169 lines
7.2 KiB
Markdown
169 lines
7.2 KiB
Markdown
# Fischer AgentKit — Project Context
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## Rules
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- Python >= 3.11, type hints required, `pydantic>=2.0` for all data models
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- Ruff for lint + format: `ruff check src/ && ruff format src/` (target py311, line-length 100)
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- Tests: `pytest` (asyncio_mode=auto), markers: `integration`, `redis`, `postgres`
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- Never use `any` type — use proper Pydantic models or `Unknown`
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- API key comparison must use `hmac.compare_digest` (constant-time)
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- Expert names validated with `_EXPERT_NAME_RE = re.compile(r"^[a-zA-Z0-9_-]{1,64}$")`
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- HandoffTransport queues bounded (`maxsize=1024`), close uses sentinel pattern
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- Frontend: Vue 3 + TypeScript + Ant Design Vue, Pinia stores, no `require()` calls
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## Tech Stack
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- **Backend**: Python 3.11+, FastAPI, Uvicorn, Pydantic v2, SQLAlchemy 2 (async)
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- **Frontend**: Vue 3, TypeScript, Vite 5, Ant Design Vue 4, Pinia, Vue Router 4
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- **Desktop**: Tauri 2.x (Rust shell + Python sidecar)
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- **Infra**: Redis (bus/cache/state), PostgreSQL + pgvector (episodic memory)
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- **CLI**: Typer + Rich
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- **Exact versions**: see `pyproject.toml` (Python), `package.json` (Node)
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## Commands
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```bash
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# Backend
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pip install -e ".[dev]" # Install with dev deps
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agentkit gui --port 8002 # Web GUI (frontend + API)
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agentkit serve --port 8001 # API-only server
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agentkit chat # CLI interactive chat
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agentkit init # Generate agentkit.yaml
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agentkit version / doctor / usage # Utility commands
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agentkit task submit/status/list/cancel # Task management
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agentkit skill list/load/info # Skill management
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agentkit pair --name X # Generate API key for external system
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pytest # Run all tests
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pytest -m "not integration" # Unit tests only
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ruff check src/ && ruff format src/ # Lint + format
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# Frontend
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cd src/agentkit/server/frontend
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npm install # Install deps
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npm run dev # Vite dev server (proxy /api -> :8000)
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npm run build:frontend # Production build -> ../static
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npm run typecheck # TypeScript check
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# Desktop
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cd src/agentkit/server/frontend
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npm run tauri dev # Tauri dev mode
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npm run tauri build # Tauri production build
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# Docker
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docker-compose up -d # AgentKit + Redis + PostgreSQL
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```
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## Architecture
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### Request Flow
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```
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User Input -> CostAwareRouter (3-layer)
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Layer 0: RegexRules (~0ms, 0 tokens) -> DIRECT_CHAT
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Layer 1: HeuristicClassifier (~0ms) / LLM quick_classify (~500ms, ~100 tokens)
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Layer 1.5: SemanticRouter (vector similarity, optional)
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Layer 2: Capability matching / Vickrey Auction
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-> ExecutionMode: DIRECT_CHAT / REACT / SKILL_REACT / TEAM_COLLAB
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```
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### Agent Hierarchy
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```
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BaseAgent (core/base.py) — abstract, execute() is final
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+-- ConfigDrivenAgent (core/config_driven.py) — YAML-driven, 3 task modes
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+-- ReActEngine (core/react.py) — Think->Act->Observe
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+-- ReflexionAgent (core/reflexion.py) — reflection-driven
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+-- ReWOOAgent (core/rewoo.py) — plan-without-observation
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+-- StandaloneAgent (core/standalone.py) — standalone runner
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```
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### Expert Team Mode
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```
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ExpertConfig (extends AgentConfig) -> Expert (wraps ConfigDrivenAgent via AgentPool)
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ExpertTeam: manages experts, shared workspace, collaboration plan
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TeamOrchestrator: executes plan (serial/parallel/competitive + merge)
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CollaborationPlan: phases with dependencies, parallel types, merge strategies
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ExpertTeamRouter: @team prefix routing, name validation, MAX_EXPERTS=10
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HandoffTransport: InProcess (asyncio.Queue) + Redis Pub/Sub
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```
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Lifecycle: FORMING -> PLANNING -> EXECUTING -> SYNTHESIZING -> COMPLETED
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On failure: fallback to single-agent mode (lead or first active expert).
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### Module Map
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| Layer | Modules | Purpose |
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|-------|---------|---------|
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| API | `server/`, `cli/` | FastAPI routes + Typer CLI |
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| Service | `core/`, `chat/`, `skills/`, `experts/` | Agent engine, routing, skills, expert teams |
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| Data | `memory/`, `session/`, `bus/` | Persistence, sessions, messaging |
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| Utility | `llm/`, `tools/`, `evolution/`, `quality/`, `mcp/` | LLM gateway, tools, self-evolution, quality, MCP |
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### Key Subsystems
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- **LLM Gateway** (`llm/`): 6 providers (OpenAI/Anthropic/Gemini/Doubao/Wenxin/Yuanbao), fallback, semantic cache, usage tracking
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- **Memory** (`memory/`): 4-layer (SOUL/USER/MEMORY/DAILY), WorkingMemory (Redis), EpisodicMemory (PG+pgvector), SemanticMemory (HTTP RAG)
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- **Evolution** (`evolution/`): Reflector, PromptOptimizer (genetic), PitfallDetector, ABTester
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- **Tools** (`tools/`): 21 built-in + MCP extension, composition (SequentialChain/ParallelFanOut/DynamicSelector)
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- **Pipeline** (`orchestrator/`): PipelineEngine, SagaOrchestrator, DynamicPipeline, HandoffManager
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- **Bus** (`bus/`): MemoryBus (in-process), RedisBus (distributed)
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### Server Routes (17 modules)
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| Prefix | Module | Purpose |
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|--------|--------|---------|
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| `/api/v1/agents` | agents.py | Agent CRUD |
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| `/api/v1/tasks` | tasks.py | Task submit/query/cancel |
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| `/api/v1/skills` | skills.py | Skill register/list |
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| `/api/v1/chat` | chat.py | Chat REST + WebSocket |
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| `/api/v1/ws` | ws.py | WebSocket channel |
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| `/api/v1/llm` | llm.py | LLM usage |
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| `/api/v1/health` | health.py | Health check |
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| `/api/v1/metrics` | metrics.py | Metrics |
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| `/api/v1/evolution` | evolution.py + evolution_dashboard.py | Self-evolution API |
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| `/api/v1/memory` | memory.py | Memory management |
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| `/api/v1/portal` | portal.py | Portal |
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| `/api/v1/kb` | kb_management.py | Knowledge base |
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| `/api/v1/skill-mgmt` | skill_management.py | Skill management |
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| `/api/v1/workflows` | workflows.py | Workflows |
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| `/api/v1/terminal` | terminal.py | Terminal |
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| `/api/v1/settings` | settings.py | Settings |
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### WebSocket Chat Protocol
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Client -> Server: `message`, `reply`, `confirmation_reply`, `cancel`, `ping`
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Server -> Client: `connected`, `token`, `thinking`, `step`, `final_answer`, `skill_match`, `confirmation_request`, `confirmation_result`, `ask_human`, `error`, `pong`
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Expert Team events: `team_formed`, `expert_step`, `expert_result`, `plan_update`, `team_synthesis`, `team_dissolved`
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### Frontend Pages
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- `/agent/chat` — Chat with Expert Team view
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- `/agent/code` — Code/workflow
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- `/agent/monitor` — Evolution dashboard
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- `/computer-use` — Desktop control
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### Configuration Priority
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CLI args > `agentkit.yaml` > env vars (`${VAR:-default}`) > `.env` > hardcoded defaults
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Config search: `--config` path > `./agentkit.yaml` > `~/.agentkit/agentkit.yaml`
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## Conventions
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- Skill configs: `configs/skills/*.yaml` (15 presets)
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- LLM configs: `agentkit.yaml` llm section (unified with server config)
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- Pipeline configs: `configs/pipelines/*.yaml`
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- Expert templates: registered via `ExpertTemplateRegistry`
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- All Pydantic models use `model_config = ConfigDict(...)` not `class Config`
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- Test files: `tests/unit/` and `tests/integration/`
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- Frontend stores: Pinia, one per domain (chat, team, settings)
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- Frontend components: `src/agentkit/server/frontend/src/components/`
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## Boundaries
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- Never modify `pyproject.toml` version without explicit request
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- Never push to main directly — use feature branches
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- Integration tests require Docker (Redis + PostgreSQL)
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- Desktop builds require Rust toolchain + PyInstaller
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