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build(agents): add test infrastructure improver and project owner agents (13 supervisors)
Adds two new agent types to the autonomous system, bringing the total
from 11 to 13 supervisors launched by the product-builder via prompt_async.

New agents:

1. ca-test-infra-improver (12th supervisor — pool with N workers):
   Dual-mode agent following the ca-bug-hunter pattern. In pool mode,
   dispatches N parallel workers via prompt_async to analyze 8 aspects
   of the testing infrastructure: CI execution time, coverage gaps, test
   architecture (BDD quality), flaky tests, CI pipeline optimization,
   test data quality, missing test levels (Behave/Robot/ASV per
   CONTRIBUTING.md), and dependency security. Workers file actionable
   Type/Testing or Type/Task issues. Hard constraint: never disables
   or weakens existing checks — only proposes additions and optimizations.
   Uses Gemini 2.5 Pro for large context. Follows all established patterns
   (clone isolation, bash sleep, prompt_async dispatch, session resume,
   bot signature).

2. ca-project-owner (13th supervisor — singleton, no pool):
   Acts as autonomous project owner. Continuously triages State/Unverified
   issues following CONTRIBUTING.md's 6-step triage process. Assigns
   MoSCoW labels (Must Have / Should Have / Could Have) based on the
   specification and milestone goals. Makes strategic priority decisions.
   Tags specific developers with questions in Forgejo comments (discovers
   expertise from git history and Forgejo assignments). Periodically
   re-evaluates MoSCoW labels as the project evolves. Follows up on
   unanswered questions after 48 hours. Single instance, not a pool —
   one project owner is sufficient. Uses Opus for nuanced strategic
   judgment. Launched via prompt_async like all other supervisors.

Modified files:

- product-builder.md: Updated from 11 to 13 supervisors in all locations
  (architecture table, Phase C.2 launch list with entries #12 and #13,
  validation count, checkpoint text, self-coordinate table). Added
  test-infra-pool to pool supervisors list and project-owner to singletons.

- ca-human-liaison.md: Clarified MoSCoW responsibility split — the liaison
  only adjusts MoSCoW labels when relaying explicit human feedback. The
  ca-project-owner handles autonomous MoSCoW assignment.
2026-04-02 15:19:53 -04:00
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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
  • 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
  • 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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