# DeerFlow Backend DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent memory, and extensible tool integration. The backend enables AI agents to execute code, browse the web, manage files, delegate tasks to subagents, and retain context across conversations - all in isolated, per-thread environments. --- ## Architecture ``` ┌──────────────────────────────────────┐ │ Nginx (Port 2026) │ │ Unified reverse proxy │ └───────┬──────────────────┬───────────┘ │ │ /api/langgraph/* │ │ /api/* (other) ▼ ▼ ┌────────────────────┐ ┌────────────────────────┐ │ LangGraph Server │ │ Gateway API (8001) │ │ (Port 2024) │ │ FastAPI REST │ │ │ │ │ │ ┌────────────────┐ │ │ Models, MCP, Skills, │ │ │ Lead Agent │ │ │ Memory, Uploads, │ │ │ ┌──────────┐ │ │ │ Artifacts │ │ │ │Middleware│ │ │ └────────────────────────┘ │ │ │ Chain │ │ │ │ │ └──────────┘ │ │ │ │ ┌──────────┐ │ │ │ │ │ Tools │ │ │ │ │ └──────────┘ │ │ │ │ ┌──────────┐ │ │ │ │ │Subagents │ │ │ │ │ └──────────┘ │ │ │ └────────────────┘ │ └────────────────────┘ ``` **Request Routing** (via Nginx): - `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming - `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads, thread-local cleanup - `/` (non-API) → Frontend - Next.js web interface Health probes are always unauthenticated, even when API authentication is enabled: `GET|HEAD /health`, `/api/health`, and `/api/langgraph/health` all return the Gateway health status without requiring a cookie or bearer token. --- ## Core Components ### Lead Agent The single LangGraph agent (`lead_agent`) is the runtime entry point, created via `make_lead_agent(config)`. It combines: - **Dynamic model selection** with thinking and vision support - **Middleware chain** for cross-cutting concerns (9 middlewares) - **Tool system** with sandbox, MCP, community, and built-in tools - **Subagent delegation** for parallel task execution - **System prompt** with skills injection, memory context, and working directory guidance ### Middleware Chain Middlewares execute in strict order, each handling a specific concern: | # | Middleware | Purpose | |---|-----------|---------| | 1 | **ThreadDataMiddleware** | Creates per-thread isolated directories (workspace, uploads, outputs) | | 2 | **UploadsMiddleware** | Injects newly uploaded files into conversation context | | 3 | **SandboxMiddleware** | Acquires sandbox environment for code execution | | 4 | **SummarizationMiddleware** | Reduces context when approaching token limits (optional) | | 5 | **TodoListMiddleware** | Tracks multi-step tasks in plan mode (optional) | | 6 | **TitleMiddleware** | Auto-generates conversation titles after first exchange | | 7 | **MemoryMiddleware** | Queues conversations for async memory extraction | | 8 | **ViewImageMiddleware** | Injects image data for vision-capable models (conditional) | | 9 | **ClarificationMiddleware** | Intercepts clarification requests and interrupts execution (must be last) | ### Sandbox System Per-thread isolated execution with virtual path translation: - **Abstract interface**: `execute_command`, `read_file`, `write_file`, `list_dir` - **Providers**: `LocalSandboxProvider` (filesystem) and `AioSandboxProvider` (Docker, in community/) - **Virtual paths**: `/mnt/user-data/{workspace,uploads,outputs}` → thread-specific physical directories - **Skills path**: `/mnt/skills` → `deer-flow/skills/` directory - **Skills loading**: Recursively discovers nested `SKILL.md` files under `skills/{public,custom}` and preserves nested container paths - **Conditional WeKnora LLMWiki**: a non-empty `llmwiki.weknora.api_base_url` enables the DeerFlow-native WeKnora BFF and UI; an empty address preserves the existing LLMWiki page. WeKnora remains independently deployed and owns its database, Redis, storage, parsers and models. DeerFlow keeps only the API/admin addresses, user/publication mappings, agent knowledge-base bindings and per-conversation selections; it stores and sends no WeKnora credential, so the endpoint must be restricted to a trusted network and allow DeerFlow traffic. Owners publish directly to the public LLMWiki without an approval queue for ordinary bases; the system-owned conversation-deposit base is always published, shared by all users, excluded from My Knowledge, immutable through user/admin detail operations, and recognized by both its reserved name and `system` owner. An existing same-name remote mapping is adopted in place as the system deposit so its mapping id and document links stay valid. Its user-facing knowledge-base description and generated document metadata use the `cmzs` brand; list/detail reads synchronize stale remote descriptions, while conversation-deposit search results, source previews and embedded-detail JSON rewrite legacy brand text to `cmzs`. Deposit requests must reference a thread accessible to the current account. Personal knowledge-base lists are scoped to the current DeerFlow account even for admins, while frontend LLMWiki query caches also include the active account id so switching accounts cannot reuse another account's list/detail data. Admins remain the only role that sees the WeKnora connection-config tab. Knowledge cards open a dedicated WeKnora-style detail route: Wiki-enabled bases default to Wiki, then documents and graph; standard bases hide Wiki and default to documents. Writable ordinary knowledge-base details expose a native multi-file upload action that submits files sequentially and reports aggregate success/failure; read-only public bases and the system deposit base do not. It supports file/URL/manual-Markdown imports, Wiki page browsing, document preview, real parsed chunks and Wiki graph nodes/edges. New chats use a compact WeKnora-style searchable/grouped multi-select inside the composer, while agent create/edit pages bind spaces through an inline panel. A single proxy service identity means DeerFlow users are isolated logically while their WeKnora entities share that identity's workspace. The read-only `llmwiki_search` tool reauthorizes the current DeerFlow user on every call and renders credential-free source previews; source previews prefer cleaned Wiki/LLMWiki text when a retrieved chunk maps to a Wiki page, with raw chunks as the fallback. The detail page first creates a signed iframe session through the normal DeerFlow API auth path, then points the iframe directly at the proxied WeKnora `/platform/...` route using the signed `deerflow_weknora_embed` cookie; global auth/CSRF bypass is limited to those signed iframe proxy requests, and HTTPS cookie attributes honor `X-Forwarded-Proto` behind reverse proxies. The iframe proxy keeps WeKnora's root-relative `/platform`, `/assets`, `/locales`, and `/api/v1/*` paths, but is registered last through `llmwiki.proxy_router` so it cannot intercept first-party Gateway APIs such as `POST /api/v1/auth/login/username`. See [docs/LLMWIKI_WEKNORA_INTEGRATION_ZH.md](docs/LLMWIKI_WEKNORA_INTEGRATION_ZH.md). - **Embedded WeKnora detail chrome**: the proxy hides WeKnora's application sidebar (`.main > .aside_box`) and expands the detail outlet to the full iframe width, while preserving the Wiki page's own index/navigation panel. - **Prefixed WeKnora deployment**: the frontend may be published under a separate prefix such as `/magentweb`, while embedded WeKnora detail traffic is served by the Gateway under `/deerflow`. The Gateway rewrites HTML root assets, WeKnora API URLs, redirects, and Vite dynamic dependency maps so page assets stay under `/deerflow`; the prefixed proxy routes must be registered before the unprefixed fallback. The signed embed cookie covers `/deerflow`, and auth/CSRF recognition includes all supported static directories. - **File-write safety**: `str_replace` serializes read-modify-write per `(sandbox.id, path)` so isolated sandboxes keep concurrency even when virtual paths match - **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access) ### Subagent System Async task delegation with concurrent execution: - **Built-in agents**: `general-purpose` (full toolset) and `bash` (command specialist, exposed only when shell access is available) - **Concurrency**: Max 3 subagents per turn, 15-minute timeout - **Execution**: Background thread pools with status tracking and SSE events - **Flow**: Agent calls `task()` tool → executor runs subagent in background → polls for completion → returns result ### Memory System LLM-powered persistent context retention across conversations: - **Automatic extraction**: Analyzes conversations for user context, facts, and preferences - **Structured storage**: User context (work, personal, top-of-mind), history, and confidence-scored facts - **Debounced updates**: Batches updates to minimize LLM calls (configurable wait time) - **System prompt injection**: Top facts + context injected into agent prompts - **Storage**: JSON file with mtime-based cache invalidation ### Tool Ecosystem | Category | Tools | |----------|-------| | **Sandbox** | `bash`, `ls`, `read_file`, `write_file`, `str_replace` | | **Built-in** | `present_files`, `ask_clarification`, `view_image`, `task` (subagent) | | **Community** | Tavily (web search), Jina AI (web fetch), Firecrawl (scraping), DuckDuckGo (image search) | | **MCP** | Any Model Context Protocol server (stdio, SSE, HTTP transports) | | **Skills** | Domain-specific workflows injected via system prompt | #### Configurable HTTP web search The config-defined `web_search` tool can call a deployment-specific JSON API instead of a public search provider. Set its `endpoint`, static `payload`, SSL verification, timeout, result limit, and `result_url_template` in `config.yaml`. At runtime only `payload.query` is overwritten with the model's search text. The API response must use `{"results": [{"recUuid": "", "content": "", "title": "", "url": ""}]}`; missing or null result fields are normalized to empty strings. When `recUuid` is present, `{recUuid}` in `result_url_template` replaces the original result URL; otherwise the original URL is retained. Set `enabled: false` to keep the tool completely out of the agent's registered tool list. `verify_ssl: false` supports trusted internal HTTPS services with self-signed certificates. ### Gateway API FastAPI application providing REST endpoints for frontend integration: | Route | Purpose | |-------|---------| | `GET /api/models` | List available LLM models | | `GET/PUT /api/mcp/config` | Manage MCP server configurations | | `GET/PUT /api/skills` | List and manage skills | | `POST /api/skills/install` | Install skill from `.skill` archive | | `GET/POST /api/agents` | List visible agents or create a user-owned agent | | `GET/PUT/DELETE /api/agents/{id}` | Read, update, or delete an id-addressed agent | | `GET/POST/PUT/DELETE /api/roundtable-chains` | Manage user-owned roundtable business chains; each seat may optionally carry `position_id` for the separate position-collaboration workspace, plus `human_validation` to mark a Step 2 client-side pause checkpoint after that seat/stage completes (the original roundtable has no extra validation card) | | `GET/PUT /api/business-mapping` | Read global business mappings. Updates to 3Q/6BF/7BF require an administrator; the 8BF public business-chain mapping is maintained exclusively by the trusted `lqq` account (derived from the authenticated email prefix). | | `GET/POST/PUT /api/position-roundtable/sessions` | Standalone position-collaboration sessions: freeze task/intent/chain snapshots, index one direct-agent node per seat, build upstream handoff context (including bounded UTF-8 excerpts of text artifacts), and make archived sessions read-only without invoking the original roundtable coordinator or jobs. Built-in summary/action-plan completion mirrors visible report text into a Markdown artifact when a model omits its final `write_file` call. `POST /sessions/{id}/nodes/{nodeKey}/reject` accepts an optional reason, marks the rejected delivery for rework, and invalidates affected downstream deliveries so they are regenerated in order. The same router is also mounted at `/api/multi-agent/position-roundtable/*` for intranet gateways that only forward the established multi-agent namespace; the compatibility mount is intentionally omitted from OpenAPI. | | `GET /api/memory` | Retrieve memory data | | `POST /api/memory/reload` | Force memory reload | | `GET /api/memory/config` | Memory configuration | | `GET /api/memory/status` | Combined config + data | | `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) | | `GET /api/threads/{id}/uploads/list` | List uploaded files | | `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail | | `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts | | `GET/POST /api/deep-research/*` | Durable Deep Research sessions and jobs. `quick`/`basic` run the compact report flow; `detailed` plans subtopics and writes independent sections; `deep` recursively investigates bounded follow-up branches. All modes reuse DeerFlow's configured material provider, LLM factory, hidden-thread artifacts, and SSE event log. Optional report illustrations use a separately configured OpenAI-compatible image endpoint and are served only through the owning session's authenticated artifact route. | | `POST /api/ai-writing/sample/extract` | Extract text from Word/PDF/Markdown/TXT samples for AI-writing imitation mode | | `POST /cop/saveSuperiorTask` | TaskCOP compatibility API (DeerFlow Bearer/session authentication required): create a situation-overview task with a sequential four-digit id (`0001`, `0002`, ...) and return the legacy `{state, msg, data}` envelope | | `POST /api/taskcop/import/tasks` | TaskCOP compatibility API (authenticated): import tasks from a JSON array or `tasks`/`records` envelope; every row's supplied `id`/`taskId` is authoritative and a duplicate id completely replaces the stored task | | `DELETE /cop/tasks/{task_id}` | TaskCOP compatibility API (authenticated): delete one task and its task-scoped situation-report document | | `GET /taskAnalyseSearch/cop-task-three-list` | TaskCOP compatibility API (authenticated): paginated task list for the situation-overview sentiment page (`content`, `taskStatus`, `taskDirection`, `startTime`, `endTime`) | | `GET/POST/PUT /api/task-reports/by-task/{task_id}` | Shared task-scoped situation-report detail. Query, first save, and full update all use the legacy-compatible `{state, msg, data:[{id, taskId, createTime, updateTime, sessionId, contentJson, categoryType}]}` shape; `contentJson` is preserved as a JSON string. Saving a non-empty report automatically moves the matching TaskCOP task to status `25` (the legacy list's “已完成” state). | | `POST /api/task-reports/import/by-task/{task_id}` | TaskCOP detail import (authenticated): accept a `sq-report-mock.json`-compatible `data`/`records` array and atomically replace the selected task's full report. The path task id overrides task ids inside the file. | The separately deployed TaskCOP Vue app uses `POST /api/v1/auth/login/username` directly (its `username`, `userId`, or `yUserId` URL parameter identifies the user), so this compatibility flow does not depend on the retired Consumer login service. It calls the Gateway address configured as `b1ConsumerUrl` directly; it does not use a Vite `/api` reverse proxy. An empty TaskCOP store remains empty; task data is introduced through normal creation or the JSON import API. The Gateway CORS middleware always merges local Vite origins `http://localhost|127.0.0.1:{5173,5174,3000,8080}`, including when `GATEWAY_CORS_ORIGINS` is set for another frontend. This keeps a Vite server that falls back from 5173 to 5174 (or the reverse) working after a backend restart. For a deployment, set `GATEWAY_CORS_ORIGINS` to the exact additional frontend origins that are allowed to call the Gateway. ### Deep Research modes Deep Research is a separate Gateway feature rather than part of AI Writing. Its `detailed` runner adapts GPT Researcher's detailed-report control flow (plan subtopics, collect materials concurrently, write sections concurrently, then assemble the report). Its `deep` runner adapts the recursive DeepResearchSkill flow (branch, extract learnings, investigate follow-ups) with a deterministic query budget derived from `deep_breadth` and `deep_depth`. The runners live in `packages/harness/deerflow/agents/deep_research/runners/` and only use injected DeerFlow adapters. `multi_agent` is a LangGraph 1.x editor/researcher/writer/ reviewer graph: it persists a plan-review pause, releases the worker lease, and resumes from the approved or revised plan through the existing durable-job API. Report prose uses the configured LangChain model's `astream()` path. Each native model delta is fanned out as an in-process `report_delta` SSE frame so the right-side `report.md` sandbox can write smoothly, while bounded, persisted `report_chunk` events remain reconnect checkpoints. The durable log is still authoritative: a browser reconnect, slow consumer, or another worker falls back to `?after=` replay rather than starting a second model call. The short non-streaming control-plane calls used to plan queries, sections, and reviews are independently capped at 45 seconds (maximum 120 seconds). If query or section planning times out, the runner emits a visible recoverable warning and searches the original research topic instead of leaving the job at the planning step indefinitely. Report-prose streaming is intentionally not capped by this control-plane limit. Planning also persists `queries_planned` before each basic, detailed, or multi-agent parallel search fan-out; the completed session stores its terminal `lastJobId` in the usage snapshot so the conversation workbench can replay the full planning/search/source/curation trace after a page refresh. These are observable execution facts only, never hidden model reasoning. Detailed reports use stable introduction/section/conclusion targets; a multi-agent review revision emits `report_reset` before the replacement draft. Every session may also provide a bounded `custom_outline`. Markdown headings or numbered entries become the deterministic section plan for `detailed` and `multi_agent`; `basic` and `deep` receive the same outline as a guarded report-writing constraint. The outline is frozen in the session snapshot and never treated as a system instruction. All owner-scoped Deep Research routes resolve the platform's asynchronous `get_current_user` identity before querying or writing persistence, so session and job records are always bound to a concrete user ID. Report artifacts are server-named and written through the canonical sandbox virtual path (`/mnt/user-data/outputs/`), which the path layer maps to the host filesystem. This keeps native Windows deployments compatible without relaxing the sandbox traversal guard. Deep Research reuses the DeerFlow Q&A retrieval stack instead of a separate scraper. It first runs enabled research skills (`deep-search`, `web-research`, and enabled knowledge-base skills) through the same `SubagentExecutor` runtime used by Q&A, then uses a configured `web_search` provider when present. If this offline deployment still has the shipped placeholder/disabled `web_search` configuration, it falls back to DeerFlow's existing keyless DuckDuckGo provider and, when that provider returns only irrelevant engine noise, a second real-web search source. It persists only real returned sources. A missing intranet endpoint therefore cannot by itself produce an empty Deep Research report. The capabilities API reports `skill` only when its required Q&A tool is available, and reports `web_search` when configured search or the online fallback can run. Cancelling a queued research job finalizes its job row and immediately marks the owning session `cancelled` with no active job, so it cannot retain a per-user concurrency slot. Saved `draft` sessions do not consume a concurrency slot; the two-job limit only applies to sessions with a `running` or `awaiting_input` durable job. #### Optional report illustrations DeerFlow's stock configuration includes image recognition but no image-generation provider. Deep Research therefore keeps illustrations disabled until an operator fills `config.yaml -> deep_research.image_generation` with an OpenAI-compatible image endpoint, API key, and model. When configured, the capabilities endpoint enables the page's “生成报告配图” switch. A selected job requests at most four base64 image results, writes them as protected `research-image-*.png|jpg|webp` artifacts, injects `deep-research://` links into the Markdown report, and emits image SSE events. A failed/unconfigured image provider only emits a recoverable warning; the text report still completes. Completed reports also expose `GET /api/deep-research/sessions/{id}/messages` and `POST /api/deep-research/sessions/{id}/chat`. Follow-up answers are grounded only in that session's report, selected sources, and recent follow-up history; the assistant returns persisted source ids for every accepted citation. Passing `allow_new_research=true` is rejected rather than silently triggering another search job. Owners can change the follow-up evidence set with `PATCH /api/deep-research/sessions/{id}/sources/{source_id}` after a research job stops. That selection is applied immediately to later follow-up prompts; it never rewrites the completed report, and edits are rejected while the job is running so they cannot race the automatic curation projection. The Deep Research page is a conversation workspace: the original research question, observable retrieval steps, completion summary, and report-grounded follow-ups appear in chronological order. The report itself is written in the adjacent sandbox as `/mnt/user-data/outputs/report.md`, with Word, Markdown, and HTML export actions. Word export uses the frontend's existing `docx` dependency and includes the collected reference list, without changing backend Python dependencies. In chat-collection sessions the report configuration card shows the unique material count inferred from the collector's visible tool results before writing begins. The write lifecycle itself is rendered as ordinary workspace message-list tool steps (not a second timeline component), and remains in the transcript after completion; the generated digest is a normal assistant message below those steps. As soon as the runner enters its summary phase, that same assistant message is inserted in a streaming state and receives each SSE `summary_delta`; completion only changes it in place to the final report-complete wording rather than appending a delayed second summary card. Each completed chat-collection report also contributes a normal file card to the message history, named from the report Markdown title rather than the internal `report.md` artifact path. Historical sessions keep the sandbox closed on replay, and opening that card selects the session's virtual report artifact in the sandbox; its download action uses the existing Markdown/Word export dialog. The per-user active-job limit and the default in-job retrieval/write concurrency are both three. The Deep Research composer uses the installed TDesign `Select`, populated from `/api/models`, for an optional per-research model override. It is disabled during active collection/writing, is sent as `model_name` to collector turns, and is applied to the `fast_model`, `smart_model`, and `strategic_model` roles when a session or report job starts. Completed-report Q&A uses `POST /api/deep-research/sessions/{id}/chat/stream` for native SSE deltas; the earlier `/chat` JSON endpoint remains for API compatibility. An explicit scoped edit such as “修改第一段”、 “重新生成一下第一段” or “重写第二节” uses the separate `POST /api/deep-research/sessions/{id}/rewrite/stream` report-section writing pipeline. That route requires a deterministic Markdown range and returns 422 instead of falling back to Q&A when no target is supplied. Ordinary follow-up questions remain report/source-grounded and never mutate the report. The streamed replacement candidate is shown in the message list while the sandbox keeps the current report unchanged. The completed candidate creates a confirmation card; only `POST /sessions/{id}/messages/{messageId}/rewrite-proposal` with `{"action":"apply"}` substitutes the matching range and saves the assembled Markdown. The proposal carries the source report hash, so a stale candidate is rejected instead of overwriting a later edit; dismissing/cancelling leaves the persisted report unchanged. This does not initiate new web research. Legacy unmarked candidates from before this route split are inferred only when their immediately preceding user message contains the same explicit scoped edit. Position-roundtable history supports two modes. A non-empty `external_task_id` from the frontend route's `taskId` creates a row with `task_scoped = true`, so all sessions, nodes and persisted conversation snapshots for that task are shared across users; `user_id` is then creator audit metadata only. When the field is absent or empty, the original per-user personal-session behavior remains unchanged. Alembic revision `20260808_01` adds `task_scoped` with the safe default `false` for historical rows plus the task/update-time index. ### Roundtable concurrency guarantees Position-roundtable node completion, rejection, thread binding and activation are database transactions with row locking and retry-stable command ids. This keeps simultaneous seat completion from leaving a downstream stage locked. Task and intent snapshots are frozen when the chain becomes active, so a late request from another user cannot alter the confirmed input. Background roundtable jobs use a database unique active-job key and renewable worker leases; cancelled jobs are finalized by the lease holder or recovered after an expired lease, allowing users to start a new job after a worker failure. The ordinary unit tests use SQLite. To verify deployed database semantics with real PostgreSQL row locks and independent connections, set `DEERFLOW_POSTGRES_CONCURRENCY_TEST_URL` and run: ```bash PYTHONPATH=. uv run --no-sync pytest tests/test_roundtable_postgres_concurrency.py -q ``` AI writing supports a sample-imitation mode: the frontend can upload or paste a sample article, the Gateway extracts text for document files, and the `ai_writing` graph runs a `sample_analyzer` node before normal intent/research/outline/draft stages. The resulting style profile is injected into outline and draft prompts with explicit guardrails against copying source sentences, facts, names, or data. ### Custom Agents Custom agents are indexed in the `agents` database table. The `id` column is the stable runtime identifier and filesystem directory name (`.deer-flow/agents/{id}/SOUL.md`); `name` is display-only, may be Chinese, and may be duplicated. Rows with `user_id = NULL` are built-in agents, user rows are private unless `published = true`, and users can only update or delete agents they created. ### AI Writing Intranet Retrieval AI 写作的「知识库检索」会直连 `ai_writing.intranet_search_url` 内网 ES/知识库接口，并默认跳过代理环境变量和 HTTPS 证书校验，适配自签名或内部 CA 证书的内网服务。需要强校验时可在 `config.yaml` 里设置 `ai_writing.intranet_verify_ssl: true`。通用检索里的非内置技能在 `ai_writing.researcher_skill_agent: true` 时会用真实 agent 运行时执行技能：以 `ai-writing-researcher` 身份读取该技能的 `SKILL.md`，并按技能要求通过 `bash` 运行脚本，弱模型也会在本轮任务里收到明确的读文件和脚本执行指令。 ### Installable Sentiment Analysis Skill skill-packages/sentiment-analysis.skill is a separately installable skill package. It keeps the external AG-UI gateway address in config/sentiment-agent.json, resolves Basic Auth from deployment environment variables, and normalizes the upstream SSE stream into NDJSON (thinking_delta / answer_delta / done) for callers that need incremental rendering. It does not alter the existing frontend sentiment-analysis page. See skill-packages/sentiment-analysis/docs/调用与安装说明.md for installation and invocation. ### IM Channels The IM bridge supports Feishu, Slack, and Telegram. Slack and Telegram still use the final `runs.wait()` response path, while Feishu now streams through `runs.stream(["messages-tuple", "values"])` and updates a single in-thread card in place. For Feishu card updates, DeerFlow stores the running card's `message_id` per inbound message and patches that same card until the run finishes, preserving the existing `OK` / `DONE` reaction flow. --- ## Quick Start ### Prerequisites - Python 3.12+ - [uv](https://docs.astral.sh/uv/) package manager - API keys for your chosen LLM provider ### Installation ```bash cd deer-flow # Copy configuration files cp config.example.yaml config.yaml # Install backend dependencies cd backend make install ``` ### Configuration Edit `config.yaml` in the project root: ```yaml models: - name: gpt-4o display_name: GPT-4o use: langchain_openai:ChatOpenAI model: gpt-4o api_key: $OPENAI_API_KEY supports_thinking: false supports_vision: true - name: gpt-5-responses display_name: GPT-5 (Responses API) use: langchain_openai:ChatOpenAI model: gpt-5 api_key: $OPENAI_API_KEY use_responses_api: true output_version: responses/v1 supports_vision: true ``` Set your API keys: ```bash export OPENAI_API_KEY="your-api-key-here" ``` For a LiteLLM OpenAI-compatible proxy, keep `supports_thinking: false` unless the configured model has an explicit, proxy-supported thinking control. This prevents DeerFlow's connection check from sending vendor-specific `thinking` fields that a generic OpenAI-compatible gateway may reject. ### Running **Full Application** (from project root): ```bash make dev # Starts LangGraph + Gateway + Frontend + Nginx ``` Access at: http://localhost:2026 **Backend Only** (from backend directory): ```bash # Terminal 1: LangGraph server make dev # Terminal 2: Gateway API make gateway ``` Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001 --- ## Project Structure ``` backend/ ├── src/ │ ├── agents/ # Agent system │ │ ├── lead_agent/ # Main agent (factory, prompts) │ │ ├── middlewares/ # 9 middleware components │ │ ├── memory/ # Memory extraction & storage │ │ └── thread_state.py # ThreadState schema │ ├── gateway/ # FastAPI Gateway API │ │ ├── app.py # Application setup │ │ └── routers/ # 6 route modules │ ├── sandbox/ # Sandbox execution │ │ ├── local/ # Local filesystem provider │ │ ├── sandbox.py # Abstract interface │ │ ├── tools.py # bash, ls, read/write/str_replace │ │ └── middleware.py # Sandbox lifecycle │ ├── subagents/ # Subagent delegation │ │ ├── builtins/ # general-purpose, bash agents │ │ ├── executor.py # Background execution engine │ │ └── registry.py # Agent registry │ ├── tools/builtins/ # Built-in tools │ ├── mcp/ # MCP protocol integration │ ├── models/ # Model factory │ ├── skills/ # Skill discovery & loading │ ├── config/ # Configuration system │ ├── community/ # Community tools & providers │ ├── reflection/ # Dynamic module loading │ └── utils/ # Utilities ├── docs/ # Documentation ├── tests/ # Test suite ├── langgraph.json # LangGraph server configuration ├── pyproject.toml # Python dependencies ├── Makefile # Development commands └── Dockerfile # Container build ``` --- ## Configuration ### Main Configuration (`config.yaml`) Place in project root. Config values starting with `$` resolve as environment variables. Key sections: - `models` - LLM configurations with class paths, API keys, thinking/vision flags - `tools` - Tool definitions with module paths and groups - `tool_groups` - Logical tool groupings - `sandbox` - Execution environment provider - `skills` - Skills directory paths - `title` - Auto-title generation settings - `summarization` - Context summarization settings - `subagents` - Subagent system (enabled/disabled) - `memory` - Memory system settings (enabled, storage, debounce, facts limits) Provider note: - `models[*].use` references provider classes by module path (for example `langchain_openai:ChatOpenAI`). - If a provider module is missing, DeerFlow now returns an actionable error with install guidance (for example `uv add langchain-google-genai`). ### Browser CORS The Gateway permits the local Vite development origins on ports 5173, 5174, 3000, and 8080 for both `localhost` and `127.0.0.1` with credentials, even when `GATEWAY_CORS_ORIGINS` is set. For a separate deployed frontend, set `GATEWAY_CORS_ORIGINS` to its exact origin (including scheme and port), for example `http://47.88.25.99:7010`. ### Extensions Configuration (`extensions_config.json`) MCP servers and skill states in a single file: ```json { "mcpServers": { "github": { "enabled": true, "type": "stdio", "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"} }, "secure-http": { "enabled": true, "type": "http", "url": "https://api.example.com/mcp", "oauth": { "enabled": true, "token_url": "https://auth.example.com/oauth/token", "grant_type": "client_credentials", "client_id": "$MCP_OAUTH_CLIENT_ID", "client_secret": "$MCP_OAUTH_CLIENT_SECRET" } } }, "skills": { "pdf-processing": {"enabled": true} } } ``` ### Environment Variables - `DEER_FLOW_CONFIG_PATH` - Override config.yaml location - `DEER_FLOW_EXTENSIONS_CONFIG_PATH` - Override extensions_config.json location - Model API keys: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `DEEPSEEK_API_KEY`, etc. - Tool API keys: `TAVILY_API_KEY`, `GITHUB_TOKEN`, etc. ### LangSmith Tracing DeerFlow has built-in [LangSmith](https://smith.langchain.com) integration for observability. When enabled, all LLM calls, agent runs, tool executions, and middleware processing are traced and visible in the LangSmith dashboard. **Setup:** 1. Sign up at [smith.langchain.com](https://smith.langchain.com) and create a project. 2. Add the following to your `.env` file in the project root: ```bash LANGSMITH_TRACING=true LANGSMITH_ENDPOINT=https://api.smith.langchain.com LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx LANGSMITH_PROJECT=xxx ``` **Legacy variables:** The `LANGCHAIN_TRACING_V2`, `LANGCHAIN_API_KEY`, `LANGCHAIN_PROJECT`, and `LANGCHAIN_ENDPOINT` variables are also supported for backward compatibility. `LANGSMITH_*` variables take precedence when both are set. ### Langfuse Tracing DeerFlow also supports [Langfuse](https://langfuse.com) observability for LangChain-compatible runs. Add the following to your `.env` file: ```bash LANGFUSE_TRACING=true LANGFUSE_PUBLIC_KEY=pk-lf-xxxxxxxxxxxxxxxx LANGFUSE_SECRET_KEY=sk-lf-xxxxxxxxxxxxxxxx LANGFUSE_BASE_URL=https://cloud.langfuse.com ``` If you are using a self-hosted Langfuse deployment, set `LANGFUSE_BASE_URL` to your Langfuse host. ### Dual Provider Behavior If both LangSmith and Langfuse are enabled, DeerFlow initializes and attaches both callbacks so the same run data is reported to both systems. If a provider is explicitly enabled but required credentials are missing, or the provider callback cannot be initialized, DeerFlow raises an error when tracing is initialized during model creation instead of silently disabling tracing. **Docker:** In `docker-compose.yaml`, tracing is disabled by default (`LANGSMITH_TRACING=false`). Set `LANGSMITH_TRACING=true` and/or `LANGFUSE_TRACING=true` in your `.env`, together with the required credentials, to enable tracing in containerized deployments. --- ## Development ### Commands ```bash make install # Install dependencies make dev # Run LangGraph server (port 2024) make gateway # Run Gateway API (port 8001) make lint # Run linter (ruff) make format # Format code (ruff) ``` ### Code Style - **Linter/Formatter**: `ruff` - **Line length**: 240 characters - **Python**: 3.12+ with type hints - **Quotes**: Double quotes - **Indentation**: 4 spaces ### Testing ```bash uv run pytest ``` ### Runtime appearance themes The admin appearance settings are persisted in `.deer-flow/system_settings.json`. Besides the built-in themes, an administrator can create up to 12 runtime themes through `PUT /api/system-settings/appearance`. Each theme stores a safe six-digit `primary` colour, an optional primary-button `text_color`, `light`/`dark` mode, name, enabled flag and sort order. The frontend derives the rest of the visual tokens; the optional `overrides` object only exposes ten interaction colours (`button_hover`, `navigation_gradient_start`, `navigation_gradient_end`, `selected_background`, `border`, `muted`, `muted_text`, `destructive`, `brand_title`, `primary_foreground`) for exceptional brand requirements. The two navigation values override the automatically derived top-bar gradient. A custom theme id must use the `custom-theme-` prefix. Disabled or removed custom themes cannot remain the system default and automatically fall back to `light`. --- ## Technology Stack - **LangGraph** (1.0.6+) - Agent framework and multi-agent orchestration - **LangChain** (1.2.3+) - LLM abstractions and tool system - **FastAPI** (0.115.0+) - Gateway REST API - **langchain-mcp-adapters** - Model Context Protocol support - **agent-sandbox** - Sandboxed code execution - **markitdown** - Multi-format document conversion - **tavily-python** / **firecrawl-py** - Web search and scraping --- ## Documentation - [Configuration Guide](docs/CONFIGURATION.md) - [Architecture Details](docs/ARCHITECTURE.md) - [API Reference](docs/API.md) - [Offline Docker wheelhouse deployment (ZH)](../README.md) - [File Upload](docs/FILE_UPLOAD.md) - [Path Examples](docs/PATH_EXAMPLES.md) - [Context Summarization](docs/summarization.md) - [Plan Mode](docs/plan_mode_usage.md) - [Backend Run/Stop Guide (ZH)](docs/BACKEND_RUN_STOP_ZH.md) - [Setup Guide](docs/SETUP.md) --- ## License See the [LICENSE](../LICENSE) file in the project root. ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines. ## Position roundtable durable history The position-roundtable workflow stores database-owned recovery snapshots for Step-1 intent chat, every position-node chat, the summary/action-plan chats, unsent composer drafts, and generated text deliverables. LangGraph checkpoints remain the execution source, but SQL history continues to render after a checkpoint or sandbox-output volume is replaced. Apply Alembic revision `20260723_05` (or simply upgrade to `head`) before deploying this version. Its live intent conversation still uses the shared `/api/intent/init` and `/api/intent/stream` endpoints used by the original multi-agent roundtable. Position roundtable identifies itself with `intent_agent_id: "position-roundtable-intent"`; this is a separately seeded built-in agent, not an alias of `roundtable-intent`. It preserves the same thread, SSE streaming, and file-reading lifecycle as the generic Step-1 agent, but has its own **at-most-five user-assistance-round** brake. A round is one AI turn plus the user reply, not one tool call: the dedicated agent may emit several independent `ask_clarification` calls in the same turn, which the frontend renders as several separate selection cards and submits together. A topic-only request is not considered resolved: the agent asks for every independent missing decision (such as scope, decision use, or task constraints) as separate cards. For the dedicated agent, a fresh `ask_clarification` emitted in a stream is a strict user-input pause and wins over any same-run premature `[INTENT_READY]`; a historical clarification from the prior turn does not block the user answer from completing. Its final `[INTENT_READY]` JSON contains four string arrays: `coreGoals`, `riskWarnings`, `keyPoints`, and `strategicSignificance`; it must contain completed conclusions, not “待确认” or other unresolved placeholders. The multi-agent endpoint remains backwards compatible when this optional field is omitted and continues to emit `objective`, `constraints`, and `assumptions`. `/api/intent/init` creates its empty thread directly through the in-process thread store/checkpointer, matching multi-agent initialization and avoiding a deployment-sensitive `127.0.0.1:` loopback. If initial checkpoint creation fails or is cancelled, bootstrap removes both the partial checkpoint and its thread metadata row. Thread metadata creation issues no post-commit ORM read-back on any code path: all returned fields are assigned before commit, and skipping the extra read keeps a MySQL read/write-splitting endpoint from sending it to a lagging replica (which fails the request with `Could not refresh instance`). Only the recovery snapshot layer is position-specific. Deployments must expose either `/api/position-roundtable/*` or the compatibility mirror `/api/multi-agent/position-roundtable/*`; updated clients probe both and only retry when the first response is the framework/proxy-level `404 Not Found`. ## Position-roundtable role directory The position-roundtable work views are persisted separately from organisation positions/RBAC in `position_roles`. Gateway startup idempotently inserts the four built-in ids (testing analysis, organisation test, solution design and comprehensive test), but only when an id is missing: administrator changes to the name, order, enabled state or role type are never overwritten. `GET /api/position-roles` is consumed by the workbench and `PUT /api/position-roles` is administrator-only. Exactly one enabled `intent` role is required. It is the initial intent-identification workspace and owns the summary/action-plan closing agents. Built-in rows retain stable ids so historical chain nodes remain readable; they can be edited or disabled but not deleted. Custom roles may be freely added or removed. ## Roundtable artifact submission status The task-scoped roundtable workbench exposes a lightweight submission-status API for generated deliverables: - `GET /api/roundtable/tasks/{taskId}/artifacts?submitted=true` returns the submitted artifact references for a task (or `items: []`). - `PUT /api/roundtable/tasks/{taskId}/artifacts/submission` records `submitted: true` or cancels it with `submitted: false`. Both routes use the normal Gateway `Authorization: Bearer ` middleware. The record is shared by `taskId`; the authenticated user is retained only in audit columns. Its server-owned idempotency key is `taskId + sourceKey + path + revision`. Repeating the same submitted payload returns the existing record; cancellation updates its state and timestamp without deleting the audit row. DeerFlow stores only the artifact reference (`threadId` + sandbox `path`, plus the optional session/node/name/MIME fields), never the Markdown body and never a task-system delivery. Continue to preview the file through the existing thread-artifact endpoint.