drew 23a014e402 feat(controller): Phase 1c-3 — production agent_runner (real MCP subprocess)
The production seam that connects the controller's runner to actual
LLM execution. Per-attempt MCP subprocess spawn + OpenCode session
drive + canonical JSON read + strict-parse → output dict.

tools/controller/worker/agent_runner.py:

- production_agent_runner(): the function tested production wires
  into run_one_attempt's agent_runner param. Per-attempt lifecycle:
  1. Map role → matching MCP module + V1 output contract.
  2. mkstemp a canonical-output path; spawn `python -m {mcp_module}`
     subprocess with CONTROLLER_CANONICAL_OUTPUT_PATH set in env.
  3. Call injected run_opencode_session (tests use JSON-RPC stdin
     driver; production wires _opencode_worker.run_session_blocking).
  4. Wait for the MCP's finalize_and_emit to write the canonical
     JSON to the tempfile.
  5. Strict-parse against the role's V1 output model.
  6. SIGTERM/grace/SIGKILL the MCP + unlink the tempfile in finally.

- ROLE_TO_MCP_MODULE / ROLE_TO_OUTPUT_MODEL: explicit per-role
  dispatch tables. Covers all 5 worker roles.

- Error classification per the WorkerError outcome enum:
  - unknown role → ValueError (master bug; not a worker outcome)
  - session raises generic Exception → WorkerError(worker-internal-error)
  - session raises WorkerLostLock → propagated unchanged
  - lost_lock_check returns True after session → WorkerLostLock
  - MCP didn't emit canonical output within timeout → WorkerError(
    worker-internal-error)
  - MCP emitted JSON that fails strict-parse → WorkerError(
    contract-violation; per v9 status='failed' policy maps to
    workflow STUCK)

tools/controller/mcp/_builder_base.py:

- finalize_and_emit() now writes to CONTROLLER_CANONICAL_OUTPUT_PATH
  if set (production subprocess path) or stdout if not (direct-call
  test path; capsys captures). Decouples canonical output from the
  FastMCP JSON-RPC stdout transport — they would otherwise collide
  in the subprocess (both writing to the same stream).
- Atomic file write: open + write + fsync + close.
- Existing test_mcp_builders.py (capsys-based) still passes with the
  fallback path; new test_worker_agent_runner.py exercises the
  file-based subprocess path with real MCPs.

10 new tests in test_worker_agent_runner.py:
- End-to-end with real MCP subprocesses (implementer-resolved,
  reviewer-approve, estimator-tier-recommendation)
- Error paths: unknown role (ValueError), session raises (wrapped
  as WorkerError), session raises WorkerLostLock (propagated),
  lost_lock_check returns True post-session (raises WorkerLostLock),
  no finalize → finalize_timeout → WorkerError
- Role-map sanity: every role has both an MCP module and output model

Total: 376 controller tests; full auto_agents suite 2738 pass.

This commit completes the v1 boundary — the controller now has the
complete stack from V1 contracts through MCP response builders, DB
schema, worker dequeue/heartbeat/runner/workspace, master state
machine + tick + reaper + pickup guard + scheduler + discovery +
Forgejo writes + MERGING + HTTP adapter, AND a production
agent_runner that wires it all to real OpenCode + MCP subprocess
spawning. Phase 1d-3+ enhancements (real OpenCode session wiring
in run_opencode_session) and Phase 2+ migration steps remain.
2026-05-18 14:05:10 -04:00
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2024-01-25 23:14:00 -05:00
2026-04-21 19:44:48 +00:00

CleverAgents Core

CleverAgents is a Python-first automation platform. It provides a unified agents CLI, an interactive Textual TUI, embedded runtime, and service orchestration tools while embracing modern Python tooling.

Highlights

  • Unified CLI entry points: cleveragents and agents
  • Interactive TUI (agents tui) — full-screen Textual app with multi-session tabs, persona switching, slash commands, reference picker, and context-sensitive F1 help
  • Persona system — YAML-backed personas bind actors, argument presets, and scope references to named identities; persisted in ~/.config/cleveragents/personas/
  • Session management — create, list, export, and import conversation sessions; full JSON export/import for portability; Markdown transcript export (--format md) for human-readable sharing
  • First-run experienceActorSelectionOverlay guides new users to pick an actor on first TUI launch; creates a "default" persona automatically
  • Server modeagents server connect configures a remote CleverAgents server; Kubernetes Helm chart in k8s/ for production deployment
  • A2A integration — Agent-to-Agent protocol facade wires CLI and TUI to live application services (session, plan, registry, event)
  • Permissions screen — TUI overlay for reviewing tool permission requests with unified, side-by-side, and context diff views; session-scoped allow/reject decisions
  • Actor thought blocks — expandable reasoning trace widgets rendered inline in the conversation stream with muted styling
  • UKO runtime — Universal Knowledge Ontology query interface, inference engine, and graph persistence for ACMS context strategies
  • Database resource handler — full CRUD and checkpoint/rollback support for SQLite, PostgreSQL, MySQL, and DuckDB resources
  • Estimation lifecycleactor.default.estimation config key wires an estimation actor into the Strategize-to-Estimate lifecycle hook
  • Shell danger detection — TUI shell mode (! prefix) classifies commands by danger level (LOW → CRITICAL) and surfaces a warning overlay before executing destructive, privilege-escalating, or exfiltration-risk commands
  • Inline permission questionsPermissionQuestionWidget renders single-file permission requests directly in the conversation stream with single-key shortcuts
  • Invariant reconciliationInvariantReconciliationActor runs automatically at every plan phase transition; failures block the transition and emit INVARIANT_VIOLATED
  • UKO provenance tracking — every typed triple now carries sourceResource, validFrom, and isCurrent metadata; a revision chain enables temporal queries
  • JSON-RPC 2.0 A2A wire formatA2aRequest/A2aResponse fields renamed to standard JSON-RPC 2.0 names (method, id, result, error)
  • Fast Typer/Click-based interface with parity for help/version behavior
  • Behavior-driven coverage via Behave and Robot Framework
  • Nox automation for linting, typing, testing, docs, builds, and benchmarks
  • MkDocs-powered documentation with CleverAgents branding

Quick Start

# clone the CleverAgents core repository
git clone https://git.cleverthis.com/cleveragents/core.git
cd core

# install dependencies
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,tests,docs]"

# set up pre-commit hooks and verify tooling
bash scripts/setup-dev.sh

# verify the CLI
agents --help
agents --version

Launch the TUI

# install TUI extra
pip install -e ".[tui]"

# launch the interactive terminal UI
agents tui

Inside the TUI:

  • Type a message and press Enter to chat with the active actor
  • Press / to open the slash command overlay (67 commands across 14 groups)
  • Press @ to open the reference picker and insert file/resource references
  • Press ! to enter shell mode and run a subprocess command
  • Press F1 to toggle the context-sensitive help panel
  • Press Ctrl+T to cycle through argument presets for the active persona
  • Press Ctrl+Q to quit

Session management

agents session create --actor openai/gpt-4o
agents session list
agents session export --session-id <ID> --output session.json
agents session import --input session.json

Server mode

# connect to a remote CleverAgents server
agents server connect --url https://my-server.example.com --token <TOKEN>

# check connection status
agents server status

Developing

Pre-commit hooks run automatically on every git commit (formatting, linting, type checking, security scanning). To run checks manually:

# core validation
nox -s format            # ruff auto-formatting
nox -s lint              # ruff linting
nox -s typecheck         # pyright type checking
nox -s unit_tests        # behave unit tests
nox -s integration_tests # robot integration tests

# quality & security
nox -s security_scan     # bandit security scanning
nox -s dead_code         # vulture dead code detection
nox -s complexity        # radon complexity analysis
nox -s pre_commit        # run all pre-commit hooks
nox -s adr_compliance    # verify ADR compliance

For the full quality automation guide, see docs/development/quality-automation.md.

Documentation

nox -s docs
nox -s serve_docs

Tests

Behave feature scenarios live under features/ and Robot suites under robot/. Use the Nox sessions above to execute them in parity with the implementation plan.

Observability

LangSmith tracing is optional and off by default. Enable it by exporting CLEVERAGENTS_LANGSMITH_ENABLED=true along with a project name and API key (CLEVERAGENTS_LANGSMITH_PROJECT, CLEVERAGENTS_LANGSMITH_API_KEY). The settings module automatically mirrors these values to LANGCHAIN_TRACING_V2, LANGCHAIN_PROJECT, and LANGCHAIN_API_KEY, so LangChain/LangGraph agents emit traces without extra wiring. Additional knobs such as CLEVERAGENTS_LANGSMITH_ENDPOINT, CLEVERAGENTS_LANGSMITH_USER_ID, and CLEVERAGENTS_LANGSMITH_TAGS are documented in docs/observability.md.

LLM provider configuration

CleverAgents ships with a LangChain/LangGraph powered provider registry that discovers whichever API keys you export and automatically selects the best available provider. The CLI now uses actors: select an actor with --actor <name> (or set a default via agents actor set-default). Actors embed provider/model choices; built-in actors are seeded from CLEVERAGENTS_DEFAULT_PROVIDER / CLEVERAGENTS_DEFAULT_MODEL, then fall back to the built-in order (openai → anthropic → google → azure → openrouter → groq → together → cohere → gemini).

Required environment variables

Provider Primary variables
OpenAI OPENAI_API_KEY
Anthropic ANTHROPIC_API_KEY
Google GOOGLE_API_KEY or GOOGLE_GENAI_API_KEY
Azure OpenAI AZURE_OPENAI_API_KEY plus AZURE_OPENAI_ENDPOINT/AZURE_OPENAI_DEPLOYMENT
OpenRouter OPENROUTER_API_KEY (+ optional CLEVERAGENTS_OPENROUTER_ORGANIZATION for sanitized headers)
Gemini GEMINI_API_KEY or GOOGLE_GEMINI_API_KEY
Cohere COHERE_API_KEY
Groq GROQ_API_KEY
Together TOGETHER_API_KEY

Set CLEVERAGENTS_DEFAULT_PROVIDER to pin the global provider (for example export CLEVERAGENTS_DEFAULT_PROVIDER=openai) and CLEVERAGENTS_DEFAULT_MODEL to lock in a model ID. When unset, the registry picks the first configured provider and uses its published default model such as gpt-4o for OpenAI or claude-sonnet-4-20250514 for Anthropic.

Diagnostics and testing shortcuts

  • agents diagnostics prints whether the registry can see your credentials and which actor/provider is selected.
  • agents tell and agents build require --actor <name> unless a default actor is set; use agents actor set-default <name> to configure one.
  • Built-in actors (<provider>/<model>) are immutable, custom actors must be named local/<id>, and the default actor cannot be removed. Use --unsafe when adding/updating configs marked unsafe; runtime only warns when invoking unsafe actors.
  • CLEVERAGENTS_TESTING_USE_MOCK_AI=true forces the in-repo mock provider so Behave/Robot suites never hit external APIs.
  • The full capability matrix (streaming, tool calls, JSON mode, etc.) is documented in docs/reference/providers.md.
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