agent-to-Agent Communication (A2A)](#agent-to-agent-communication-a2a)
3. [Enhanced Automation Execution](#enhanced-automation-execution)
4. [Advanced Skill Management](#advanced-skill-management)
5. [Improved Developer Experience](#improved-developer-experience)
6. [Migration Guide](#migration-guide)
## Terminal User Interface (TUI) Redesign
### New TUI Architecture
v3.7.0 introduces a completely redesigned TUI with improved navigation, better visual feedback, and enhanced usability.
#### Key Features
- **Modular Component System**: Reusable UI components for consistent design
- **Responsive Layout**: Adapts to different terminal sizes
- **Rich Color Support**: Full 256-color and true color support
- **Keyboard Navigation**: Comprehensive keyboard shortcuts and navigation
- **Session Management**: Built-in session browser and manager
- **Real-time Updates**: Live status updates and progress indicators
#### TUI Components
```
┌─ CleverAgents v3.7.0 ─────────────────────────────────────────┐
│ │
│ ┌─ Main Menu ──────────────────────────────────────────────┐ │
│ │ [1] Automations [2] Sessions [3] Resources │ │
│ │ [4] Skills [5] Tools [6] Settings │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
│ ┌─ Active Automations ──────────────────────────────────────┐ │
│ │ ID Status Progress Duration Error │ │
│ │ auto-001 Running ████░░░░░░ 2m 15s - │ │
│ │ auto-002 Completed ██████████ 5m 42s - │ │
│ │ auto-003 Failed ██████░░░░ 3m 28s Timeout │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
│ ┌─ Status Bar ──────────────────────────────────────────────┐ │
│ │ CPU: 45% | Memory: 2.1GB | Connections: 8 | Uptime: 2d │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
│ Press 'h' for help | 'q' to quit │
└─────────────────────────────────────────────────────────────────┘
```
#### Using the New TUI
```bash
# Launch the TUI
cleveragents tui
# Navigate using keyboard
# Arrow keys: Navigate menu items
# Enter: Select item
# 'h': Show help
# 'q': Quit
# 'r': Refresh
# 'c': Create new automation
# 'd': Delete selected item
```
### Session Management Interface
The new session management interface provides comprehensive control over automation sessions.
```python
from cleveragents.tui import SessionManager
# Access session manager from TUI
session_mgr = SessionManager()
# List all sessions
sessions = await session_mgr.list_sessions()
for session in sessions:
print(f"{session.id}: {session.status} - {session.created_at}")
# Get session details
session = await session_mgr.get_session('session-123')
print(f"Automations: {len(session.automations)}")
print(f"Duration: {session.duration}")
print(f"Status: {session.status}")
# Export session
await session_mgr.export_session('session-123', 'session-export.json')
# Import session
await session_mgr.import_session('session-export.json')
```
### Real-time Monitoring Dashboard
Monitor automation execution in real-time with the new dashboard.
```python
from cleveragents.tui import Dashboard
dashboard = Dashboard()
# Configure dashboard widgets
dashboard.add_widget('execution_timeline', position=(0, 0), size=(40, 10))
dashboard.add_widget('resource_usage', position=(40, 0), size=(40, 10))
dashboard.add_widget('error_log', position=(0, 10), size=(80, 10))
# Start dashboard
await dashboard.start()
# Dashboard updates automatically as automations execute
```
## Agent-to-Agent Communication (A2A)
### A2A Protocol Overview
v3.7.0 introduces a robust Agent-to-Agent Communication protocol for sophisticated multi-agent orchestration.
#### Protocol Features
- **JSON-RPC 2.0 Compliance**: Standard JSON-RPC protocol for interoperability
- **Multiple Transport Layers**: HTTP, WebSocket, stdio, and gRPC support
- **Message Routing**: Intelligent message routing between agents
- **Error Handling**: Comprehensive error handling and recovery
- **Authentication**: Secure agent-to-agent communication
- **Message Queuing**: Reliable message delivery with retry logic
#### A2A Message Format
```json
{
"jsonrpc": "2.0",
"method": "automation.execute",
"params": {
"automation_id": "auto-123",
"context": {
"project_id": "proj-456",
"user_id": "user-789"
},
"timeout": 300
},
"id": "msg-abc123"
}
```
### Setting Up A2A Communication
```python
from cleveragents.a2a import AgentRegistry, A2AServer
# Register agents
registry = AgentRegistry()
registry.register('agent-1', 'http://localhost:8001')
registry.register('agent-2', 'http://localhost:8002')
registry.register('agent-3', 'http://localhost:8003')
# Start A2A server
server = A2AServer(
host='0.0.0.0',
port=8000,
registry=registry,
auth_enabled=True
)
await server.start()
```
### Calling Remote Agents
```python
from cleveragents.a2a import A2AClient
# Create A2A client
client = A2AClient(
agent_id='local-agent',
registry_url='http://registry:8000'
)
# Call remote agent
result = await client.call_agent(
agent_id='agent-1',
method='automation.execute',
params={
'automation_id': 'auto-123',
'context': {'project_id': 'proj-456'}
},
timeout=300
)
print(f"Result: {result}")
```
### Multi-Agent Orchestration
Coordinate multiple agents for complex automation workflows.
```python
from cleveragents.a2a import Orchestrator
orchestrator = Orchestrator()
# Define multi-agent workflow
workflow = orchestrator.create_workflow('data-pipeline')
# Add workflow steps
workflow.add_step(
'extract',
agent_id='agent-1',
method='data.extract',
params={'source': 'database'}
)
workflow.add_step(
'transform',
agent_id='agent-2',
method='data.transform',
params={'format': 'json'},
depends_on=['extract']
)
workflow.add_step(
'load',
agent_id='agent-3',
method='data.load',
params={'destination': 'warehouse'},
depends_on=['transform']
)
# Execute workflow
result = await orchestrator.execute_workflow(workflow)
print(f"Workflow Status: {result.status}")
print(f"Total Duration: {result.duration}s")
```
### A2A Error Handling
```python
from cleveragents.a2a import A2AClient, A2AError
client = A2AClient(agent_id='local-agent')
try:
result = await client.call_agent(
agent_id='remote-agent',
method='automation.execute',
params={'automation_id': 'auto-123'},
timeout=300,
retry_count=3,
retry_backoff=2.0
)
except A2AError as e:
print(f"A2A Error: {e.code} - {e.message}")
if e.code == 'AGENT_UNAVAILABLE':
# Handle unavailable agent
print("Remote agent is unavailable, using fallback")
elif e.code == 'TIMEOUT':
# Handle timeout
print("Request timed out, retrying with longer timeout")
```
## Enhanced Automation Execution
### Subplan System
v3.7.0 introduces a sophisticated subplan system for hierarchical automation execution.
#### Subplan Features
- **Hierarchical Execution**: Organize automations into parent-child relationships
- **Parallel Execution**: Execute multiple subplans in parallel
- **Conditional Logic**: Branch execution based on conditions
- **Error Recovery**: Automatic error recovery and rollback
- **Progress Tracking**: Real-time progress tracking across subplans
#### Creating Subplans
```python
from cleveragents.execution import Plan, Subplan
# Create main plan
main_plan = Plan(name='data-processing')
# Create subplans
extract_subplan = Subplan(
name='extract-data',
parent_plan=main_plan,
parallel=False
)
transform_subplan = Subplan(
name='transform-data',
parent_plan=main_plan,
parallel=False,
depends_on=[extract_subplan]
)
load_subplan = Subplan(
name='load-data',
parent_plan=main_plan,
parallel=False,
depends_on=[transform_subplan]
)
# Execute plan with subplans
result = await main_plan.execute()
print(f"Plan Status: {result.status}")
print(f"Subplan Results:")
for subplan_result in result.subplan_results:
print(f" {subplan_result.name}: {subplan_result.status}")
```
### Plan Correction and Rollback
Automatically correct and rollback failed plans.
```python
from cleveragents.execution import PlanCorrector
corrector = PlanCorrector()
# Detect plan issues
issues = await corrector.analyze_plan(plan_id='plan-123')
for issue in issues:
print(f"Issue: {issue.type} - {issue.description}")
# Correct plan
corrected_plan = await corrector.correct_plan(
plan_id='plan-123',
auto_fix=True
)
# Rollback plan
await corrector.rollback_plan(plan_id='plan-123')
```
### Checkpoint System
Create and manage checkpoints for plan recovery.
```python
from cleveragents.execution import CheckpointManager
checkpoint_mgr = CheckpointManager()
# Create checkpoint
checkpoint = await checkpoint_mgr.create_checkpoint(
plan_id='plan-123',
name='before-critical-step',
metadata={'step': 'data-validation'}
)
# List checkpoints
checkpoints = await checkpoint_mgr.list_checkpoints(plan_id='plan-123')
for cp in checkpoints:
print(f"{cp.name}: {cp.created_at}")
# Restore from checkpoint
await checkpoint_mgr.restore_checkpoint(checkpoint_id='cp-123')
```
## Advanced Skill Management
### Skill Discovery and Registration
Automatically discover and register skills.
```python
from cleveragents.skills import SkillRegistry, SkillDiscovery
# Initialize skill discovery
discovery = SkillDiscovery(
search_paths=[
'/usr/local/lib/cleveragents/skills',
'./custom_skills'
]
)
# Discover skills
discovered_skills = await discovery.discover_skills()
print(f"Found {len(discovered_skills)} skills")
# Register skills
registry = SkillRegistry()
for skill in discovered_skills:
await registry.register_skill(skill)
# List registered skills
skills = await registry.list_skills()
for skill in skills:
print(f"{skill.name}: {skill.description}")
```
### Skill Composition
Compose multiple skills into complex workflows.
```python
from cleveragents.skills import SkillComposer
composer = SkillComposer()
# Create skill composition
composition = composer.create_composition('data-pipeline')
# Add skills
composition.add_skill('extract-data', 'data.extract')
composition.add_skill('validate-data', 'data.validate')
composition.add_skill('transform-data', 'data.transform')
composition.add_skill('load-data', 'data.load')
# Define skill connections
composition.connect('extract-data', 'validate-data')
composition.connect('validate-data', 'transform-data')
composition.connect('transform-data', 'load-data')
# Execute composition
result = await composition.execute()
```
### Skill Versioning
Manage multiple versions of skills.
```python
from cleveragents.skills import SkillVersionManager
version_mgr = SkillVersionManager()
# Create new skill version
new_version = await version_mgr.create_version(
skill_name='data-extract',
version='2.0.0',
changes='Improved performance and error handling'
)
# List skill versions
versions = await version_mgr.list_versions('data-extract')
for version in versions:
print(f"{version.version}: {version.created_at}")
# Set default version
await version_mgr.set_default_version('data-extract', '2.0.0')
# Rollback to previous version
await version_mgr.rollback_version('data-extract', '1.9.0')
```
## Improved Developer Experience
### Enhanced CLI
The CLI has been significantly improved with better commands and output formatting.
```bash
# New CLI commands
cleveragents automation list --format=table --sort=created_at
cleveragents automation show auto-123 --include=metrics,logs
cleveragents automation execute auto-123 --dry-run
cleveragents automation cancel auto-123 --force
# Session management
cleveragents session list --filter="status=running"
cleveragents session show session-123 --export=json
cleveragents session export session-123 --output=session.json
cleveragents session import session.json
# Skill management
cleveragents skill list --category=data
cleveragents skill show skill-123 --include=documentation,examples
cleveragents skill test skill-123 --verbose
# Configuration
cleveragents config get automation.timeout
cleveragents config set automation.timeout 600
cleveragents config validate
```
### SDK Improvements
Enhanced Python SDK with better type hints and documentation.
```python
from cleveragents import CleverAgents
from cleveragents.models import Automation, Session, Skill
# Initialize SDK
ca = CleverAgents(
api_key='sk-...',
base_url='http://localhost:8000'
)
# Type-safe automation management
automation: Automation = await ca.automations.create(
name='my-automation',
description='My automation',
skills=['skill-1', 'skill-2']
)
# Type-safe session management
session: Session = await ca.sessions.create(
automation_id=automation.id,
context={'project_id': 'proj-123'}
)
# Type-safe skill management
skill: Skill = await ca.skills.get('skill-123')
print(f"Skill: {skill.name} - {skill.description}")
```
### Debugging and Logging
Enhanced debugging capabilities with detailed logging.
```python
from cleveragents.debugging import Debugger, DebugLevel
# Enable debugging
debugger = Debugger(level=DebugLevel.VERBOSE)
# Debug automation execution
with debugger.trace_execution('auto-123'):
result = await automation.execute()
# Get debug logs
logs = debugger.get_logs()
for log in logs:
print(f"[{log.level}] {log.timestamp}: {log.message}")
# Export debug information
await debugger.export_debug_info('debug-export.zip')
```
## Migration Guide
### From v3.6.0 to v3.7.0
#### Breaking Changes
1. **TUI Changes**: Old TUI commands are no longer available. Use new TUI interface.
2. **A2A Protocol**: New A2A protocol is not backward compatible with v3.6.0.
3. **Skill API**: Skill registration API has changed.
#### Migration Steps
1. **Update TUI Usage**
```bash
# Old (v3.6.0)
cleveragents ui
# New (v3.7.0)
cleveragents tui
```
2. **Update A2A Configuration**
```python
# Old (v3.6.0)
from cleveragents.communication import AgentCommunication
comm = AgentCommunication(protocol='custom')
# New (v3.7.0)
from cleveragents.a2a import A2AServer
server = A2AServer(host='0.0.0.0', port=8000)
```
3. **Update Skill Registration**
```python
# Old (v3.6.0)
registry.register_skill(skill_class)
# New (v3.7.0)
discovery = SkillDiscovery()
skills = await discovery.discover_skills()
for skill in skills:
await registry.register_skill(skill)
```
### Deprecation Timeline
- **v3.7.0**: Old APIs available with deprecation warnings
- **v3.8.0**: Old APIs still available but warnings increased
- **v3.9.0**: Old APIs removed
## Best Practices
### TUI Usage
1. **Keyboard Navigation**: Learn keyboard shortcuts for efficient navigation
2. **Session Management**: Regularly export sessions for backup
3. **Real-time Monitoring**: Use dashboard for monitoring critical automations
4. **Error Handling**: Check error logs regularly for issues
### A2A Communication
1. **Agent Registration**: Keep agent registry up-to-date
2. **Error Handling**: Implement proper error handling and retry logic
3. **Message Routing**: Use message routing for complex workflows
4. **Security**: Enable authentication for production deployments
### Automation Execution
1. **Subplan Organization**: Organize automations into logical subplans
2. **Checkpoint Strategy**: Create checkpoints at critical points
3. **Error Recovery**: Implement error recovery and rollback strategies
4. **Progress Tracking**: Monitor progress of long-running automations
### Skill Management
1. **Skill Versioning**: Use versioning for skill updates
2. **Skill Composition**: Compose skills for complex workflows
3. **Skill Testing**: Test skills before deployment
4. **Documentation**: Keep skill documentation up-to-date
## Troubleshooting
### TUI Issues
**Problem**: TUI is not responsive
**Solution**:
- Check terminal size (minimum 80x24)
- Restart TUI
- Check system resources
### A2A Communication Issues
**Problem**: Agent communication fails
**Solution**:
- Check agent registration
- Verify network connectivity
- Check authentication credentials
- Review error logs
### Subplan Execution Issues
**Problem**: Subplan execution fails
**Solution**:
- Check subplan dependencies
- Review error logs
- Use checkpoint recovery
- Check resource availability
## Additional Resources
- [TUI User Guide](../reference/tui-guide.md)
- [A2A Protocol Specification](../reference/a2a-protocol.md)
- [Skill Development Guide](../reference/skill-development.md)
- [API Reference](../api/v1.md)
## Support and Feedback
For issues, questions, or feedback regarding v3.7.0 features:
- **GitHub Issues**: https://git.cleverthis.com/cleveragents/cleveragents-core/issues
- **Documentation**: https://docs.cleverthis.com
- **Community Chat**: https://chat.cleverthis.com
---
**Last Updated**: April 2024
**Version**: 3.7.0
+658
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@@ -0,0 +1,658 @@
# CleverAgents v3.7.0 Terminal User Interface (TUI) Guide
## Overview
The CleverAgents v3.7.0 Terminal User Interface (TUI) provides a comprehensive, interactive environment for managing automations, sessions, skills, and resources directly from your terminal. This guide covers all aspects of the TUI, from basic navigation to advanced features.
## Table of Contents
1. [Getting Started](#getting-started)
2. [Main Interface](#main-interface)
3. [Navigation](#navigation)
4. [Automations Management](#automations-management)
5. [Sessions Management](#sessions-management)
6. [Skills Management](#skills-management)
7. [Resources Management](#resources-management)
8. [Settings and Configuration](#settings-and-configuration)
9. [Keyboard Shortcuts](#keyboard-shortcuts)
10. [Advanced Features](#advanced-features)
11. [Troubleshooting](#troubleshooting)
## Getting Started
### Installation
The TUI is included with CleverAgents v3.7.0. No additional installation is required.
### Launching the TUI
```bash
# Start the TUI
cleveragents tui
# Start with specific configuration
cleveragents tui --config=/path/to/config.yaml
# Start in debug mode
cleveragents tui --debug
# Start with specific theme
cleveragents tui --theme=dark
```
### System Requirements
- Terminal size: Minimum 80x24 characters
- Color support: 256-color or true color recommended
- Python 3.8+
- CleverAgents v3.7.0+
## Main Interface
### Layout Overview
The TUI is organized into several main sections:
```
┌─────────────────────────────────────────────────────────────────────┐
│ CleverAgents v3.7.0 - Main Dashboard │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ [1] Automations [2] Sessions [3] Skills [4] Resources [5] Set │
│ │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Active Automations (3) │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ ID Name Status Progress Duration │ │
│ │ auto-001 data-pipeline Running ████░░░░░░ 2m 15s │ │
│ │ auto-002 report-gen Completed ██████████ 5m 42s │ │
│ │ auto-003 backup-task Failed ██████░░░░ 3m 28s │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ System Status │
│ CPU: 45% | Memory: 2.1GB / 8GB | Connections: 8 | Uptime: 2d 3h │
│ │
├─────────────────────────────────────────────────────────────────────┤
│ Press 'h' for help | 'q' to quit | Arrow keys to navigate │
└─────────────────────────────────────────────────────────────────────┘
```
### Color Scheme
- **Green**: Success, running, healthy
- **Yellow**: Warning, pending, in progress
- **Red**: Error, failed, critical
- **Blue**: Information, selected
- **Cyan**: Highlight, focus
- **White**: Normal text
## Navigation
### Menu Navigation
The TUI uses a tabbed interface for main sections:
```
[1] Automations [2] Sessions [3] Skills [4] Resources [5] Settings
```
### Navigating Between Tabs
```
- Press number key (1-5) to jump to tab
- Press Tab/Shift+Tab to move to next/previous tab
- Press Left/Right arrow keys to move between tabs
```
### List Navigation
Within each section, navigate lists using:
```
- Up/Down arrow keys: Move between items
- Page Up/Page Down: Scroll by page
- Home/End: Jump to first/last item
- Enter: Select/open item
- Space: Toggle selection (multi-select mode)
```
### Search and Filter
```
- Press '/' to open search
- Type search term
- Press Enter to search
- Press Escape to close search
- Press 'f' to open filter dialog
```
## Automations Management
### Viewing Automations
The Automations tab displays all automations with their current status.
```
Automations (12 total)
┌─────────────────────────────────────────────────────────────────────┐
│ ID Name Status Progress Duration Error │
├─────────────────────────────────────────────────────────────────────┤
│ auto-001 data-pipeline Running ████░░░░░░ 2m 15s - │
│ auto-002 report-gen Completed ██████████ 5m 42s - │
│ auto-003 backup-task Failed ██████░░░░ 3m 28s Timeout│
│ auto-004 cleanup Pending ░░░░░░░░░░ - - │
│ auto-005 sync-data Running ██████░░░░ 1m 30s - │
└─────────────────────────────────────────────────────────────────────┘
```
### Creating New Automation
```
Press 'c' to create new automation
┌─ Create Automation ──────────────────────────────────────────────┐
│ │
│ Name: [_____________________________] │
│ Description: [_____________________________] │
│ Skills: [Select skills...] │
│ Timeout (seconds): [300] │
│ Retry Count: [3] │
│ Retry Backoff: [2.0] │
│ │
│ [Create] [Cancel] │
└──────────────────────────────────────────────────────────────────┘
```
### Viewing Automation Details
Press Enter on an automation to view details:
```
┌─ Automation Details: data-pipeline ──────────────────────────────┐
│ │
│ ID: auto-001 │
│ Status: Running │
│ Created: 2024-01-15 10:30:45 │
│ Started: 2024-01-15 10:35:00 │
│ Duration: 2m 15s │
│ Progress: 45% │
│ │
│ Skills: │
│ - data-extract (completed) │
│ - data-validate (running) │
│ - data-transform (pending) │
│ - data-load (pending) │
│ │
│ Metrics: │
│ CPU Usage: 45% │
│ Memory: 512MB / 2GB │
│ I/O Operations: 1,234 │
│ │
│ [View Logs] [Cancel] [Pause] [Resume] [Stop] │
└──────────────────────────────────────────────────────────────────┘
```
### Executing Automations
```
Press 'e' to execute automation
┌─ Execute Automation ─────────────────────────────────────────────┐
│ │
│ Automation: data-pipeline │
│ │
│ Context Variables: │
│ project_id: [_____________________________] │
│ user_id: [_____________________________] │
│ environment: [production ▼] │
│ │
│ Options: │
│ ☑ Enable logging │
│ ☑ Enable metrics collection │
│ ☐ Dry run │
│ │
│ [Execute] [Cancel] │
└──────────────────────────────────────────────────────────────────┘
```
### Viewing Logs
Press 'l' to view automation logs:
```
┌─ Logs: data-pipeline ────────────────────────────────────────────┐
│ │
│ [2024-01-15 10:35:00] INFO: Starting automation │
│ [2024-01-15 10:35:01] INFO: Loading context │
│ [2024-01-15 10:35:02] INFO: Executing skill: data-extract │
│ [2024-01-15 10:35:15] INFO: Skill completed: data-extract │
│ [2024-01-15 10:35:16] INFO: Executing skill: data-validate │
│ [2024-01-15 10:35:45] WARN: Validation found 5 issues │
│ [2024-01-15 10:35:46] INFO: Executing skill: data-transform │
│ [2024-01-15 10:36:00] INFO: Running... │
│ │
│ [Scroll: ↑↓] [Filter: f] [Export: e] [Close: q] │
└──────────────────────────────────────────────────────────────────┘
```
## Sessions Management
### Viewing Sessions
The Sessions tab displays all recorded sessions.
```
Sessions (8 total)
┌─────────────────────────────────────────────────────────────────────┐
│ ID Automation Status Created Dur │
├─────────────────────────────────────────────────────────────────────┤
│ sess-001 data-pipeline Completed 2024-01-15 10:30:45 5m 42s│
│ sess-002 report-gen Completed 2024-01-15 09:15:20 3m 15s│
│ sess-003 backup-task Failed 2024-01-15 08:00:00 2m 30s│
│ sess-004 sync-data Completed 2024-01-14 23:45:30 1m 10s│
└─────────────────────────────────────────────────────────────────────┘
```
### Session Details
Press Enter on a session to view details:
```
┌─ Session Details: sess-001 ──────────────────────────────────────┐
│ │
│ ID: sess-001 │
│ Automation: data-pipeline │
│ Status: Completed │
│ Created: 2024-01-15 10:30:45 │
│ Started: 2024-01-15 10:35:00 │
│ Completed: 2024-01-15 10:40:42 │
│ Duration: 5m 42s │
│ │
│ Execution Timeline: │
│ data-extract: ████████████░░░░░░░░ 2m 15s │
│ data-validate: ████████░░░░░░░░░░░░ 1m 30s │
│ data-transform: ██████████░░░░░░░░░░ 1m 45s │
│ data-load: ████████░░░░░░░░░░░░ 1m 12s │
│ │
│ [View Timeline] [Export] [Delete] [Close] │
└──────────────────────────────────────────────────────────────────┘
```
### Exporting Sessions
Press 'e' to export session:
```
┌─ Export Session ─────────────────────────────────────────────────┐
│ │
│ Session: sess-001 │
│ Format: [JSON ▼] │
│ Include: │
│ ☑ Logs │
│ ☑ Metrics │
│ ☑ Timeline │
│ ☑ Context │
│ │
│ Output File: [session-001-export.json] │
│ │
│ [Export] [Cancel] │
└──────────────────────────────────────────────────────────────────┘
```
## Skills Management
### Viewing Skills
The Skills tab displays all available skills.
```
Skills (24 total)
┌─────────────────────────────────────────────────────────────────────┐
│ Name Category Status Version Last Updated │
├─────────────────────────────────────────────────────────────────────┤
│ data-extract Data Active 2.1.0 2024-01-10 │
│ data-validate Data Active 1.5.0 2024-01-08 │
│ data-transform Data Active 3.0.0 2024-01-15 │
│ data-load Data Active 2.0.0 2024-01-12 │
│ report-generate Reporting Active 1.2.0 2024-01-05 │
└─────────────────────────────────────────────────────────────────────┘
```
### Skill Details
Press Enter on a skill to view details:
```
┌─ Skill Details: data-extract ────────────────────────────────────┐
│ │
│ Name: data-extract │
│ Version: 2.1.0 │
│ Category: Data │
│ Status: Active │
│ Description: Extract data from various sources │
│ │
│ Parameters: │
│ source (required): Data source URL │
│ format (optional): Output format (json, csv, xml) │
│ timeout (optional): Timeout in seconds (default: 300) │
│ │
│ Returns: │
│ data: Extracted data │
│ count: Number of records │
│ duration: Execution time │
│ │
│ [View Documentation] [Test] [Edit] [Close] │
└──────────────────────────────────────────────────────────────────┘
```
### Testing Skills
Press 't' to test a skill:
```
┌─ Test Skill: data-extract ───────────────────────────────────────┐
│ │
│ Parameters: │
│ source: [_____________________________] │
│ format: [json ▼] │
│ timeout: [300] │
│ │
│ [Run Test] [Cancel] │
│ │
│ Test Results: │
│ Status: Success │
│ Duration: 1.23s │
│ Output: │
│ data: [{"id": 1, "name": "Item 1"}, ...] │
│ count: 1000 │
│ duration: 1.23 │
│ │
└──────────────────────────────────────────────────────────────────┘
```
## Resources Management
### Viewing Resources
The Resources tab displays all available resources.
```
Resources (15 total)
┌─────────────────────────────────────────────────────────────────────┐
│ Name Type Status Created Exp │
├─────────────────────────────────────────────────────────────────────┤
│ prod-database Database Active 2024-01-01 - │
│ api-key-prod Credential Active 2024-01-01 - │
│ s3-bucket Storage Active 2024-01-05 - │
│ smtp-config Service Active 2024-01-10 - │
└─────────────────────────────────────────────────────────────────────┘
```
### Resource Details
Press Enter on a resource to view details:
```
┌─ Resource Details: prod-database ────────────────────────────────┐
│ │
│ Name: prod-database │
│ Type: Database │
│ Status: Active │
│ Created: 2024-01-01 00:00:00 │
│ Last Used: 2024-01-15 10:40:42 │
│ │
│ Configuration: │
│ Host: db.example.com │
│ Port: 5432 │
│ Database: production │
│ Connection Pool: 10 │
│ │
│ Usage Statistics: │
│ Total Connections: 1,234 │
│ Active Connections: 5 │
│ Failed Connections: 2 │
│ │
│ [View Logs] [Test Connection] [Edit] [Close] │
└──────────────────────────────────────────────────────────────────┘
```
## Settings and Configuration
### Accessing Settings
Press '5' to go to Settings tab or press 's' from any tab.
```
Settings
┌─────────────────────────────────────────────────────────────────────┐
│ [1] General [2] Appearance [3] Logging [4] Advanced │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ General Settings │
│ │
│ API URL: [http://localhost:8000] │
│ API Key: [sk-****...****] │
│ Default Timeout: [300] seconds │
│ Auto-refresh: [☑] Every [5] seconds │
│ Confirm on Delete: [☑] │
│ │
│ [Save] [Reset] [Cancel] │
└─────────────────────────────────────────────────────────────────────┘
```
### Appearance Settings
```
Appearance Settings
Theme: [Dark ▼]
Color Scheme: [Default ▼]
Font Size: [Medium ▼]
Show Status Bar: [☑]
Show Line Numbers: [☑]
Syntax Highlighting: [☑]
[Save] [Reset] [Cancel]
```
### Logging Settings
```
Logging Settings
Log Level: [INFO ▼]
Log Format: [JSON ▼]
Log Output: [Console ▼]
Max Log Size: [100] MB
Log Retention: [30] days
Enable Audit Logging: [☑]
[Save] [Reset] [Cancel]
```
## Keyboard Shortcuts
### Global Shortcuts
| Key | Action |
|-----|--------|
| `1-5` | Jump to tab (Automations, Sessions, Skills, Resources, Settings) |
| `Tab` / `Shift+Tab` | Move to next/previous tab |
| `h` | Show help |
| `q` | Quit TUI |
| `?` | Show keyboard shortcuts |
| `Ctrl+C` | Force quit |
### Navigation Shortcuts
| Key | Action |
|-----|--------|
| `↑` / `↓` | Move up/down in list |
| `←` / `→` | Move left/right between tabs |
| `Page Up` / `Page Down` | Scroll by page |
| `Home` / `End` | Jump to first/last item |
| `Enter` | Select/open item |
| `Space` | Toggle selection (multi-select) |
### List Shortcuts
| Key | Action |
|-----|--------|
| `/` | Open search |
| `f` | Open filter |
| `c` | Create new item |
| `e` | Edit selected item |
| `d` | Delete selected item |
| `r` | Refresh list |
| `Ctrl+A` | Select all |
| `Ctrl+D` | Deselect all |
### Automation Shortcuts
| Key | Action |
|-----|--------|
| `e` | Execute automation |
| `l` | View logs |
| `m` | View metrics |
| `p` | Pause automation |
| `Shift+P` | Resume automation |
| `s` | Stop automation |
| `c` | Cancel automation |
### Session Shortcuts
| Key | Action |
|-----|--------|
| `e` | Export session |
| `i` | Import session |
| `v` | View timeline |
| `l` | View logs |
| `m` | View metrics |
## Advanced Features
### Real-time Monitoring
The TUI provides real-time monitoring of automations with live updates.
```
Real-time Monitoring Dashboard
┌─ Execution Timeline ─────────────────────────────────────────────┐
│ │
│ data-extract: ████████████░░░░░░░░ 2m 15s (45%) │
│ data-validate: ████████░░░░░░░░░░░░ 1m 30s (30%) │
│ data-transform: ██████████░░░░░░░░░░ 1m 45s (35%) │
│ data-load: ░░░░░░░░░░░░░░░░░░░░ 0s (0%) │
│ │
│ Total Progress: ███████░░░░░░░░░░░░░░ 35% │
│ Estimated Time Remaining: 6m 45s │
│ │
└──────────────────────────────────────────────────────────────────┘
┌─ Resource Usage ─────────────────────────────────────────────────┐
│ │
│ CPU: ████████░░░░░░░░░░░░ 40% │
│ Memory: ██████░░░░░░░░░░░░░░ 30% │
│ Disk: ███░░░░░░░░░░░░░░░░░ 15% │
│ Network: ██████████░░░░░░░░░░ 50% │
│ │
└──────────────────────────────────────────────────────────────────┘
```
### Bulk Operations
Select multiple items and perform bulk operations:
```
# Select multiple automations
Press Space on each automation to select
# Perform bulk operation
Press 'e' to execute all selected
Press 'd' to delete all selected
Press 's' to stop all selected
```
### Custom Dashboards
Create custom dashboards for monitoring:
```
Press 'd' to create custom dashboard
┌─ Create Dashboard ───────────────────────────────────────────────┐
│ │
│ Name: [_____________________________] │
│ │
│ Widgets: │
│ ☑ Execution Timeline │
│ ☑ Resource Usage │
│ ☑ Error Log │
│ ☑ Performance Metrics │
│ ☐ Custom Metrics │
│ │
│ Refresh Interval: [5] seconds │
│ │
│ [Create] [Cancel] │
└──────────────────────────────────────────────────────────────────┘
```
## Troubleshooting
### TUI Not Starting
**Problem**: TUI fails to start
**Solution**:
```bash
# Check terminal size
echo $LINES x $COLUMNS
# Try with debug mode
cleveragents tui --debug
# Check logs
tail -f ~/.cleveragents/logs/tui.log
```
### Slow Performance
**Problem**: TUI is slow or unresponsive
**Solution**:
- Reduce auto-refresh interval in settings
- Close unnecessary tabs
- Check system resources
- Restart TUI
### Display Issues
**Problem**: Colors or text are not displaying correctly
**Solution**:
- Check terminal color support: `echo $TERM`
- Try different theme: `cleveragents tui --theme=light`
- Update terminal emulator
- Check terminal encoding: `echo $LANG`
### Connection Issues
**Problem**: Cannot connect to API
**Solution**:
- Check API URL in settings
- Verify API key is valid
- Check network connectivity
- Review API logs
## Additional Resources
- [CleverAgents API Reference](../api/v1.md)
- [Automation Guide](../reference/automations.md)
- [Skills Development Guide](../reference/skill-development.md)
- [Troubleshooting Guide](../reference/troubleshooting.md)
---
**Last Updated**: April 2024
**Version**: 3.7.0
+94 -91
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@@ -1,13 +1,8 @@
# yaml-language-server: $schema=https://json.schemastore.org/mkdocs-1.6.json
# yaml-language-server: customTags:
# - !ENV scalar
# - !ENV sequence
site_name: CleverAgents Documentation
site_description: Documentation for CleverAgents.
site_author: CleverThis, Inc.
site_url: https://docs.cleverthis.com/cleveragents
site_dir: build/site
nav:
- Specification: specification.md
- Architecture: architecture.md
@@ -52,6 +47,19 @@ nav:
- Automation Tracking: development/automation-tracking.md
- Custom Sandbox Strategy: development/custom_sandbox_strategy.md
- Documentation Writer: development/docs-writer.md
- ACP to A2A Migration: development/acp-to-a2a-migration.md
- Milestones:
- Overview: milestones/index.md
- "v3.0.0 \u2014 Minimal Local Source-Code Workflow (M1)":
- milestones/v3.0.0/index.md
- milestones/v3.0.0/cli-reference.md
- milestones/v3.0.0/deep-dive.md
- "v3.1.0 \u2014 Actor Compiler + Full LLM Integration (M2)":
- milestones/v3.1.0/index.md
- milestones/v3.1.0/actor-yaml.md
- milestones/v3.1.0/integration.md
- milestones/v3.1.0/skills.md
- milestones/v3.1.0/quickstart.md
- Implementation Timeline: timeline.md
- Advanced Concepts (v3.6.0):
- Overview & Context Strategies: advanced-concepts/index.md
@@ -118,105 +126,100 @@ nav:
- ADR-046 TUI Reference and Command System: adr/ADR-046-tui-reference-and-command-system.md
- ADR-047 A2A Standard Adoption: adr/ADR-047-acp-standard-adoption.md
- ADR-048 Server Application Architecture: adr/ADR-048-server-application-architecture.md
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name: material
custom_dir: docs/overrides
features:
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- toc.integrate
- toc.follow
- navigation.tabs
- navigation.tabs.sticky
- toc.integrate
- toc.follow
palette:
- scheme: default
primary: blue
accent: cyan
toggle:
icon: material/brightness-7
name: Switch to dark mode
- scheme: slate
primary: blue
accent: cyan
toggle:
icon: material/brightness-4
name: Switch to light mode
- scheme: default
primary: blue
accent: cyan
toggle:
icon: material/brightness-7
name: Switch to dark mode
- scheme: slate
primary: blue
accent: cyan
toggle:
icon: material/brightness-4
name: Switch to light mode
hooks:
- hooks/adr_hooks.py
- hooks/adr_hooks.py
extra_css:
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- stylesheets/extra.css
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- javascripts/toc-collapse.js
- javascripts/diagram-lightbox.js
- javascripts/adr-page.js
- javascripts/toc-collapse.js
- javascripts/diagram-lightbox.js
extra:
adr_tiers:
1:
title: "Foundational"
description: "Structural and technological foundation upon which all other decisions rest."
title: Foundational
description: Structural and technological foundation upon which all other decisions
rest.
2:
title: "Core Domain"
description: "Domain model entities, lifecycles, and relationships that constitute core logic."
title: Core Domain
description: "Domain model \u2014 entities, lifecycles, and relationships that\
\ constitute core logic."
3:
title: "Infrastructure and Behavior"
description: "Cross-cutting behavioral systems and infrastructure concerns."
title: Infrastructure and Behavior
description: Cross-cutting behavioral systems and infrastructure concerns.
4:
title: "Integration and Operations"
description: "External integrations, operational interfaces, and deployment concerns."
title: Integration and Operations
description: External integrations, operational interfaces, and deployment concerns.
plugins:
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- gen-files:
scripts:
- docs/gen_ref_pages.py
- literate-nav:
nav_file: SUMMARY.md
- mkdocstrings:
handlers:
python:
paths: [src]
options:
docstring_style: google
docstring_section_style: table
show_root_heading: true
show_source: true
merge_init_into_class: true
separate_signature: true
show_signature_annotations: true
signature_crossrefs: true
summary: true
extensions:
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schema: true
- kroki:
server_url: https://kroki.qoto.org
#server_url: https://kroki.io
enable_mermaid: true
fence_prefix: kroki-
http_method: POST
- search
- gen-files:
scripts:
- docs/gen_ref_pages.py
- literate-nav:
nav_file: SUMMARY.md
- mkdocstrings:
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paths:
- src
options:
docstring_style: google
docstring_section_style: table
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show_source: true
merge_init_into_class: true
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show_signature_annotations: true
signature_crossrefs: true
summary: true
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schema: true
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enable_mermaid: true
fence_prefix: kroki-
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markdown_extensions:
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- pymdownx.keys
- pymdownx.mark
- pymdownx.tilde
- pymdownx.tasklist:
custom_checkbox: true
- attr_list
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- footnotes
- md_in_html
- tables
- codehilite
- toc:
permalink: true
toc_depth: 1-6
- admonition
- pymdownx.details
- pymdownx.superfences
- pymdownx.tabbed:
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- pymdownx.critic
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- md_in_html
- tables
- codehilite
- toc:
permalink: true
toc_depth: 1-6
+122 -31
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@@ -1,48 +1,139 @@
*** Settings ***
Documentation E2E acceptance test for M2 (v3.1.0): Actor Compiler + Full LLM Integration.
Documentation E2E acceptance test for M2 (v3.1.0): Actor Compiler + Full LLM Integration.
...
... Exercises actor YAML compilation into functional graphs, skill registry,
... tool lifecycle, and plan execution with a custom actor using real LLM keys.
... Zero mocking — all CLI invocations hit the real CleverAgents binary with
... real provider keys.
Resource common_e2e.resource
Suite Setup E2E Suite Setup
... Exercises actor YAML compilation into functional graphs, skill registry,
... tool lifecycle, and plan execution with a custom actor using real LLM keys.
... Zero mocking — all CLI invocations hit the real CleverAgents binary with
... real provider keys.
Resource common_e2e.resource
Suite Setup E2E Suite Setup
Suite Teardown E2E Suite Teardown
*** Variables ***
${ACTOR_NAME} local/m2-e2e-actor
${ACTION_NAME} local/m2-e2e-action
${RESOURCE_NAME} local/m2-e2e-repo
${PROJECT_NAME} local/m2-e2e-project
${ACTOR_NAME} local/m2-e2e-actor
${ACTION_NAME} local/m2-e2e-action
${RESOURCE_NAME} local/m2-e2e-repo
${PROJECT_NAME} local/m2-e2e-project
*** Test Cases ***
M2 Full Actor Compiler And LLM Integration
[Documentation] End-to-end acceptance test for the M2 milestone.
...
... Exercises actor YAML compilation, registration via CLI,
... resource and project setup, action creation referencing a
... custom actor, and the full plan lifecycle (use, execute
... strategize, execute, diff, apply) with real LLM API keys.
[Tags] E2E tdd_issue tdd_issue_4189 tdd_expected_fail
Skip If No LLM Keys
# ---- Step 1: Create temp git repo with sample project files ----
${repo_dir}= Create Temp Git Repo m2-e2e-repo
Create Directory ${repo_dir}${/}src
Create File ${repo_dir}${/}src${/}main.py print("hello world")\n
${r_add}= Run Process git add . cwd=${repo_dir} timeout=60s on_timeout=kill
Should Be Equal As Integers ${r_add.rc} 0 msg=git add failed (rc=${r_add.rc}). Check DEBUG logs above.
${r_commit}= Run Process git commit -m Add source files cwd=${repo_dir} timeout=60s on_timeout=kill
Should Be Equal As Integers ${r_commit.rc} 0 msg=git commit failed (rc=${r_commit.rc}). Check DEBUG logs above.
# Detect the default branch name created by git init
${branch_result}= Run Process git rev-parse --abbrev-ref HEAD cwd=${repo_dir} timeout=60s on_timeout=kill
${branch}= Strip String ${branch_result.stdout}
Log Detected branch: ${branch}
[Documentation] End-to-end acceptance test for the M2 milestone.
...
... Exercises actor YAML compilation, registration via CLI,
... resource and project setup, action creation referencing a
... custom actor, and the full plan lifecycle (use, execute
... strategize, execute, diff, apply) with real LLM API keys.
[Tags] E2E
Skip If No LLM Keys
# ---- Step 1: Create temp git repo with sample project files ----
${repo_dir}= Create Temp Git Repo m2-e2e-repo
Create Directory ${repo_dir}${/}src
Create File ${repo_dir}${/}src${/}main.py print("hello world")\n
${r_add}= Run Process git add . cwd=${repo_dir} timeout=60s on_timeout=kill
Should Be Equal As Integers ${r_add.rc} 0 msg=git add failed (rc=${r_add.rc}). Check DEBUG logs above.
${r_commit}= Run Process git commit -m Add source files cwd=${repo_dir} timeout=60s on_timeout=kill
Should Be Equal As Integers ${r_commit.rc} 0 msg=git commit failed (rc=${r_commit.rc}). Check DEBUG logs above.
# Detect the default branch name created by git init
${branch_result}= Run Process git rev-parse --abbrev-ref HEAD cwd=${repo_dir} timeout=60s on_timeout=kill
${branch}= Strip String ${branch_result.stdout}
Log Detected branch: ${branch}
# ---- Resolve LLM actor (OpenAI if available, Anthropic fallback) ----
${llm_actor}= Resolve LLM Actor
${llm_parts}= Evaluate $llm_actor.split('/', 1)
${llm_provider}= Set Variable ${llm_parts}[0]
${llm_model}= Set Variable ${llm_parts}[1]
# ---- Step 2: Create and register custom actor YAML ----
${actor_yaml}= Catenate SEPARATOR=\n
... name: ${ACTOR_NAME}
... type: llm
... description: M2 E2E acceptance actor for compiler and LLM integration
... version: "1.0"
... model: ${llm_model}
${actor_yaml_path}= Set Variable ${SUITE_HOME}${/}m2_actor.yaml
Create File ${actor_yaml_path} ${actor_yaml}
${actor_config}= Catenate SEPARATOR=\n
... {
... "name": "${ACTOR_NAME}",
... "provider": "${llm_provider}",
... "model": "${llm_model}",
... "options": {"temperature": 0.2}
... }
${config_path}= Set Variable ${SUITE_HOME}${/}actor_config.json
Create File ${config_path} ${actor_config}
${r_actor}= Run CleverAgents Command
... actor add --config ${config_path} --format plain
Should Be Equal As Integers ${r_actor.rc} 0 msg=Actor add failed (rc=${r_actor.rc}). Check DEBUG logs above.
Should Not Contain ${r_actor.stdout}${r_actor.stderr} Traceback
Log Actor registration: ${r_actor.stdout}
# ---- Step 3: Register resource and create project ----
${r_resource}= Run CleverAgents Command
... resource add git-checkout ${RESOURCE_NAME}
... --path ${repo_dir} --branch ${branch} --format plain
Output Should Contain ${r_resource} ${RESOURCE_NAME}
${r_project}= Run CleverAgents Command
... project create ${PROJECT_NAME}
... --description M2 E2E acceptance project
... --resource ${RESOURCE_NAME} --format plain
Output Should Contain ${r_project} ${PROJECT_NAME}
# ---- Step 4: Create action referencing the custom actor ----
${action_yaml}= Catenate SEPARATOR=\n
... name: ${ACTION_NAME}
... description: M2 acceptance test action for actor compiler and LLM integration
... definition_of_done: Generate or modify at least one source file
... strategy_actor: ${llm_actor}
... execution_actor: ${llm_actor}
${action_yaml_path}= Set Variable ${SUITE_HOME}${/}action.yaml
Create File ${action_yaml_path} ${action_yaml}
${r_action}= Run CleverAgents Command
... action create --config ${action_yaml_path} --format plain
Output Should Contain ${r_action} ${ACTION_NAME}
# ---- Step 5: Plan use ----
${r_use}= Run CleverAgents Command
... plan use ${ACTION_NAME} ${PROJECT_NAME} --format plain
Should Not Be Empty ${r_use.stdout}
${plan_ids}= Get Regexp Matches ${r_use.stdout} [0-9A-Z]{26}
Should Not Be Empty ${plan_ids} msg=Expected a ULID plan ID in plan use output
${plan_id}= Set Variable ${plan_ids}[0]
Log Extracted plan_id: ${plan_id}
# ---- Step 6: Plan execute — strategize phase ----
${r_strategize}= Run CleverAgents Command
... plan execute ${plan_id} --format plain
... timeout=180s
Should Not Contain ${r_strategize.stdout}${r_strategize.stderr} INTERNAL
Should Not Contain ${r_strategize.stdout}${r_strategize.stderr} Traceback
Log Strategize phase output: ${r_strategize.stdout}
# ---- Step 7: Plan execute — execute phase ----
${r_execute}= Run CleverAgents Command
... plan execute ${plan_id} --format plain
... timeout=180s
Should Not Contain ${r_execute.stdout}${r_execute.stderr} INTERNAL
Should Not Contain ${r_execute.stdout}${r_execute.stderr} Traceback
Log Execute phase output: ${r_execute.stdout}
# ---- Step 8: Plan diff ----
${r_diff}= Run CleverAgents Command
... plan diff ${plan_id} --format plain
Should Not Contain ${r_diff.stdout}${r_diff.stderr} INTERNAL
Should Not Contain ${r_diff.stdout}${r_diff.stderr} Traceback
Log Diff output: ${r_diff.stdout}
# ---- Step 9: Plan apply ----
${r_apply}= Run CleverAgents Command
... plan apply --yes ${plan_id} --format plain
Should Not Contain ${r_apply.stdout}${r_apply.stderr} INTERNAL
Should Not Contain ${r_apply.stdout}${r_apply.stderr} Traceback
Log Apply output: ${r_apply.stdout}
# ---- Step 10: Verify actor compilation and plan integrity ----
${r_status}= Run CleverAgents Command
... plan status ${plan_id} --format plain
Should Not Be Empty ${r_status.stdout}
Output Should Contain ${r_status} ${plan_id}
Log Final plan status: ${r_status.stdout}