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fischer-agentkit/AGENTS.md

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Fischer AgentKit — Project Context

Rules

  • Python >= 3.11, type hints required, pydantic>=2.0 for all data models
  • Ruff for lint + format: ruff check src/ && ruff format src/ (target py311, line-length 100)
  • Tests: pytest (asyncio_mode=auto), markers: integration, redis, postgres
  • Never use any type — use proper Pydantic models or Unknown
  • API key comparison must use hmac.compare_digest (constant-time)
  • Expert names validated with _EXPERT_NAME_RE = re.compile(r"^[a-zA-Z0-9_-]{1,64}$")
  • HandoffTransport queues bounded (maxsize=1024), close uses sentinel pattern
  • Frontend: Vue 3 + TypeScript + Ant Design Vue, Pinia stores, no require() calls

Tech Stack

  • Backend: Python 3.11+, FastAPI, Uvicorn, Pydantic v2, SQLAlchemy 2 (async)
  • Frontend: Vue 3, TypeScript, Vite 5, Ant Design Vue 4, Pinia, Vue Router 4
  • Desktop: Tauri 2.x (Rust shell + Python sidecar)
  • Infra: Redis (bus/cache/state), PostgreSQL + pgvector (episodic memory)
  • CLI: Typer + Rich
  • Exact versions: see pyproject.toml (Python), package.json (Node)

Commands

# Backend
pip install -e ".[dev]"                # Install with dev deps
agentkit gui --port 8002               # Web GUI (frontend + API)
agentkit serve --port 8001             # API-only server
agentkit chat                          # CLI interactive chat
agentkit init                          # Generate agentkit.yaml
agentkit version / doctor / usage      # Utility commands
agentkit task submit/status/list/cancel # Task management
agentkit skill list/load/info          # Skill management
agentkit pair --name X                 # Generate API key for external system
pytest                                 # Run all tests
pytest -m "not integration"            # Unit tests only
ruff check src/ && ruff format src/    # Lint + format

# Frontend
cd src/agentkit/server/frontend
npm install                            # Install deps
npm run dev                            # Vite dev server (proxy /api -> :8000)
npm run build:frontend                 # Production build -> ../static
npm run typecheck                      # TypeScript check

# Desktop
cd src/agentkit/server/frontend
npm run tauri dev                      # Tauri dev mode
npm run tauri build                    # Tauri production build

# Docker
docker-compose up -d                   # AgentKit + Redis + PostgreSQL

Architecture

Request Flow

User Input -> CostAwareRouter (3-layer)
  Layer 0: RegexRules (~0ms, 0 tokens) -> DIRECT_CHAT
  Layer 1: HeuristicClassifier (~0ms) / LLM quick_classify (~500ms, ~100 tokens)
  Layer 1.5: SemanticRouter (vector similarity, optional)
  Layer 2: Capability matching / Vickrey Auction
  -> ExecutionMode: DIRECT_CHAT / REACT / SKILL_REACT / TEAM_COLLAB

Agent Hierarchy

BaseAgent (core/base.py) — abstract, execute() is final
  +-- ConfigDrivenAgent (core/config_driven.py) — YAML-driven, 3 task modes
  +-- ReActEngine (core/react.py) — Think->Act->Observe
  +-- ReflexionAgent (core/reflexion.py) — reflection-driven
  +-- ReWOOAgent (core/rewoo.py) — plan-without-observation
  +-- StandaloneAgent (core/standalone.py) — standalone runner

Expert Team Mode

ExpertConfig (extends AgentConfig) -> Expert (wraps ConfigDrivenAgent via AgentPool)
ExpertTeam: manages experts, shared workspace, collaboration plan
TeamOrchestrator: executes plan (serial/parallel/competitive + merge)
CollaborationPlan: phases with dependencies, parallel types, merge strategies
ExpertTeamRouter: @team prefix routing, name validation, MAX_EXPERTS=10
HandoffTransport: InProcess (asyncio.Queue) + Redis Pub/Sub

Lifecycle: FORMING -> PLANNING -> EXECUTING -> SYNTHESIZING -> COMPLETED On failure: fallback to single-agent mode (lead or first active expert).

Module Map

Layer Modules Purpose
API server/, cli/ FastAPI routes + Typer CLI
Service core/, chat/, skills/, experts/ Agent engine, routing, skills, expert teams
Data memory/, session/, bus/ Persistence, sessions, messaging
Utility llm/, tools/, evolution/, quality/, mcp/ LLM gateway, tools, self-evolution, quality, MCP

Key Subsystems

  • LLM Gateway (llm/): 6 providers (OpenAI/Anthropic/Gemini/Doubao/Wenxin/Yuanbao), fallback, semantic cache, usage tracking
  • Memory (memory/): 4-layer (SOUL/USER/MEMORY/DAILY), WorkingMemory (Redis), EpisodicMemory (PG+pgvector), SemanticMemory (HTTP RAG)
  • Evolution (evolution/): Reflector, PromptOptimizer (genetic), PitfallDetector, ABTester
  • Tools (tools/): 21 built-in + MCP extension, composition (SequentialChain/ParallelFanOut/DynamicSelector)
  • Pipeline (orchestrator/): PipelineEngine, SagaOrchestrator, DynamicPipeline, HandoffManager
  • Bus (bus/): MemoryBus (in-process), RedisBus (distributed)

Server Routes (17 modules)

Prefix Module Purpose
/api/v1/agents agents.py Agent CRUD
/api/v1/tasks tasks.py Task submit/query/cancel
/api/v1/skills skills.py Skill register/list
/api/v1/chat chat.py Chat REST + WebSocket
/api/v1/ws ws.py WebSocket channel
/api/v1/llm llm.py LLM usage
/api/v1/health health.py Health check
/api/v1/metrics metrics.py Metrics
/api/v1/evolution evolution.py + evolution_dashboard.py Self-evolution API
/api/v1/memory memory.py Memory management
/api/v1/portal portal.py Portal
/api/v1/kb kb_management.py Knowledge base
/api/v1/skill-mgmt skill_management.py Skill management
/api/v1/workflows workflows.py Workflows
/api/v1/terminal terminal.py Terminal
/api/v1/settings settings.py Settings

WebSocket Chat Protocol

Client -> Server: message, reply, confirmation_reply, cancel, ping Server -> Client: connected, token, thinking, step, final_answer, skill_match, confirmation_request, confirmation_result, ask_human, error, pong Expert Team events: team_formed, expert_step, expert_result, plan_update, team_synthesis, team_dissolved

Frontend Pages

  • /agent/chat — Chat with Expert Team view
  • /agent/code — Code/workflow
  • /agent/monitor — Evolution dashboard
  • /computer-use — Desktop control

Configuration Priority

CLI args > agentkit.yaml > env vars (${VAR:-default}) > .env > hardcoded defaults

Config search: --config path > ./agentkit.yaml > ~/.agentkit/agentkit.yaml

Conventions

  • Skill configs: configs/skills/*.yaml (15 presets)
  • LLM configs: agentkit.yaml llm section (unified with server config)
  • Pipeline configs: configs/pipelines/*.yaml
  • Expert templates: registered via ExpertTemplateRegistry
  • All Pydantic models use model_config = ConfigDict(...) not class Config
  • Test files: tests/unit/ and tests/integration/
  • Frontend stores: Pinia, one per domain (chat, team, settings)
  • Frontend components: src/agentkit/server/frontend/src/components/

Boundaries

  • Never modify pyproject.toml version without explicit request
  • Never push to main directly — use feature branches
  • Integration tests require Docker (Redis + PostgreSQL)
  • Desktop builds require Rust toolchain + PyInstaller