deerflow-code/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

This is a customized offline deployment of DeerFlow — a LangGraph-based AI super agent platform. The repo contains two sub-projects:

• offline-backend-20260512/backend/ — Python backend (FastAPI Gateway + LangGraph agent runtime)
• frontend-web/ — Vite + React 19 frontend (TypeScript, pnpm)

Managed together via shell scripts in scripts/.

Commands

Full Application (from repo root)

./scripts/start-all.sh    # Start backend + frontend as background daemons
./scripts/stop-all.sh     # Stop both
./scripts/restart-all.sh  # Restart both
./scripts/status.sh       # Check running status

Logs and PID files are written to .runtime/logs/ and .runtime/pids/.

Backend Only (from offline-backend-20260512/backend/)

make install    # Install Python dependencies (uv)
make dev        # Run Gateway API with hot-reload on port 8001
make gateway    # Run Gateway API without reload
make test       # Run all tests: PYTHONPATH=. uv run pytest tests/ -v
make lint       # Lint with ruff
make format     # Format with ruff

Run a single test file:

PYTHONPATH=. uv run pytest tests/test_<feature>.py -v

Frontend Only (from frontend-web/)

pnpm install    # Install dependencies
pnpm dev        # Start Vite dev server on port 5174
pnpm build      # Production build
pnpm typecheck  # TypeScript type checking (tsc --noEmit)

Service Ports

Service	Port	Notes
Backend (Gateway API)	8001	FastAPI + LangGraph runtime
Frontend (Vite)	5174	React SPA

Frontend connects to backend via env vars VITE_BACKEND_BASE_URL and VITE_LANGGRAPH_BASE_URL (injected by scripts/start-frontend.sh).

Architecture

Backend

The backend (offline-backend-20260512/backend/) is split into two layers:

• packages/harness/deerflow/ — publishable agent framework (deerflow.* imports): LangGraph lead agent, middleware chain, sandbox, tools, MCP, memory, models, skills. Never imports from app.*.
• app/ — application layer (app.* imports): FastAPI Gateway routers, IM channel integrations (Feishu, Slack, Telegram, DingTalk).

The Lead Agent (deerflow/agents/lead_agent/) is the single LangGraph graph entry point. It wraps a middleware chain (~18 middlewares assembled in strict order) around LLM calls, handling: thread isolation, sandbox lifecycle, uploads, memory, plan mode, vision, subagent delegation, and loop detection.

Middleware order matters — middlewares are appended in a fixed sequence; ClarificationMiddleware must always be last. See offline-backend-20260512/backend/CLAUDE.md for the full ordered list.

Harness → App import firewall is enforced by tests/test_harness_boundary.py (runs in CI).

Frontend

The frontend (frontend-web/) is a React 19 + Vite SPA:

• Routing: React Router v7; top-level routes in src/pages/PageRoutes.tsx, workspace sub-routes in src/pages/WorkspaceRoutes.tsx
• Pages: ChatPage (thread chat), AgentChatPage (custom agent chat), AgentsPage/NewAgentPage (agent management), ScheduledTasksPage/ScheduledTaskRunDetailPage (scheduled tasks), LightAppManagementPage/LightAppListPage/LightAppIframeView (轻应用中心 — register/list/embed mini-apps)
• 轻应用中心 (Light App Center): a sibling of 任务管理 in the sidebar with two pages — 应用管理 (admin registers window_open or iframe mini-apps with a route-param builder: login params resolved from localStorage.userInfo, theme params literal) and 应用列表 (card grid of window_open apps, window.open launch). iframe apps are mounted under a chosen parent menu (XK/PG/TY/综合管理) and dynamically injected into the sidebar by core/page-layout/use-sidebar-menu.ts (useSidebarMenuItems), opening in the LightAppIframeView host. Client + React Query hooks in strategy-components/api/light-apps.ts; URL builder/constants in strategy-components/lib/light-app.ts. Backend: /api/light-apps (global/shared, admin-gated writes).
• State: TanStack Query for server state, context providers (SubtasksProvider, ArtifactsProvider, PromptInputProvider) scoped to WorkspaceRoutes
• LLM streaming: @langchain/langgraph-sdk streams messages from the backend via SSE
• UI: Radix UI primitives + TailwindCSS v4 + shadcn-style components in src/components/ui/
• Shims: src/shims/ provides next/link and next/navigation polyfills (original code was Next.js)
• Login URL params (LoginPage.tsx, frontend-only — the backend never sees these): besides the auth params (ticket/userId/env/theme/yUserId/token/access_token/authToken/password), the page supports a task deep-link via ?taskId=&goPath=. After login it calls the consumer API GET {VITE_AIPI_CONSUMER_URL}/taskAnalysSearch/cop-task-detail?taskId= (header Authorization: Bearer admin, auto-injected by utils/request.ts), stashes the full data (id/overview/taskDirection/taskName/taskContent) into sessionStorage["login.copTaskDetail"], then routes by goPath: base codes 3qfx/rwfx/xdfx → that business's configured single agent Q&A (resolved via /api/business-mapping, opening message in sessionStorage.pendingAgentMessage); the -A suffix (3qfx-A/…) → multi-agent roundtable with roundtablePrefill.businessCode so the page auto-selects the configured 业务链条. goPath→businessCode: 3qfx→3Q, rwfx→6BF, xdfx→7BF. Any failure (no consumer URL / fetch error / unmapped goPath / no configured agent) silently falls back to the default home. Helpers: core/auth/task-deeplink.ts.

Configuration

Backend config lives in offline-backend-20260512/backend/config.yaml. Values starting with $ resolve as environment variables (e.g., $OPENAI_API_KEY). MCP servers and skills are configured in extensions_config.json in the same directory.

Detailed Backend Reference

The backend has its own comprehensive documentation at:

• offline-backend-20260512/backend/CLAUDE.md — architecture, middleware chain, all API routes, config schema, sandbox/memory/subagent systems
• offline-backend-20260512/backend/README.md — setup guide, technology stack