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.
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
`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 the local TaskCOP Vite origins http://localhost:8080 and http://127.0.0.1:8080, including when GATEWAY_CORS_ORIGINS is set for another frontend. scripts/env.sh also includes them in its startup default. For a deployment, set GATEWAY_CORS_ORIGINS to the exact additional frontend origins that are allowed to call the Gateway.

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.

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。

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 package manager
API keys for your chosen LLM provider

Installation

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:

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:

export OPENAI_API_KEY="your-api-key-here"

Running

Full Application (from project root):

make dev  # Starts LangGraph + Gateway + Frontend + Nginx

Access at: http://localhost:2026

Backend Only (from backend directory):

# 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 TaskCOP/Vite development origins http://localhost:8080 and http://127.0.0.1:8080 with credentials. 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. Explicit origins are used in addition to the local TaskCOP development origins.

Extensions Configuration (`extensions_config.json`)

MCP servers and skill states in a single file:

{
  "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 integration for observability. When enabled, all LLM calls, agent runs, tool executions, and middleware processing are traced and visible in the LangSmith dashboard.

Setup:

Sign up at smith.langchain.com and create a project.
Add the following to your .env file in the project root:

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 observability for LangChain-compatible runs.

Add the following to your .env file:

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

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

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

License

See the LICENSE file in the project root.

Contributing

See 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:<gateway-port> 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 <token> 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.

README.md

DeerFlow Backend

Architecture

Core Components

Lead Agent

Middleware Chain

Sandbox System

Subagent System

Memory System

Tool Ecosystem

Configurable HTTP web search

Gateway API

Custom Agents

AI Writing Intranet Retrieval

IM Channels

Quick Start

Prerequisites

Installation

Configuration

Running

Project Structure

Configuration

Main Configuration (config.yaml)

Browser CORS

Extensions Configuration (extensions_config.json)

Environment Variables

LangSmith Tracing

Langfuse Tracing

Dual Provider Behavior

Development

Commands

Code Style

Testing

Runtime appearance themes

Technology Stack

Documentation

License

Contributing

Position roundtable durable history

Position-roundtable role directory

Roundtable artifact submission status

Main Configuration (`config.yaml`)

Extensions Configuration (`extensions_config.json`)