An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.

🦌 DeerFlow - 2.0

On February 28th, 2026, DeerFlow claimed the 🏆 #1 spot on GitHub Trending following the launch of version 2. Thanks a million to our incredible community — you made this happen! 💪🔥

DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to do almost anything — powered by extensible skills.

https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18

[!NOTE] DeerFlow 2.0 is a ground-up rewrite. It shares no code with v1. If you're looking for the original Deep Research framework, it's maintained on the 1.x branch — contributions there are still welcome. Active development has moved to 2.0.

Official Website

Learn more and see real demos on our official website. The landing-page case studies open as allowlisted, read-only showcases without requiring a sign-in.

Sister Projects

LLM Space - Meet our secret weapon behind DeerFlow — one desktop tool to prototype agent ideas, inspect each harness step, replay failures, and benchmark performance.

Coding Plan from ByteDance Volcengine

We strongly recommend using Doubao-Seed-2.0-Code, DeepSeek v3.2 and Kimi 2.5 to run DeerFlow
Learn more
中国大陆地区的开发者请点击这里

InfoQuest

InfoQuest reader, web search, and image search use a 30-second HTTP connect/read inactivity timeout. The crawl timeout and navigation_timeout settings remain separate server-side options; they do not control the local HTTP timeout.

DeerFlow has newly integrated the intelligent search and crawling toolset independently developed by BytePlus — InfoQuest (supports free online experience)

🦌 DeerFlow - 2.0

One-Line Agent Setup

If you use Claude Code, Codex, Cursor, Windsurf, or another coding agent, you can hand it the setup instructions in one sentence:

Help me clone DeerFlow if needed, then bootstrap it for local development by following https://raw.githubusercontent.com/bytedance/deer-flow/main/Install.md

That prompt is intended for coding agents. It tells the agent to clone the repo if needed, choose Docker when available, and stop with the exact next command plus any missing config the user still needs to provide.

Quick Start

Configuration

Operators can extend lead-agent, subagent, and DeerMem extraction prompts with literal prepend/append configuration without editing source templates. See prompt overlays.

Optional per-model request_admission paces requests to help stay within provider request-per-minute limits. It is disabled by default; see the linked guide to enable it.

For Google's official Gemini OpenAI-compatible endpoint, use the Gemini reasoning profile.

Clone the DeerFlow repository

git clone https://github.com/bytedance/deer-flow.git
cd deer-flow

Run the setup wizard

From the project root directory (deer-flow/), run:
```
make setup
```
This launches an interactive wizard that guides you through choosing an LLM provider, optional web search, and execution/safety preferences such as sandbox mode, bash access, and file-write tools. It generates a minimal config.yaml and writes your keys to .env. Takes about 2 minutes.

The wizard also lets you configure an optional web search provider, or skip it for now.

Jina, Browserless, and InfoQuest web fetches resolve relative links and image sources using the requested page URL (or a usable HTML base URL), so returned Markdown includes complete destinations. Link resolution preserves the surrounding HTML source, including malformed-page formatting.

Jina fetches support opt-in bounded retries via max_retries (default 0) and retry_budget_seconds (default 30) in the tool configuration. Retry waits are randomized within the time budget. Retries may increase upstream requests and cost; see Jina fetch retries.

Run make doctor at any time to verify your setup and get actionable fix hints. If you are opening a GitHub issue about a local setup or runtime problem, run make support-bundle. The command prints reporter next steps, writes a *-issue-summary.md file to paste into the issue, a *-issue-draft.md file for AI-assisted issue filing, and an optional evidence zip under .deer-flow/support-bundles/. If an AI assistant files the issue, start from the draft and replace every REQUIRED placeholder instead of inventing missing facts. Attach the zip only if a maintainer asks for it, or if the summary alone is not enough. Maintainers and AI triage tools can start with triage.json; the bundle includes redacted diagnostics and file manifests only, and does not include .env, raw conversation messages, or user file contents.

Advanced / manual configuration: If you prefer to edit config.yaml directly, run make config instead to copy the full template. Optional dependency auto-detection accepts UTF-8 configuration files with or without a byte-order mark (BOM). See config.example.yaml for the complete reference including CLI-backed providers (Codex CLI, Claude Code OAuth), OpenRouter, Responses API, subagent runtime caps such as subagents.max_total_per_run, and more.

Optional per-model pricing must use one currency across all priced models. DeerFlow disables Console cost estimates when currencies are mixed rather than presenting an invalid aggregate.

Administrators can also open Settings → Models to add, edit, test, and enable/disable shared OpenAI-compatible Chat Completions models without editing config.yaml. Enter a unique name, base URL, model ID, and optional API key; saving refreshes the chat model list. Connection testing sends a short streaming tool-call request and may incur provider charges. It does not save the draft or verify image support; set image support and token limits from provider documentation. Official DeepSeek models at https://api.deepseek.com or https://api.deepseek.com/v1 (default HTTPS port) automatically use DeerFlow's DeepSeek adapter, preserving reasoning content across tool calls and honoring output token limits. Chat uses the selected thinking mode; the connection test temporarily disables thinking because DeepSeek rejects forced tool selection in thinking mode. The test checks streaming tool connectivity, not every agent workflow or thinking-mode behavior. Existing saved DeepSeek profiles receive this adapter without re-entering credentials. DeepSeek-specific settings for third-party proxies, other native adapters, and advanced reasoning settings remain YAML-configured.

DeepSeek regression tests run offline with the normal backend suite. To verify the real provider explicitly, set DEEPSEEK_TEST_API_KEY in your environment and run from backend/:
```
DEER_FLOW_RUN_LIVE_TESTS=1 uv run --no-sync pytest tests/test_managed_deepseek_live.py -q
```
These opt-in tests send short requests to DeepSeek and may incur charges; they use temporary state, never save credentials to the deployment catalog, and are skipped in CI. DEEPSEEK_TEST_MODEL optionally selects a different DeepSeek model ID (default: deepseek-flash). The same tests can be run on unfixed and fixed revisions; success is always the expected result.

YAML models remain read-only in this page and take precedence on name conflicts. Managed models are appended after YAML models; edits apply to new configuration snapshots, while active runs retain their existing snapshot. Disabling a model removes it from future selection/resolution, so update any custom-agent or scheduled task definitions that explicitly reference it before disabling it. Managed models are shared by the deployment, not personal API-key profiles, and remain subject to the existing model authorization policy.

The encrypted catalog and a generated local encryption key are stored in $DEER_FLOW_HOME/managed-models/ (default .deer-flow/managed-models/). Persist and back up the whole directory, restrict filesystem access, and share it across Gateway workers/replicas that should use the same catalog. The local key is protected by filesystem permissions; encryption does not protect against someone who can read both files. Losing the key requires restoring the backup. Reads and writes fail if the catalog cannot be decrypted, rather than replacing it. This storage is independent of the SQL backend and works with read-only YAML mounts.

When several models are configured, open either model picker and use the star beside a model to favorite it. Favorites appear first in both the main chat and Side Chat pickers without changing either chat's selected or default model. They are stored for the signed-in user in the current browser, so they do not sync to another browser or device and do not require a startup setting. The compact favorites picker intentionally omits search and only adds favorite ordering to the two-line model list.

Manual model configuration examples

models:
  - name: gpt-4o
    display_name: GPT-4o
    use: langchain_openai:ChatOpenAI
    model: gpt-4o
    api_key: $OPENAI_API_KEY

  - name: openrouter-gemini-2.5-flash
    display_name: Gemini 2.5 Flash (OpenRouter)
    use: langchain_openai:ChatOpenAI
    model: google/gemini-2.5-flash-preview
    api_key: $OPENROUTER_API_KEY
    base_url: https://openrouter.ai/api/v1

  - 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

  - name: qwen3-32b-vllm
    display_name: Qwen3 32B (vLLM)
    use: deerflow.models.vllm_provider:VllmChatModel
    model: Qwen/Qwen3-32B
    api_key: $VLLM_API_KEY
    base_url: http://localhost:8000/v1
    supports_thinking: true
    when_thinking_enabled:
      extra_body:
        chat_template_kwargs:
          enable_thinking: true

OpenRouter and similar OpenAI-compatible gateways should be configured with langchain_openai:ChatOpenAI plus base_url. If you prefer a provider-specific environment variable name, point api_key at that variable explicitly (for example api_key: $OPENROUTER_API_KEY).

To route OpenAI models through /v1/responses, keep using langchain_openai:ChatOpenAI and set use_responses_api: true with output_version: responses/v1.

Models whose provider contract differs from DeerFlow's generic thinking/effort assumptions can declare a per-model mapping-valued reasoning: block (thinking unsupported/optional/required, the accepted effort values with aliases and a default, the payload dialect, and the reasoning-history requirement). The setup wizard's Z.AI GLM-5.3-Flash profile uses it: thinking stays on for every foreground and background call, and the effort selector offers the model's own low/high/max levels. Ollama's existing boolean reasoning: true remains a native provider setting and is forwarded to ChatOllama. When migrating a profile to a custom effort path, remove any old reasoning_effort setting from the profile and thinking templates; configuration validation rejects the leftover key. The chat UI drops a remembered provider-specific effort when switching to a legacy model that does not advertise it. Profiles without the block keep their existing provider behavior. See config.example.yaml for the shape and the equivalent manual configuration.

For vLLM 0.19.0, use deerflow.models.vllm_provider:VllmChatModel. For Qwen-style reasoning models, DeerFlow toggles reasoning with extra_body.chat_template_kwargs.enable_thinking and preserves vLLM's non-standard reasoning field across multi-turn tool-call conversations. Legacy thinking configs are normalized automatically for backward compatibility. If the endpoint reports a cumulative usage snapshot on every streaming chunk, set cumulative_stream_usage: true so DeerFlow converts those snapshots into per-chunk deltas; the option is disabled by default and leaves usage unchanged when a stable completion id is unavailable. Reasoning models may also require the server to be started with --reasoning-parser .... If your local vLLM deployment accepts any non-empty API key, you can still set VLLM_API_KEY to a placeholder value.

When prompt caching is enabled for a model configured with deerflow.models.claude_provider:ClaudeChatModel, DeerFlow preserves thinking and redacted-thinking history without placing cache breakpoints directly on those blocks. Extended-thinking tool follow-ups can keep using prompt caching.

CLI-backed provider examples:

models:
  - name: gpt-5.4
    display_name: GPT-5.4 (Codex CLI)
    use: deerflow.models.openai_codex_provider:CodexChatModel
    model: gpt-5.4
    supports_thinking: true
    supports_reasoning_effort: true

  - name: claude-sonnet-4.6
    display_name: Claude Sonnet 4.6 (Claude Code OAuth)
    use: deerflow.models.claude_provider:ClaudeChatModel
    model: claude-sonnet-4-6
    max_tokens: 4096
    supports_thinking: true

Codex CLI reads ~/.codex/auth.json
Completed Codex responses still return their text and tool calls when token usage is null, omitted, or empty; usage metadata remains unavailable.
The Codex model provider returns completed responses without waiting for the SSE connection to close. Failed or incomplete responses report the provider's error or reason; partial output is not returned as a successful answer. Non-object error details are reported as text.
Claude Code accepts CLAUDE_CODE_OAUTH_TOKEN, ANTHROPIC_AUTH_TOKEN, CLAUDE_CODE_CREDENTIALS_PATH, or ~/.claude/.credentials.json
CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR accepts a UTF-8 token handoff and reuses it for later model instances in the same process. Undecodable handoffs are skipped so Claude Code can still try its override or default credentials file.
CLI credential JSON files accept UTF-8 with or without a BOM, independently of the host locale. make doctor accepts the same files when checking CLI authentication. Invalid text encoding is treated as an unreadable source; Claude Code can still try its default file after an invalid override.
ACP agent entries are separate from model providers — if you configure acp_agents.codex, point it at a Codex ACP adapter such as npx -y @zed-industries/codex-acp
Each ACP agent's timeout_seconds (default: 1800) is one shared budget for initialization, session creation, and the prompt, starting after the subprocess launches. On timeout, DeerFlow aborts the invocation and closes the subprocess before returning an error. Workspace/MCP preparation and subprocess cleanup are outside this budget. A TimeoutError raised by the SDK before this deadline expires retains its own error message.
MiniMax Code speaks ACP directly. Install and authenticate it, then add it as an ACP agent:

npm install --global @minimax-ai/code
mcode login

acp_agents:
  mcode:
    command: mcode
    args: ["acp"]
    description: MiniMax Code for implementation, refactoring, debugging, and repository tasks
    auto_approve_permissions: false

mcode must be on the Gateway process's PATH; installing it only on the Docker host does not make it available inside the Gateway container. DeerFlow invokes it through invoke_acp_agent in a per-thread ACP workspace and forwards enabled MCP servers. Keep auto_approve_permissions: false for untrusted tasks; enable it only when mcode must edit files or run commands and you trust the task.

On macOS, export Claude Code auth explicitly if needed:

eval "$(python3 scripts/export_claude_code_oauth.py --print-export)"

The exporter rejects malformed credential containers and non-string or blank access tokens before printing a token, emitting a shell export, or writing a credentials file. Valid tokens are exported unchanged, and file exports preserve the full credential container.

API keys can also be set manually in .env (recommended) or exported in your shell:

OPENAI_API_KEY=your-openai-api-key
TAVILY_API_KEY=your-tavily-api-key

For an explicit backend dotenv file, export DEER_FLOW_ENV_FILE before startup, alongside DEER_FLOW_CONFIG_PATH if needed. For example, from backend/:

DEER_FLOW_ENV_FILE=/srv/deer-flow/stage.env DEER_FLOW_CONFIG_PATH=/srv/deer-flow/stage.yaml make gateway

Relative dotenv paths use the backend process working directory; absolute paths work regardless of that directory. Existing process variables win. An unset selector preserves default dotenv discovery; a specified empty, missing, non-file or unreadable path fails startup. Explicit selection also fails when PYTHON_DOTENV_DISABLED disables dotenv loading. Restart after changing the file. This selects backend dotenv input only: shell launchers, Docker Compose and the frontend retain their own environment loading. Values they already export win. It does not select ENV profiles or isolate databases, storage or tenants. See backend dotenv selection.

Running the Application

Deployment Sizing

Use the table below as a practical starting point when choosing how to run DeerFlow:

Deployment target	Starting point	Recommended	Notes
Local evaluation / `make dev`	4 vCPU, 8 GB RAM, 20 GB free SSD	8 vCPU, 16 GB RAM	Good for one developer or one light session with hosted model APIs. `2 vCPU / 4 GB` is usually not enough.
Docker development / `make docker-start`	4 vCPU, 8 GB RAM, 25 GB free SSD	8 vCPU, 16 GB RAM	Image builds, bind mounts, and sandbox containers need more headroom than pure local dev.
Long-running server / `make up`	8 vCPU, 16 GB RAM, 40 GB free SSD	16 vCPU, 32 GB RAM	Preferred for shared use, multi-agent runs, report generation, or heavier sandbox workloads.

These numbers cover DeerFlow itself. If you also host a local LLM, size that service separately.
Linux plus Docker is the recommended deployment target for a persistent server. macOS and Windows are best treated as development or evaluation environments.
If CPU or memory usage stays pinned, reduce concurrent runs first, then move to the next sizing tier.

Option 1: Docker (Recommended)

Requires Docker Desktop / Docker Engine and Docker Compose v2.24+ (docker compose version). Older Compose clients cannot parse the optional env_file syntax in docker/docker-compose-dev.yaml.

Development (hot-reload, source mounts):

make docker-init    # Pull sandbox image (only once or when image updates)
make docker-start   # Start services (auto-detects sandbox mode from config.yaml)
make docker-logs    # View logs

make docker-start starts provisioner only when config.yaml uses provisioner mode (sandbox.use: deerflow.community.aio_sandbox:AioSandboxProvider with provisioner_url).

Docker builds use the upstream uv registry by default. If you need faster mirrors in restricted networks, export UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple and NPM_REGISTRY=https://registry.npmmirror.com before running make docker-init or make docker-start.

Local AIO sandbox control traffic is always direct: loopback/private addresses, single-label cluster hosts, and Docker/Podman internal hostnames do not inherit HTTP_PROXY or HTTPS_PROXY. External sandbox FQDNs and public IPs still honor environment proxy settings.

Backend processes automatically pick up config.yaml changes on the next config access, so model metadata updates do not require a manual restart during development.

Gateway runs use the top-level recursion_limit in config.yaml when an API request does not provide one. The default is 100; valid per-request values take precedence, and max_recursion_limit (default 1000) caps both. Changes apply to the next run without restarting the Gateway. This top-level setting applies to Gateway API runs; IM channel and embedded DeerFlowClient runs retain their own defaults and per-call override paths. The checkpoint storage settings database.checkpoint_channel_mode and database.checkpoint_delta.snapshot_frequency (default 10) are exceptions: both are frozen when the process first builds an agent (including through DeerFlowClient) and require a process restart to change safely.

The optional database.checkpoint_cache section (delta channel mode only) caches materialized checkpoint histories: type is memory (default) or redis, and max_entries: 0 disables the cache. The redis backend is Gateway/async-only; the sync TUI/embedded path supports memory only. The cache is performance-only — results are identical with it disabled — so it is never frozen and workers sharing one checkpoint database may safely run different cache settings.

[!TIP] On Linux, if Docker-based commands fail with permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock, add your user to the docker group and re-login before retrying. See CONTRIBUTING.md for the full fix.

Production (builds images locally, mounts runtime config and data):

make up     # Build images and start all production services
make down   # Stop and remove containers

Access: http://localhost:2026

make up waits for the Gateway /health endpoint before reporting success. If the Gateway does not become healthy within the startup window, deployment exits non-zero and prints the container status plus recent Gateway logs. The production image starts from its already-built environment and never resolves or installs Python dependencies at container startup.

For persistent deployments, configure database.backend as sqlite or postgres. The selected backend is shared by the LangGraph checkpointer, LangGraph Store, and DeerFlow application data. The deprecated checkpointer section, when present, overrides the first two for backward compatibility.

Gateway startup automatically repairs the missing run-change schema affecting some existing databases (#5516). The repair preserves run history and existing change positions; downgrading the repair to its predecessor also retains the schema and positions required by that version.

For lightweight single-process event persistence, run_events.backend: jsonl keeps Unicode message content intact, including line and paragraph separators. Existing valid JSONL records remain readable without rewriting the files.

For per-call usage audits, an immediate LLM response replay with populated usage updates both nested usage and top-level token counters, even if initial usage was absent or zero. The original request, model, and status metadata stay intact; flushed events are not rewritten. See the run event contract.

The unified nginx endpoint is same-origin by default and does not emit browser CORS headers. If you run a split-origin or port-forwarded browser client, set GATEWAY_CORS_ORIGINS to comma-separated exact origins such as http://localhost:3000; the Gateway then applies the CORS allowlist and matching CSRF origin checks.

When fine-grained authorization is enabled, Live Browser connections require threads:write as well as ownership of the thread, even when only viewing frames: the same connection can control the browser. Permission checks run when connecting. Restart Gateway after upgrading to disconnect sessions admitted by older code.

Browser login uses HttpOnly session cookies. The login page offers a "keep me signed in" option that extends the browser session when the request is HTTPS (including trusted X-Forwarded-Proto: https) or localhost HTTP. The localhost exception uses the direct request Host and ignores forwarded host headers. Public HTTP deployments, including many temporary sandbox URLs, fall back to session cookies by default. DeerFlow never stores the password in browser storage; the UI may remember only the email address.

DeerFlow still uses Forwarded / X-Forwarded-* headers to recover the browser-facing scheme and origin behind a proxy. The bundled nginx sets X-Forwarded-Proto, but preserves an upstream HTTPS value and does not overwrite every forwarded header. Configure the outer trusted proxy to replace or strip client-supplied forwarding headers before traffic reaches DeerFlow.

[!IMPORTANT] The Gateway still owns active run tasks in process, so production defaults to a single Gateway worker (GATEWAY_WORKERS=1). Multi-worker deployments require Postgres, the Redis stream bridge (stream_bridge.type: redis), run_ownership.heartbeat_enabled: true, and run_events.backend: db; process-local memory/JSONL event stores cannot enforce singleton delivery receipts across workers. Kubernetes replicas run one worker per Pod, which the worker count cannot see: declare them with deployment.multi_instance: true (or DEER_FLOW_MULTI_INSTANCE=1, which deploy tooling such as a Helm chart can set from its replica count) so the startup gate enforces the same prerequisites instead of staying inert. The bridge shares SSE delivery and bounded Last-Event-ID replay across workers. When a valid reconnect cursor has been trimmed, or a subscriber that already established an empty-stream wait falls behind before its first delivery, Memory and Redis emit a machine-readable SSE gap event instead of silently returning a partial replay; the Web UI reloads durable thread/event state and resumes from the retained tail. Lease reconciliation marks runs from dead workers as errors, persists their delivery receipts, publishes the terminal stream marker, schedules retained-stream cleanup, and updates the affected thread status. SSE, /wait, and internal stream consumers use stream_bridge.heartbeat_interval_seconds (default 15) for idle liveness checks; changing it requires a Gateway restart. Malformed Redis reconnect IDs live-tail new events instead of replaying the retained buffer, and the rolling retained-buffer TTL (stream_ttl_seconds) remains a cleanup safety net rather than a run timeout. IM channel state and other process-local services still need their own multi-worker coordination.

In single-process JSONL deployments, cancelling an admitted event-store mutation waits for its background file I/O, rollback, and bookkeeping to settle before releasing the thread write lock. This prevents an older cancelled write from recreating deleted records or rolling back a later successful write. Cancellation can therefore wait on slow storage; it does not stop an in-flight filesystem operation. Callers still waiting to acquire the lock can cancel without starting a mutation. A batch spanning multiple threads drains its current thread group before propagating cancellation; subsequent thread groups do not start.

After a run publishes its terminal stream marker, its process-local RunRecord remains available for the existing five-minute grace period before cleanup; durable run history remains available through RunStore, while the stream bridge retains its delivery tail on its separate cleanup schedule.

Run cancellation may land on any Gateway worker. A non-owning worker now persists the interrupt or rollback request for the live owner, which observes it during lease renewal and performs the normal cancellation flow; load-balancer routing alone no longer produces a 409. The first accepted action wins even if a retry lands on the owner, and accepted cancellation competes atomically with owner completion. Dead owners still follow lease takeover and orphan recovery. Cancellation latency is therefore bounded by the lease heartbeat interval.

Cancelling a model recovery probe, including while it is queued or waiting to retry, lets the next call check whether the provider has recovered. Cancellation does not count as a provider failure or release another call's active recovery probe.

With lease heartbeat enabled, a transient RunStore renewal error is retried only until the last confirmed lease expires; the stale worker then cancels local execution and suppresses checkpoint, completion-hook, delivery-receipt, and thread-status finalization. A remote tool side effect already in flight may still be outside local cancellation.

Reconciliation uses an atomic takeover claim that re-checks the lease after candidate selection, so a successful owner renewal wins over orphan recovery and only one reconciler can report a run as recovered. When multiple Gateway workers share the Docker/AIO or E2B sandbox backend, also configure sandbox.ownership.type: redis; E2B uses the leases during background startup and periodic reconciliation so duplicate/orphan cleanup cannot terminate a live peer's sandbox.

See CONTRIBUTING.md for detailed Docker development guide.

Upgrading an existing checkout

Keep config.yaml, .env, and extensions_config.json. Stop the services you currently use, run git pull --ff-only, then start the same mode again. Do not run make config or make docker-init again for a routine source upgrade. If the new version requires configuration changes, run make config-upgrade before restarting. See Operations and Troubleshooting for the commands for each mode.

Option 2: Local Development

If you prefer running services locally:

Prerequisite: complete the "Configuration" steps above first (make setup). make dev requires a valid config.yaml in the project root. Set DEER_FLOW_PROJECT_ROOT to define that root explicitly, or DEER_FLOW_CONFIG_PATH to point at a specific config file. Runtime state defaults to .deer-flow under the project root and can be moved with DEER_FLOW_HOME; skills default to skills/ under the project root and can be moved with DEER_FLOW_SKILLS_PATH. Run make doctor to verify your setup before starting. On Windows, run the local development flow from Git Bash. Native cmd.exe and PowerShell shells are not supported for the bash-based service scripts, and WSL is not guaranteed because some scripts rely on Git for Windows utilities such as cygpath.

The documented root make commands invoke repository .sh files through Bash explicitly. They therefore continue to work from source archives or filesystems that do not preserve POSIX executable bits. When calling a script directly from such a checkout, use bash ./scripts/.sh ....

Check prerequisites:
```
make check  # Verifies Node.js 22+, pnpm, uv, nginx
```
The local make check, make install, make dev, and make start entry points use a direct pnpm executable when available and otherwise fall back to corepack pnpm. With native Windows Python, the shared runner checks pnpm.cmd before the generic pnpm lookup, which follows PATH/PATHEXT and may select an .exe or .bat in the same or an earlier PATH directory. The Corepack fallback likewise checks corepack.cmd before corepack. POSIX Python keeps the generic names first, including when running under MSYS/Cygwin. The runner and diagnostics resolve repository paths absolutely, so these checks work regardless of the caller's current directory. Corepack runs from frontend/, so it honors the packageManager version pinned in frontend/package.json; enabling a global pnpm shim is not required.
Install dependencies:
```
make install  # Install backend + frontend dependencies + pre-commit hooks
```
Hook setup calls pre-commit through uv, so uv's tool directory need not be on PATH.
(Optional) Pre-pull sandbox image:
```
# Recommended if using Docker/Container-based sandbox
make setup-sandbox
```
Reads the configured sandbox image from UTF-8 config.yaml, with or without a leading BOM, using LF or CRLF line endings. On macOS, a successful Apple Container pull completes this step even when Docker is not installed. If Docker is available, its image is also pulled.
Start services:
```
make dev
```
Access: http://localhost:2026
(Optional) Load sample memory data for local review: open Settings > Memory, click Import memory, and select backend/docs/memory-settings-sample.json. The browser imports into the signed-in user's memory.

To replace memory for every registered user in a disposable review environment:
```
cd backend
uv run python ../scripts/load_memory_sample.py --all-users
```
Bulk mode supports SQLite/PostgreSQL user registries, creates timestamped backups under .deer-flow/memory-sample-backups/, and rejects the non-persistent database.backend: memory mode. See backend/docs/MEMORY_SETTINGS_REVIEW.md for the complete review flow.

Local services always use their internal ports (8001, 3000, and 2026). The root .env variable PORT configures only the published Docker ingress; it does not change the Next.js port used by make dev.

Startup Modes

DeerFlow runs the agent runtime inside the Gateway API. Development mode enables hot-reload; production mode uses a pre-built frontend.

	Local Foreground	Local Daemon
Dev	`./scripts/serve.sh --dev`
`make dev`	`./scripts/serve.sh --dev --daemon`
`make dev-daemon`	`./scripts/docker.sh start`
`make docker-start`	—
Prod	`./scripts/serve.sh --prod`
`make start`	`./scripts/serve.sh --prod --daemon`
`make start-daemon`	—	`./scripts/deploy.sh`
`make up`

Action	Local	Docker Dev	Docker Prod
Stop	`./scripts/serve.sh --stop`
`make stop`	`./scripts/docker.sh stop`
`make docker-stop`	`./scripts/deploy.sh down`
`make down`
Restart	`./scripts/serve.sh --restart [flags]`	`./scripts/docker.sh restart`	—

make start and make start-daemon rebuild the frontend with next build on every run. To reuse the last build instead, pass SKIP_FRONTEND_BUILD=1 (or add --skip-frontend-build when calling ./scripts/serve.sh --prod directly). This is opt-in: it fails fast when frontend/.next has no completed build.

Gateway owns /api/langgraph/* and translates those public LangGraph-compatible paths to its native /api/* routers behind nginx.

Cold agent imports during run creation use a dedicated worker pool, keeping unrelated Gateway requests responsive while the agent stack loads. A failed factory import prevents the run from being admitted.

For a read-only demo without the Gateway, run make build-static from frontend/, then HOSTNAME=127.0.0.1 PORT=3000 node --env-file=.env .next/standalone/server.js from the same directory. The build includes public demo assets and resolves supported demo API reads locally; writes are unavailable. To display the homepage GitHub star count, set GITHUB_OAUTH_TOKEN in frontend/.env before starting Node. The token stays on the server; missing credentials or GitHub failures hide the count. Restart Node after changing the token; no rebuild is needed.

LangGraph Studio (Optional)

The default make dev topology uses DeerFlow's Gateway-embedded runtime and does not require LangGraph Studio. To inspect and test the registered lead-agent graph with the standalone development server, run the command from backend/ so the CLI discovers langgraph.json:

cd backend
uv run langgraph dev --allow-blocking

The command prints the local API and Studio UI URLs. This in-memory server is for development and testing only. The flag permits DeerFlow's synchronous configuration and graph-factory setup during local Studio requests; it must not be treated as a production-server setting. Local Studio authentication is handled automatically, so the connection does not require custom headers. Use DeerFlow's documented production startup modes or a supported LangSmith deployment for production workloads. Assistant ownership and provenance in this standalone mode are server-owned: Studio can discover registered graphs and the assistants it creates, and normal assistant-version selection remains available. Before the locked local runtime loads its persisted development store, DeerFlow repairs legacy assistant rows and version history so historical client metadata cannot restore server privileges or be discarded by the runtime's startup cleanup. Keep the backend dependencies synchronized with uv sync; this compatibility path requires the declared LangGraph runtime versions and logs a warning if the persisted-store contract no longer matches its expectations. The documented command uses LangGraph's file-based custom-app loader, which is also covered directly by DeerFlow's regression tests.

Standalone runs using if_not_exists="create" retain config and run metadata on the newly created thread, including searchable tags; run metadata takes precedence for duplicate keys. Thread ownership and MCP incarnation remain server-owned, and later runs do not replace the thread's creation metadata.

For workflows that invoke backend/langgraph.json through LangGraph Studio or a direct LangGraph Server, DeerFlow consumes the authenticated identity published by that runtime and uses it for custom-agent configuration/SOUL, user skills and skill policy, uploads, thread data, and memory reads/writes. This keeps authenticated runs out of the shared default filesystem bucket, and the server-owned identity takes precedence over ordinary client-supplied user_id values. External identities such as email addresses are mapped to stable, collision-resistant directory-safe user IDs before accessing DeerFlow storage. The default DeerFlow service topology remains the Gateway-embedded runtime described above.

Gateway runs automatically enforce native delivery for artifacts created or modified under /mnt/user-data/outputs: present_files must present at least one output produced by the current run, and the terminal run.delivery receipt must be durably recorded. Virtual artifact paths are resolved within the same authenticated user and thread scope that produced the output before the output-directory boundary is validated. Runs that do not produce output artifacts keep ordinary conversational behavior.

Thread-scoped runs.wait() calls that finish with status: error report the current run error instead of an earlier answer. The asynchronous Python LangGraph SDK raises by default; pass raise_error=False to inspect the returned status and error.

DeerFlow's built-in custom events are available through both LangGraph streaming interfaces: native clients can continue subscribing to stream_mode="custom", while callback-based integrations can consume the same payloads as on_custom_event records from astream_events(version="v2"). The callback event name matches the payload's type field.

Docker Production Deployment

./scripts/deploy.sh supports building and starting separately:

# One-step (build + start)
./scripts/deploy.sh

# Two-step (build once, start later)
./scripts/deploy.sh build       # build all images
./scripts/deploy.sh start       # start pre-built images

# Stop
./scripts/deploy.sh down

Advanced

Sandbox Mode

DeerFlow supports multiple sandbox execution modes:

Local Execution (runs sandbox code directly on the host machine)
Docker Execution (runs sandbox code in isolated Docker containers)
Docker Execution with Kubernetes (runs sandbox code in Kubernetes pods via provisioner service)

Sandbox references in conversation state are server-owned. External run and thread-state APIs reject caller-supplied sandbox values; when restoring a checkpoint, the runtime resolves the reference against the authenticated user and thread before a tool can reuse it. A missing runtime thread ID raises an error even when the referenced sandbox is cached.

When host Bash is enabled for Local Execution, DeerFlow starts OS detection with uname -s, then uses sw_vers on Darwin. On Linux, it reads host system files such as /etc/os-release only when the active sandbox policy permits it. Host filesystem path checks still apply; after a blocked path, the agent is directed to use a permitted command-only probe or virtual path instead of repeating the rejected command.

For Docker development, service startup follows config.yaml sandbox mode. In Local/Docker modes, provisioner is not started.

See the Sandbox Configuration Guide to configure your preferred mode.

Remote directory listings report traversal failures (for example, unreadable directories) as incomplete results, even when no entries were returned. A missing start path is reported separately as “Directory not found.”

The optional Tenki cloud sandbox provider uses Tenki SDK 1.4.0 or newer. Timed-out commands preserve partial output and report Exit Code: 124; unsuccessful health checks cannot reclaim a warm sandbox. Health probes tolerate login-shell output around the ok line, and failures log the sandbox ID and probe output before replacing the sandbox.

BoxLite shutdown rejects late VM registration and keeps its SDK loop open while in-flight acquisitions finish. If they cannot drain within five seconds, shutdown fails with resources still owned and can be retried.

MCP Server

In the chat UI, enable Token Usage → Debug to inspect generic/MCP tool calls. Each Tool details panel starts collapsed and shows the tool name, call ID, input, and received result or explicit error. Large previews are truncated; fields whose names exceed the remaining preview budget are omitted rather than renamed. Structured previews retain complete JSON syntax, including escaped strings and closing delimiters. Array previews stop when the text budget cannot display another element; literal ellipsis values are preserved. Consecutive generated markers at an array's end share one ellipsis indicating an omitted suffix; markers before later values retain their positions. Text results retain their original representation, including large numeric IDs and duplicate JSON keys, without reparsing. Text exceeding the limit is shown as a prefix with a truncation notice; structured objects and arrays are formatted separately. Copy actions copy only the displayed preview. This is a frontend view of data already received by the browser, without an additional secret-redaction layer.

In plan mode, malformed TODO statuses return normal tool-validation errors without aborting token attribution, so the agent can correct the call.

Tool-produced paths and URLs can be retained as short artifact handles across context compaction (tool_artifacts in config.yaml). Handles distinguish separate tool-result occurrences, even when a provider reuses call IDs. Detected file URLs preserve their query strings and fragments. When PII redaction is enabled, model-visible artifact labels follow that policy; internal references stay intact for tool argument resolution. The configured registry limit retains the newest artifacts, while checkpointed processing identities prevent evicted results from being recaptured after restart. Resolution runs before authorization and write-safety checks; unknown or expired handles return an error without executing the tool. Small unknown structured results may be retained as complete JSON up to 4096 UTF-8 bytes; empty or oversized payloads are skipped. Handles are agent-local: task arguments resolve parent handles to concrete references, and delegated reports must return concrete references rather than child-local handles. A truncated model projection reports how many handles are omitted.

DeerFlow supports configurable MCP servers and skills to extend its capabilities. For HTTP/SSE MCP servers, OAuth token flows are supported (client_credentials, refresh_token). Durable HTTP/SSE task status and cancellation calls select configured user_auth credentials using the persisted task owner, including after restart; per-request secrets are not retained for background calls. If a request-scoped credential overrides submit authentication, both credentials must authorize access to the same remote task. For stdio MCP servers, per-tool call timeouts can be configured with tool_call_timeout; durable background-task calls honor the same setting for HTTP/SSE servers as well. For stdio file outputs, a bare filename is linked to a uniquely matching file created or changed by that call. Filenames embedded in unrelated paths, including Windows backslash paths, are left intact. For HTTP/SSE background-task calls, session_init_timeout separately bounds connection setup (including the SSE endpoint event) and MCP initialization together; it stops applying once the tool call begins. Initialization deadline errors identify the server and configured time limit. Ordinary task subagents retain the parent run's captured thread incarnation for MCP calls, including legacy threads, so delegation preserves the same lifecycle scope. MCP tool names are prefixed with _ by default to prevent collisions across servers. If a server already namespaces its own tools, set tool_name_prefix: false on that server in extensions_config.json to keep the original names. Disable the prefix only when the resulting names remain unique across all enabled servers. Signed-in users' notification toggle, default model, conversation mode, and reasoning effort are saved to their account and restored on other browsers or after clearing browser storage. Browser notification permission still needs to be granted on each device. Changes retry after network failures; unsent changes survive a reload in the same tab. Concurrent edits to different fields are preserved; for the same field, the last server write wins. Existing unscoped browser preferences are not uploaded automatically because they have no account owner; reselect those settings once after upgrading. Static demos and auth-disabled development keep browser-local settings. Thread-specific model overrides and other display preferences remain local.

In a new chat, the submitted question stays above its streamed reasoning and tool steps while the server creates the conversation and confirms the message.

Capability Center groups plugins by office collaboration, documents and knowledge, search and research, business and data, and development and operations. The directory includes setup references alongside existing MCP configurations and Lark. Recommended integrations and built-in support do not imply an installed or verified connection; the Installed filter shows configured MCP entries and installed Lark only.

Personal MCP connections configured in the web interface are persisted per user. Deployment tools remain shared. Administrators can add, edit, enable, disable and delete shared MCP servers under Platform provided; ordinary users see their status without controls. Personal plugin switches affect only the signed-in user's connections. See connection ownership.

For plugin manifests, adapter registration, and Agent capability selection, see Capability Center integration contract.

DingTalk and WeCom group notifications and HubSpot CRM are bundled configurable plugins. Administrators supply robot credentials or a HubSpot private app token; Agents can then send requested group notifications, list companies, or create contacts. Saving configuration performs no external write. These plugins reuse the existing MCP lifecycle and require no separate plugin service. See the integration contract above for required fields, scopes, and feature boundaries.

Plugin brand icons are bundled locally. When adding or editing one personal MCP plugin, users can upload a PNG, JPG, or WebP image (up to 2 MB), preview it, or restore the default icon. Changes take effect only after Save; custom icons persist across browsers as a normalized 128px PNG in the server entry's display-only presentation.icon metadata. They are not sent to the MCP transport.

Capability Center > Plugins adds, replaces, and deletes one MCP server at a time through targeted mutations that preserve concurrent sibling changes; deletes use a bodyless URL-addressed request. An invalid stdio command on one server no longer blocks toggling another, while enabling that invalid server remains protected by the command allowlist and surfaces the backend validation message in the UI. Targeted updates accept both DeerFlow's type field and the MCP-spec transport field for SSE/HTTP servers. Runtime MCP and skill updates replace extensions_config.json atomically, so an interrupted write cannot leave the shared configuration truncated or partially written. The admin MCP cache reset advances a durable generation marker in the writable config directory. Every Gateway worker mounting that same directory retires its own cached tools and pooled sessions before the next lookup; replicas with independent filesystems are not implicitly covered. If no config path is available, the API reports a process-local reset instead. extensions_config.json accepts UTF-8 with or without a leading byte-order mark (BOM), including files saved as UTF-8 with BOM by an editor. MCP routing hints can also prefer a specific MCP tool for matching requests without forbidding other tools. When tool_search defers MCP schemas, matching routing metadata can auto-promote up to tool_search.auto_promote_top_k deferred schemas before the model call.

OpenViking users can register the official Streamable HTTP endpoint at /mcp with an owner-bound USER API key. The native forget tool is exposed for capability parity; deletion is irreversible, so it should be called only after explicit user confirmation. DeerFlow does not enforce that confirmation. This explicit, model-selected MCP tool path can run alongside the separate automatic OpenViking memory backend; it does not replace automatic turn capture or recall. See the OpenViking MCP tools configuration.

The Gateway can adapt an MCP server's ordinary submit / status / cancel tools into durable background tasks. The Agent sees only the configured submit tool and a DeerFlow-local task ID; remote IDs are persisted before the submit call returns, while status and cancel stay internal to the runtime. Polling uses cross-worker leases, exponential retry backoff, scoped MCP sessions, bounded result storage, and restart recovery. A status-tool isError is retained as a bounded diagnostic and retried; servers report a permanent remote-task outcome through a normal structured result with status: "failed". Remote poll hints are finite positive numbers capped at 24 hours, artifact-reference JSON is limited to 64 KiB, and task/server identifiers are validated against their durable SQL column limits before persistence. Input-required and terminal updates wake the current chat through idempotent Agent runs, while list_background_tasks and cancel_background_task let the Agent manage tasks without asking users for remote handles. Current-thread tasks are available through GET /api/threads/{thread_id}/mcp-tasks, its detail endpoint, and POST /api/threads/{thread_id}/mcp-tasks/{task_id}/cancel; when the task runtime actually starts, the Web UI exposes the same safe local view from the chat header with live status refresh, cancellation, and on-demand result, artifact, input-request, status-error, and cancellation-retry details. Default-disabled and memory-backend deployments hide that UI and do not poll the task endpoints. A failed remote cancellation remains queued with backoff, and its latest bounded error and attempt count stay visible in the expanded task card. Enable mcp_tasks in config.yaml, configure task_toolsets with exact raw tool names in extensions_config.json, and use a SQL database backend (sqlite or postgres). Task-enabled server connection, authentication, interceptor, timeout, or binding changes require a Gateway restart so Agent tool discovery and background calls cannot use different configuration versions. input_required is notification-only for now: DeerFlow can display the request but cannot yet submit the user's answer back to the remote task.

For deployment-level HTTP/SSE servers with task_toolsets, discovery, ordinary tools, and background task calls share OAuth token state within one Gateway process, including across tool-cache resets. Rotated refresh tokens stay in memory; they are not written back to configuration or shared across processes. After a restart, the configured refresh token must still be valid.

Notification launch and failed Agent-run deliveries use capped exponential backoff with a visible attempt count and stop after five failed attempts. When a bounded ordinary release exceeds its drain deadline, the service retains ownership until it settles. A permanently rejected target such as a deleted chat is dead-lettered immediately instead of retried forever or recreated. Cancellation endpoints return after durably recording the request; the background service owns the potentially slow remote MCP call and its retry schedule.

Notification runs keep their trusted delivery instruction separate from the framed, untrusted remote event payload. The process-started task runtime—not a hot config read—controls whether the task-management tools are exposed, so changing mcp_tasks requires a Gateway restart. When a skill's allowed-tools policy is active, list_background_tasks and cancel_background_task must be declared explicitly like other business tools. See the MCP Server Guide for detailed instructions.

Security: pass per-request MCP credentials only through config.context.secrets; credentials must never be placed in either run metadata surface (metadata.auth_token or config.metadata.auth_token). See MCP credential migration and cleanup for the supported interceptor flow and the required rotation and retained-copy cleanup when migrating from legacy metadata credentials.

IM Channels

DeerFlow supports receiving tasks from messaging apps. Channels auto-start when configured — no public IP required for any of them.

DeerFlow can also expose user-owned IM channel connections in the workspace UI. When channel_connections is enabled, logged-in users can bind Telegram, Slack, Discord, Feishu/Lark, DingTalk, WeChat, WeCom, QQ, or Buzz from the sidebar / Settings > Channels. It reuses the existing outbound channels.* transports, so no public IP or provider callback URL is required. Incoming IM messages then run under the connected DeerFlow user account. See IM Channel Connections for setup and security notes.

Channel	Transport	Difficulty
Telegram	Bot API (long-polling)	Easy
Slack	Socket Mode	Moderate
Feishu / Lark	WebSocket	Moderate
WeChat	Tencent iLink (long-polling)	Moderate
WeCom	WebSocket	Moderate
QQ	WebSocket (text-only C2C and group @mentions; four/five passive replies per source)	Moderate
DingTalk	Stream Push (WebSocket)	Moderate
Buzz	Nostr relay (WebSocket, NIP-42)	Moderate

Configuration in config.yaml:

Discord's channels.discord.allowed_guilds accepts one positive numeric guild ID (quoted or unquoted) or a YAML list. Unset, null, [], or a blank string allows all guilds. Invalid entries are ignored with a warning; any other configured value yielding no valid ID denies every guild and logs an error. allowed_channels accepts one channel ID (quoted or unquoted) or a YAML list of IDs exempt from mention_only, within allowed guilds. An empty value gives no exemptions, so mention_only applies everywhere when enabled.

channels:
  # LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
  langgraph_url: http://localhost:8001/api
  # Gateway API URL (default: http://localhost:8001)
  gateway_url: http://localhost:8001

  # Maximum queued or provider-reserved inbound messages (default: 1000)
  inbound_queue_maxsize: 1000
  # Fixed number of long-lived inbound handler workers (default: 5)
  max_concurrency: 5
  # Seconds to drain accepted work before cancelling active handlers (default: 3)
  shutdown_grace_period_seconds: 3

  # Optional: global session defaults for all mobile channels
  session:
    assistant_id: lead_agent  # or a custom agent name; custom agents are routed via lead_agent + agent_name
    config:
      recursion_limit: 100
    context:
      thinking_enabled: true
      is_plan_mode: false
      subagent_enabled: false

  feishu:
    enabled: true
    app_id: $FEISHU_APP_ID
    app_secret: $FEISHU_APP_SECRET
    # domain: https://open.feishu.cn       # China (default)
    # domain: https://open.larksuite.com   # International

  qq:
    enabled: true
    app_id: $QQ_APP_ID
    client_secret: $QQ_CLIENT_SECRET
    allowed_users: []  # QQ OpenIDs, not QQ account numbers

  wecom:
    enabled: true
    bot_id: $WECOM_BOT_ID
    bot_secret: $WECOM_BOT_SECRET
    # Optional: extra host suffixes inbound media downloads may come from, in
    # addition to the built-in qq.com family and WeCom's official COS media
    # host (ww-aibot-img-1258476243..myqcloud.com); add one here if
    # WeCom rotates to a new COS account or media goes through a proxy
    allowed_media_hosts: []

  slack:
    enabled: true
    bot_token: $SLACK_BOT_TOKEN     # xoxb-...
    app_token: $SLACK_APP_TOKEN     # xapp-... (Socket Mode)
    allowed_users: []               # empty = allow all

  telegram:
    enabled: true
    bot_token: $TELEGRAM_BOT_TOKEN
    # Optional: render final Markdown replies as Telegram Rich Messages.
    rich_messages: false
    allowed_users: []               # numeric user IDs, not @usernames; empty = allow all

  wechat:
    enabled: false
    bot_token: $WECHAT_BOT_TOKEN
    ilink_bot_id: $WECHAT_ILINK_BOT_ID
    qrcode_login_enabled: true      # optional: allow first-time QR bootstrap when bot_token is absent
    allowed_users: []               # empty = allow all
    polling_timeout: 35             # timing values must be positive finite seconds
    polling_retry_delay: 5
    qrcode_poll_interval: 2
    qrcode_poll_timeout: 180
    state_dir: ./.deer-flow/wechat/state
    max_inbound_image_bytes: 20971520
    max_outbound_image_bytes: 20971520
    max_inbound_file_bytes: 52428800
    max_outbound_file_bytes: 52428800
    # Inbound media downloads stream with the caps above and are restricted to
    # these host suffixes (plus *.qq.com and the cdn_base_url host by default)
    allowed_media_hosts: []

    # Optional: per-channel / per-user session settings
    session:
      assistant_id: mobile-agent  # custom agent names are also supported here
      context:
        thinking_enabled: false
      users:
        "123456789":
          assistant_id: vip-agent
          config:
            recursion_limit: 150
          context:
            thinking_enabled: true
            subagent_enabled: true

  dingtalk:
    enabled: true
    client_id: $DINGTALK_CLIENT_ID             # Client ID of your DingTalk application
    client_secret: $DINGTALK_CLIENT_SECRET     # Client Secret of your DingTalk application
    allowed_users: []                          # empty = allow all
    card_template_id: ""                       # Optional: AI Card template ID for streaming typewriter effect

Notes:

assistant_id: lead_agent calls the default LangGraph assistant directly.
If assistant_id is set to a custom agent name, DeerFlow still routes through lead_agent and injects that value as agent_name, so the custom agent's SOUL/config takes effect for IM channels.
IM channel workers call Gateway's LangGraph-compatible API internally and automatically attach process-local internal auth plus the CSRF cookie/header pair required for thread and run creation.
Inbound work is bounded to inbound_queue_maxsize pending messages plus max_concurrency active workers. When capacity is exhausted, socket/polling providers drop new messages before sending DeerFlow's working acknowledgment and emit a rate-limited warning. Buzz leaves its replay cursor unchanged and reconnects for relay replay; GitHub webhooks return 503, marking the delivery failed for manual/API redelivery. Shutdown closes admission immediately, keeps channel transports available while accepted messages drain for up to shutdown_grace_period_seconds, then cancels and awaits active handlers before closing provider resources; the Gateway's outer timeout can cancel an incomplete shutdown without detaching those resources.
Feishu/Lark now queues rapid follow-up messages per mapped DeerFlow thread_id instead of immediately surfacing the generic busy reply, and topic replies keep a per-message card with a compact source-message preview across queued/running/final patches.
Streaming IM channels treat backend error events like transport failures and follow the same reply and retry path. The channel logs the error type and message for diagnosis.

Set the corresponding API keys in your .env file:

# Telegram
TELEGRAM_BOT_TOKEN=123456789:ABCdefGHIjklMNOpqrSTUvwxYZ

# Slack
SLACK_BOT_TOKEN=xoxb-...
SLACK_APP_TOKEN=xapp-...

# Feishu / Lark
FEISHU_APP_ID=cli_xxxx
FEISHU_APP_SECRET=your_app_secret

# WeChat iLink
WECHAT_BOT_TOKEN=your_ilink_bot_token
WECHAT_ILINK_BOT_ID=your_ilink_bot_id

# WeCom
WECOM_BOT_ID=your_bot_id
WECOM_BOT_SECRET=your_bot_secret

# DingTalk
DINGTALK_CLIENT_ID=your_client_id
DINGTALK_CLIENT_SECRET=your_client_secret

Telegram Setup

Chat with @BotFather, send /newbot, and copy the HTTP API token.
Set TELEGRAM_BOT_TOKEN in .env and enable the channel in config.yaml.
The bot accepts inbound text, photos, and documents (with or without captions). Hosted Bot API downloads are limited to 20 MB per attachment.

Slack Setup

Create a Slack App at api.slack.com/apps → Create New App → From scratch.
Under OAuth & Permissions, add Bot Token Scopes: app_mentions:read, chat:write, im:history, im:read, im:write, files:write.
Enable Socket Mode → generate an App-Level Token (xapp-…) with connections:write scope.
Under Event Subscriptions, subscribe to bot events: app_mention, message.im.
Set SLACK_BOT_TOKEN and SLACK_APP_TOKEN in .env and enable the channel in config.yaml.

Feishu / Lark Setup

Create an app on Feishu Open Platform → enable Bot capability.
Add permissions: im:message, im:message.p2p_msg:readonly, im:resource.
Under Events, subscribe to im.message.receive_v1 and select Long Connection mode.
Copy the App ID and App Secret. Set FEISHU_APP_ID and FEISHU_APP_SECRET in .env and enable the channel in config.yaml.
The bot supports inbound text, image, and file messages. Inbound attachment downloads are limited to 20 MB per attachment.

WeChat Setup

Enable the wechat channel in config.yaml.
Either set WECHAT_BOT_TOKEN in .env, or set qrcode_login_enabled: true for first-time QR bootstrap.
When bot_token is absent and QR bootstrap is enabled, watch backend logs for the QR content returned by iLink and complete the binding flow.
After the QR flow succeeds, DeerFlow persists the acquired token under state_dir for later restarts.
For Docker Compose deployments, keep state_dir on a persistent volume so the get_updates_buf cursor and saved auth state survive restarts.
Outbound images/files enforce max_outbound_image_bytes / max_outbound_file_bytes (20 MiB / 50 MiB defaults) while reading, including files that grow after resolution. Oversize reads are rejected before encryption/upload instead of sending a truncated prefix. Non-positive limits disable the corresponding cap.

WeCom Setup

Create a bot on the WeCom AI Bot platform and obtain the bot_id and bot_secret.
Enable channels.wecom in config.yaml and fill in bot_id / bot_secret.
Set WECOM_BOT_ID and WECOM_BOT_SECRET in .env.
Make sure backend dependencies include wecom-aibot-python-sdk. The channel uses a WebSocket long connection and does not require a public callback URL.
The current integration supports inbound text, image, and file messages. Final images/files generated by the agent are also sent back to the WeCom conversation.

DingTalk Setup

Create a DingTalk application in the DingTalk Developer Console and enable Robot capability.
Set the message receiving mode to Stream Mode in the robot configuration page.
Copy the Client ID and Client Secret, set DINGTALK_CLIENT_ID and DINGTALK_CLIENT_SECRET in .env, and enable the channel in config.yaml.
(Optional) To enable streaming AI Card replies (typewriter effect), create an AI Card template on the DingTalk Card Platform, then set card_template_id in config.yaml to the template ID. You also need to apply for the Card.Streaming.Write and Card.Instance.Write permissions.

When DeerFlow runs in Docker Compose, IM channels execute inside the gateway container. In that case, do not point channels.langgraph_url or channels.gateway_url at localhost; use container service names such as http://gateway:8001/api and http://gateway:8001, or set DEER_FLOW_CHANNELS_LANGGRAPH_URL and DEER_FLOW_CHANNELS_GATEWAY_URL.

Commands

Once a channel is connected, you can interact with DeerFlow directly from the chat:

Command	Description
`/new`	Start a new conversation
`/status`	Show current thread info
`/models`	List available models
`/memory`	View memory
`/agent list`	List your Custom Agents
`/agent use`	Start a new conversation with a Custom Agent
`/help`	Show help

Messages without a command prefix are treated as regular chat — DeerFlow creates a thread and responds conversationally.

Agent selection is conversation-scoped: /agent use starts a fresh conversation and pins that Custom Agent in the thread metadata. Existing conversations never switch agents midway, the selection survives a Gateway restart, and opening the IM-created thread in the Web UI continues through the same Custom Agent. Use /agent use lead_agent to return to the default agent in a new conversation.

Request Trace Correlation

Every Gateway HTTP response carries an X-Trace-Id header. The id is inherited from an inbound X-Trace-Id when the caller sends one and generated otherwise, so a proxy or an upstream service can pin one id across services. It needs no configuration and cannot be turned off.

The same id stays attached to work that outlives the HTTP response: the detached run task, any subagents it delegates to, and the background memory-update threads. It is recorded as deerflow_trace_id on the run record (visible in the runs API), in the thread's checkpoint metadata, and in Langfuse traces. Scheduled tasks, MCP task notification runs, and IM channel messages start outside HTTP and mint their own id per occurrence.

Log records carry that id only when enhanced logging is on:

logging:
  enhance:
    enabled: true   # print trace_id into log records
    format: text    # or json

This is off by default because turning it on changes the log format. logging is restart-required, so edit config.yaml and restart the Gateway. The setting affects log output only — the id, the response header, and the run metadata are unaffected.

deerflow_trace_id is a DeerFlow correlation id: it is not a run id, and it is not a provider's native trace id. It is not a lookup key either — nothing resolves a thread or a run from it; use it to correlate log lines. A deerflow_trace_id sent in a run request's metadata or config.context is ignored and overwritten, so the response header, the logs, and the persisted run can never disagree. To pin a correlation id, send the X-Trace-Id header.

Gateway run history also records one terminal run.delivery receipt per run, including zero-output and crash-recovered runs. The receipt is persisted before the durable terminal run status during normal execution. Orphan recovery first atomically claims an expired lease and then idempotently backfills the receipt, so a stale recovery scan cannot overwrite a live run's detailed delivery facts. Receipt persistence remains best-effort during an event-store outage. Runs that fail checkpoint preflight (or are cancelled while waiting for prior finalization) keep the existing completion-data behavior: they receive the zero-delivery receipt but do not overwrite RunStore completion fields with an empty snapshot.

When tool_progress.enabled is true, the same run event history also records result-quality guard phase changes. It records loop-detection decisions and deferred MCP tool promotions for both the lead agent and ordinary task subagents. Promotion events identify newly promoted deferred-tool names and whether routing metadata or tool_search selected them, without copying the search query, routing keywords, schemas, arguments, results, or catalog hash into the promotion event itself.

LangSmith Tracing

DeerFlow has built-in LangSmith integration for observability. When enabled, all LLM calls, agent runs, and tool executions are traced and visible in the LangSmith dashboard.

Add the following to your .env file:

LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx
LANGSMITH_PROJECT=xxx

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 instance, set LANGFUSE_BASE_URL to your deployment URL.

Trace correlation fields. Every agent run is annotated with Langfuse's reserved trace attributes so the Sessions and Users pages light up automatically:

session_id = LangGraph thread_id — groups every trace of the same conversation
user_id = effective user from get_effective_user_id() (falls back to default in no-auth mode)
trace_name = assistant id (defaults to lead-agent)
tags = [env:, model:] (built-in Gateway lead-agent and embedded client runs tag the selected model after default or fallback resolution; custom graph factories may only provide the requested model; the environment tag is omitted when unset)
metadata.deerflow_trace_id = DeerFlow request correlation id, matching X-Trace-Id when request trace correlation is enabled

These are injected into RunnableConfig.metadata at the graph invocation root for both the gateway path (runtime/runs/worker.py::run_agent) and the embedded path (client.py::DeerFlowClient.stream), so any LangChain-compatible callback can read them. Set DEER_FLOW_ENV (or ENVIRONMENT) to tag traces by deployment environment.

Monocle Tracing

DeerFlow also supports Monocle, an OpenTelemetry-based tracer for agentic applications. It records each run end-to-end: LLM calls, agent steps, and tool and MCP invocations, with their inputs, outputs, timings, and token counts.

Add the following to your .env file:

MONOCLE_TRACING=true
MONOCLE_EXPORTERS=file          # file, console, okahu, s3, blob, gcs (default: file)
OKAHU_API_KEY=okh_xxxxxxxx      # required only for the `okahu` exporter

Each run writes one trace file to .monocle/; open it in the Monocle VS Code extension to inspect the span timeline and token counts. Connect to Okahu, an agent-observability platform, to analyze traces across runs and run trace-based and agentic evaluations (via the okahu exporter).

Traces capture span inputs and outputs verbatim — prompts, tool arguments, and model responses — plus token usage and timings. The file exporter keeps them on local disk and never rotates or cleans them up, so prune .monocle/ periodically; the remote exporters (okahu, s3, blob, gcs) send that same data off-box, so enable only destinations you trust. Monocle is initialized once at Gateway startup: a configuration error (unknown exporter, missing OKAHU_API_KEY) is logged there and tracing stays off until the Gateway restarts.

Using Multiple Providers

LangSmith and Langfuse attach as LangChain callbacks, so you can enable both and DeerFlow reports each run to both. If an enabled provider is missing required credentials or fails to initialize, DeerFlow fails fast and names it. Monocle uses a global OpenTelemetry provider rather than a callback; Langfuse shares that provider, so all three can run together. Because both span processors sit on the same shared provider, Monocle's exporters also see Langfuse's spans when both are enabled.

For Docker deployments, tracing is disabled by default. Set LANGSMITH_TRACING=true and LANGSMITH_API_KEY in your .env to enable it.

Existing-Run Stream Actions

Existing-run SSE joins are observation-only on GET: supplying action=interrupt|rollback returns 405. Cancellation on this stream route is POST-only and requires the runs:cancel permission. Accordingly, the OpenAPI contract exposes action and wait only on POST; the GET operation exposes only its path parameters.

Personal Access Tokens

Non-interactive clients (CI pipelines, scripts, server-to-server integrations) can call the Gateway API with a personal access token (PAT) instead of a browser session. Create one while logged in via POST /api/v1/auth/pats — the raw dfp_... value is shown exactly once; only its SHA-256 digest is stored — then send it as a Bearer credential:

POST /api/threads/search
Authorization: Bearer dfp_...
Content-Type: application/json

{}

Each token runs with its owning user's identity (owner filtering and per-user memory keep working), carries a scope set that can only narrow that user's permissions, and is admitted only to the thread/run lifecycle routes — every other route answers 403 to PAT callers, and a PAT never carries admin capability. Tokens can be listed and revoked at any time; revocation is immediate. PATs require a database backend (SQLite/PostgreSQL). Full reference: API Reference — Personal Access Tokens.

From Deep Research to Super Agent Harness

DeerFlow started as a Deep Research framework — and the community ran with it. Since launch, developers have pushed it far beyond research: building data pipelines, generating slide decks, spinning up dashboards, automating content workflows. Things we never anticipated.

That told us something important: DeerFlow wasn't just a research tool. It was a harness — a runtime that gives agents the infrastructure to actually get work done.

So we rebuilt it from scratch.

DeerFlow 2.0 is no longer a framework you wire together. It's a super agent harness — batteries included, fully extensible. Built on LangGraph and LangChain, it ships with everything an agent needs out of the box: a filesystem, memory, skills, sandbox-aware execution, and the ability to plan and spawn sub-agents for complex, multi-step tasks.

Use it as-is. Or tear it apart and make it yours.

Core Features

Skills & Tools

Open Capability Center from the workspace sidebar to manage Plugins (MCP servers and Lark/Feishu integration) and Skills. Both catalogs support search; skill cards show concise descriptions with full descriptions in a detail view. Built-in and user-created/imported skills are listed separately. The Community tab supports importing .skill archives into My skills. General preferences remain in Settings.

Skills are what make DeerFlow do almost anything.

A standard Agent Skill is a structured capability module — a Markdown file that defines a workflow, best practices, and references to supporting resources. DeerFlow ships with built-in skills for research, report generation, slide creation, web pages, image and video generation, and more. But the real power is extensibility: add your own skills, replace the built-in ones, or combine them into compound workflows.

Skills are loaded progressively — only when the task needs them, not all at once. This keeps the context window lean and makes DeerFlow work well even with token-sensitive models.

When deferred skill discovery is enabled, describe_skill ranks installed skills by bounded, Unicode-normalized intent-term coverage across names and descriptions. Natural multi-term requests can therefore find a relevant skill without requiring one exact phrase, while exact select: and required-name +prefix lookups remain available. Ranked searches use up to 256 characters and return up to five results; exact select: lists are not truncated and return all requested catalog matches.

A skill directory is a package boundary: once DeerFlow finds its SKILL.md, nested SKILL.md files under that package (for example evaluation fixtures) remain supporting data and are not registered as runtime skills. This applies to managed integration packs as well as public and custom skills. Namespace directories without their own SKILL.md can still group nested skills.

Discovery follows operator-managed directory symlinks, but skips links back to an ancestor directory so a cyclic namespace does not repeatedly rescan the same tree. Independent links to the same external skill tree remain supported.

Skill Markdown and bundled text resources use UTF-8. Skill-creator CLI and review utilities read and write text explicitly as UTF-8 so localized skills behave consistently across operating systems.

Users can explicitly activate an enabled skill for a single turn by starting the request with /skill-name, for example /data-analysis analyze uploads/foo.csv. DeerFlow loads that skill's SKILL.md as hidden current-turn context while leaving the base prompt limited to skill metadata. Slash activation respects disabled skills, custom-agent skill whitelists, and existing channel commands such as /new and /help.

After an answer loads skills, its toolbar includes Skills used. Hover over or click the icon to see the skills and their sources; hovering a skill name underlines it. Select a skill to inspect its SKILL.md in the resizable side panel (a drawer on mobile). The view uses snapshots captured during successful configured read-tool loads or explicit slash activation, so later edits or removal of a skill do not rewrite its history. Repeated loads appear once per run, in first-load order. Range reads and bounded snapshots are labeled as partial; older conversations without captured evidence do not show this menu. Copy returns the captured Markdown, including YAML frontmatter. Package-relative links and images remain readable references rather than navigating away from the conversation. Skill loads performed through other tools, such as shell commands, are not inferred from answer text.

An enabled skill's allowed-tools policy applies only after that skill is explicitly slash-activated or captured in the agent's active skill context after a read_file load. Merely enabling, advertising, or listing a skill in a custom agent or subagent skills allowlist does not reduce that agent's normal toolset; subagents use the same progressive discovery and activation policy as the lead agent. During a slash-activated run, that explicit skill's policy is authoritative: reading another SKILL.md may provide instructions but cannot widen the slash skill's tools. Without slash activation, policies from skills actually loaded into active context retain their union semantics. Once active, the policy filters both model-visible tool schemas and tool execution. Framework discovery tools (tool_search and describe_skill) remain available so an allowed deferred tool or installed skill can still be discovered, but discovery and promotion never grant permission to execute a business tool omitted from allowed-tools. task is not framework-exempt; a restrictive skill must list it explicitly to delegate to a subagent. Per-step policy decisions are internal runtime context and are removed from observable or persisted context copies. Registry failures and an active set with no remaining valid skill fail closed to framework-safe tools; individual stale paths are ignored only when another valid active skill remains. This is best-effort behavioral scoping, not a hard security boundary: loading skill instructions through another tool is not captured, and active-skill entries can be evicted from bounded context.

When you install .skill archives through the Gateway, DeerFlow accepts standard space-separated allowed-tools, optional frontmatter metadata, and the Claude-compatible argument-hint field instead of rejecting otherwise valid external skills. YAML lists remain supported for allowed-tools and preserve exact runtime names. Exact portable spellings such as WebFetch, WebSearch, Glob, Grep, and Read map to DeerFlow's web_fetch, web_search, glob, grep, and read_file tools; lowercase or otherwise unknown scalar names remain unchanged so custom and MCP tools keep their exact runtime spelling. Parenthesized entries such as Bash(tvly *) are tokenized as one literal entry, including spaces, quoted text, and escaped parentheses, but remain inactive because DeerFlow does not inspect tool arguments; declare bash only when the skill may use the full Bash tool.

Disabling a skill also removes it from the sandbox filesystem view, so shell commands and structured file tools follow the same enabled state. Local, Docker/AIO, hostPath provisioner, and newly created E2B sandboxes source /mnt/skills from enabled-only projections that update when public, custom, legacy, or managed integration skills are toggled, edited, created, deleted, or installed. Structured read_file calls (including line ranges and read-before-write checks) use the sandbox provider's mount mapping, so the user identity captured when the sandbox was acquired remains authoritative. Managed integration packages remain shared, while their projected filesystem visibility follows each user's enabled state. Multi-worker Gateways re-read on-disk enable state while rebuilding user projections, so a toggle handled by one worker is honored by another worker's next sandbox acquire. Existing E2B sandboxes retain their creation-time snapshot until they are recreated. PVC-backed provisioner skills keep their configured PVC snapshot/layout for now; dynamic PVC materialization is tracked separately.

# Paths inside the sandbox container
/mnt/skills/public
├── research/SKILL.md
├── report-generation/SKILL.md
├── slide-creation/SKILL.md
├── web-page/SKILL.md
└── image-generation/SKILL.md

/mnt/skills/custom
└── your-custom-skill/SKILL.md      ← yours

/mnt/skills/integrations
└── lark-cli/lark-doc/SKILL.md      ← managed, read-only

The built-in image-generation skill supports Gemini, MiniMax, and OpenAI-compatible Images APIs. Select the latter with IMAGE_GENERATION_PROVIDER=openai, then configure IMAGE_GENERATION_API_KEY, IMAGE_GENERATION_BASE_URL, and IMAGE_GENERATION_MODEL. For a containerized sandbox, expose these variables through sandbox.environment; sandbox commands intentionally do not inherit API keys from the Gateway process.

For LocalSandboxProvider, this is a managed tool-path boundary rather than host filesystem isolation. Explicit per-Agent skill policies are accepted only while host bash is disabled (the default), because a host subprocess can address canonical paths without using the provider's virtual-path mappings. Use Docker/AIO, the Kubernetes provisioner, or E2B when the filesystem boundary must remain enforceable alongside shell access.

Managed integrations install shared read-only skill packs without mixing them into custom skills. The Lark/Feishu CLI integration is available under Capability Center → Plugins → Lark / Feishu; an administrator installs or upgrades the official lark-* pack once under {DEER_FLOW_HOME}/integrations/skills/lark-cli, and every user discovers that same pack with an independent enabled state. Each user's app configuration and OAuth data remain isolated under {DEER_FLOW_HOME}/users/{user_id}/integrations/lark-cli/{config,data}. These secret directories are restricted to 0700, regular credential files to 0600, and symlinks are rejected.

After installation, users can click Connect Lark to open a browser authorization link; no terminal authorization is required. The same UI can request additional permission domains such as Calendar, Docs, or Drive, or a specific OAuth scope reported by lark-cli. A cheap status refresh only inspects the local credential tree, so the UI reports Credentials configured (not live-verified) until an explicit browser completion performs live token verification. The action then remains Reconnect Lark so users can replace or extend authorization. If an agent hits missing Lark authorization during a conversation, the managed lark-shared guidance points the user back to the same plugin configuration with /workspace/capabilities?tab=plugins&plugin=lark.

Once configured, Change Lark app lets a user point their DeerFlow account at a different Lark/Feishu app without a reinstall — either by pasting an existing app's App ID / App Secret or by re-registering an app in the browser. Switching is per-user (it never touches another user's credentials), validates the new credentials through the official CLI's live tenant-token probe before replacing the active app, and revokes/removes the previous app's OAuth tokens. A rejected credential change does not supersede an in-progress setup or authorization flow. The previous OAuth data is cleared before the CLI stores the replacement app, so the new file-backed keychain secret remains available during reconnection. DeerFlow then immediately opens browser authorization for the newly bound app so the switch ends in a usable connection.

Installing the Lark skill pack resolves the latest official larksuite/cli release from GitHub and downloads that version's skills at install time, so the Gateway needs outbound internet access for that step (it falls back to a bottom-line pinned version if the release lookup fails). The settings page shows the installed version and, when available, the newest published version so an admin can reinstall to upgrade. Air-gapped deployments can pre-stage the archive and point DEER_FLOW_LARK_CLI_SKILLS_ARCHIVE at the local file. Integrity does not depend on a pinned archive byte hash (GitHub does not guarantee stable source-archive bytes); instead the download is restricted to the official GitHub host, every archive member passes structural safety guards, and a content hash of the effective installed skill tree (including DeerFlow's injected shared guidance) is recorded so content changes are auditable across reinstalls.

When sandbox.use selects the AIO provider, the same install also downloads the official Linux amd64 and arm64 CLI release archives, verifies their published SHA-256 checksums, safely extracts one executable per architecture, and mounts the resulting runtime read-only at /mnt/integrations/lark-cli/runtime. An architecture-selecting launcher in that mount makes lark-cli available in the sandbox PATH. Air-gapped AIO deployments can pre-stage a symlink-free runtime tree containing bin/lark-cli plus both linux-{amd64,arm64}/lark-cli files and set DEER_FLOW_LARK_CLI_SANDBOX_RUNTIME_DIR to that directory.

Sandbox trust boundary: the browser never receives the Lark app secret, but agent conversations run lark-cli inside the sandbox, so the per-user credential directories are mounted into it: config (holding the long-lived appSecret) is mounted read-only, its otherwise empty config/locks subdirectory is over-mounted writable for lark-cli coordination files, and data (refreshable OAuth tokens) is writable. The credential-bearing config and data mounts remain readable by any process the agent runs there, so code reached via prompt injection in a tool result could read them. Treat the sandbox as inside the Lark credential trust boundary until the sidecar credential-broker follow-up removes these mounts from sandbox execution.

For remote/Kubernetes deployments (the provisioner backend), the sandbox lark-cli runtime can instead be supplied by an optional init container that copies the binaries into a shared emptyDir — no install-time GitHub download and no hostPath/PVC runtime mount. Publish the image under docker/lark-cli-init and set LARK_CLI_INIT_IMAGE on the provisioner (with the Helm chart, provisioner.larkCliInitImage / provisioner.larkCliBrokerImage); it stays off (legacy behavior) when unset. The Lark integration status (GET /api/integrations/lark/status) reports sandbox_runtime_mode, sandbox_runtime_probed, and sandbox_runtime_ready. sandbox_runtime_probed marks whether runtime readiness was actually evaluated; responses from older backends may omit the flag, in which case the Settings mutation cache keeps the last probed runtime fields instead of overwriting them with an unevaluated fallback — so the Settings UI shows whether lark-cli will actually be present in the sandbox at chat time, rather than a green status hiding a later command not found.

In Lark broker mode, AIO's fresh Bash runs close inherited pipe stdin. Persistent terminal commands keep their input; the shim ignores terminal stdin. Explicit pipelines, heredocs, and file redirections still supply input normally, and the shim forwards that input only after EOF. A stdin idle timeout aborts without executing the command, and broker execution logs omit argument values. See the broker image guide for timeout settings and image rebuild requirements.

If a trusted operator manages the configured skills directory through an external mount such as MinIO, NFS, or CSI, an administrator can call POST /api/skills/reload after changing files. This invalidates skill prompt caches for the current Gateway process and waits up to the bounded refresh timeout so subsequent runs rescan the latest files; running tasks are unchanged. A loader-level filesystem failure returns a generic server error and preserves the last successfully loaded process cache rather than publishing an empty catalog. Uvicorn workers and Kubernetes Pods must each be targeted separately. Direct mount writes bypass the validation, SkillScan, and history applied by DeerFlow's install/edit APIs, so only operator-controlled systems should have write access.

Skill installs and agent-managed skill edits run through SkillScan, a native deterministic safety scanner before the LLM-based skill scanner. Phase 1 runs offline with no Semgrep/OpenGrep dependency, blocks high-confidence CRITICAL findings such as private keys or shell execution, and passes warning findings to the LLM scanner for contextual review. Code files (anything under scripts/, a script suffix such as .py, .sh, or .js, or an extensionless file starting with #!) that are not NUL-free UTF-8 text raise a warning and are still analyzed over a lossy decode, so a single stray byte cannot hide them from CRITICAL checks. The moderation adapter normalizes both plain-text model responses and LangChain Responses API text blocks before parsing the required JSON decision. Python instance-client exfiltration checks follow a minimal same-scope evidence chain: a simple name bound to a known client constructor, optional name-to-name aliases, and an actual outbound method or context-manager use supported by that constructor. Constructor roots must be proven imports; bare canonical-looking names are not inferred as modules. Nested scopes do not inherit client handles and inherit only constructor import aliases that are never rebound in the enclosing scope. Comprehensions, walrus-bearing statements, annotations, complex binding targets, unsupported operations, and ambiguous branch flows produce no finding from this signal; skipped constructs conservatively invalidate every name they may bind so stale client state cannot create a finding. A deterministic work budget or recursion limit reached by this best-effort analysis does not discard findings already collected for the file. Set skill_scan.enabled: false in config.yaml to disable only the deterministic analyzers; safe archive extraction and the LLM scanner still run.

Windows scripts (.bat, .cmd, .ps1, .psm1, .js, .jse, .vbs, .vbe, .wsf), HTML applications (.hta), and scriptlets (.sct) count as code even outside scripts/, regardless of filename case. They receive both SkillScan analysis and the installer's executable-code policy. SkillScan warns about remote downloads piped into common shells, including sudo, interpreter paths, and shell line continuations. Pipes to non-shell tools such as jq and tee do not trigger this warning.

SkillScan treats HTTP hostnames case-insensitively and recognizes bracketed IPv6 loopback ([::1]) URLs as local. External IPv6 endpoints still trigger network findings, and cloud-metadata hostname detection is case-insensitive.

For Python credential mappings, SkillScan checks literal values while treating ordinary dictionary keys as labels. A mapping such as tokens = {"access_token": os.getenv("ACCESS_TOKEN")} does not report a hardcoded credential. Keys matching a recognized cloud or API token format are still checked as embedded credentials.

DeerFlow also ships with skill-reviewer, a public skill for read-only skill quality review. It uses the built-in review_skill_package tool to inspect installed skills, local packages, archives, or pasted SKILL.md content without activating the target skill, binding its secrets, executing its scripts, or installing it. The tool returns a compact, tag-neutralized JSON payload to the model context and keeps the full raw review payload in the tool artifact for programmatic consumers. The deterministic review core reuses DeerFlow parsing and SkillScan facts, emits versioned JSON contracts under contracts/skill_review/, and can be run from the backend CLI:

cd backend
uv run python -m deerflow.skills.review.cli ../skills/public/data-analysis --format text --fail-on error --fail-on-incomplete

Public-skill CI waivers are exact, expiring exceptions in .github/skill-review-waivers.v1.json. Because only the trusted base manifest can suppress a finding, a file-changing pull request can be preauthorized safely by first merging a manifest-only change that lists the reviewed future full-file SHA-256 in preapproved_file_sha256s; the file change can then land in a later pull request.

Tools follow the same philosophy. DeerFlow comes with a core toolset — web search, web fetch, rendered web capture, file operations, bash execution — and supports custom tools via MCP servers and Python functions. The bundled DDG, Brave, Tavily, SearXNG, and Serper search providers accept an optional time_range of day, week, month, or year; omitting it preserves existing search behavior. For DDG recency searches, DeerFlow excludes DDGS backends that ignore time limits. Swap anything. Add anything.

For DDG web search and DDG image search, max_results in config.yaml can be a positive integer or an environment-variable reference such as max_results: $DDG_MAX_RESULTS with DDG_MAX_RESULTS=5. The configured value takes precedence over the tool call's max_results argument. Invalid values (for example, abc, an empty string, or 3.5), zero, and negative counts produce a warning and fall back to the default of 5 results.

Stdio MCP servers can set cwd in extensions_config.json when their entrypoint or data files depend on a specific working directory. The setting applies to both discovery and tool calls; see MCP configuration. Omitted, null, or empty values keep the default working directories.

Tavily web_search also accepts optional include_domains and exclude_domains lists in its config.yaml tool entry to control search sources. Non-empty include_domains uses Tavily's filter mode to restrict results to those domains. These are deployment settings; the model still supplies only query and optional time_range. Omitted filters preserve the existing SDK request; an explicit empty list is forwarded and imposes no restriction of that kind. See the tool configuration example.

Serper web_search supports deployment-level include_domains and exclude_domains too. It checks returned URL hosts (including subdomains), with exclusion taking precedence. Filters can return fewer results, including zero; there are no refill requests. This selects sources, not factual accuracy or a global URL-access policy. The model arguments and image search are unchanged. See Serper configuration for validation and query-length limits.

When using Tavily for web_fetch, extracted pages without a title use their URL as the heading; their content remains available to the agent. Chat tool-step titles accept leading blank lines and up to three spaces before a page's first H1 heading. Indented code, including mixed spaces and tabs, is not used as a title; the tool step falls back to the URL. Tavily search and fetch each read api_key from their own tool entry in config.yaml, falling back to TAVILY_API_KEY when omitted. Fetch does not reuse the search entry's key, so search can use a different provider. If you previously configured a shared Tavily key only under web_search, also set it under web_fetch or use TAVILY_API_KEY for both.

Exporting Custom Skills

Administrators can export their own custom skills from Capability Center → Skills → My skills → View details → Export. Review the file list and declared environment requirements, then choose Download .skill. The archive contains the currently saved skill, including supporting files and empty directories; disabled skills can also be exported. If the skill changes after preview, refresh the file list before downloading. Import the archive on another DeerFlow instance with Install .skill; existing-name conflicts and normal installation security checks still apply.

Account settings, conversations and history outside the skill folder are excluded. Files inside the folder are preserved unchanged, including any credentials an author placed there; filename notices are advisory. Configure dependencies and credentials on the destination. Linked folders/files, hard links, unsupported executable binaries, nested SKILL.md files and nonportable paths cannot be exported. Export supports hosts with descriptor-relative no-follow filesystem APIs (Linux/macOS); unsupported hosts fail explicitly. Limits: 4096 ZIP entries, 64 MiB per file, 100 MiB total content/archive and 1 MiB frontmatter. YAML aliases and excessively complex declarations are not supported. Ordinary script executable semantics are preserved on POSIX import, without restoring special permissions. See the export API contract.

Claude Code Integration

The claude-to-deerflow skill lets you interact with a running DeerFlow instance directly from Claude Code. Send research tasks, check status, manage threads — all without leaving the terminal.

Install the skill:

npx skills add https://github.com/bytedance/deer-flow --skill claude-to-deerflow

Then make sure DeerFlow is running (default at http://localhost:2026) and use the /claude-to-deerflow command in Claude Code.

What you can do:

Send messages to DeerFlow and get streaming responses
Choose execution modes: flash (fast), standard, pro (planning), ultra (sub-agents)
Check DeerFlow health, list models/skills/agents
Manage threads and conversation history
Upload files for analysis

Environment variables (optional, for custom endpoints):

DEERFLOW_URL=http://localhost:2026            # Unified proxy base URL
DEERFLOW_GATEWAY_URL=http://localhost:2026    # Gateway API
DEERFLOW_LANGGRAPH_URL=http://localhost:2026/api/langgraph  # LangGraph API

See skills/public/claude-to-deerflow/SKILL.md for the full API reference.

Private Knowledge Retrieval (RAGFlow)

Answers can cite retrieved RAGFlow evidence with clickable knowledge citations. Click a citation, or an entry in the answer's knowledge sources list, to see the original retrieved excerpt, dataset and document names, and page numbers when RAGFlow supplies them. These are retrieval-time snapshots retained with the conversation, including sources forwarded by ordinary task subagents; they remain inspectable after reloading the conversation. An excerpt is not a live copy of the full document: changes in RAGFlow do not rewrite past evidence. Missing source records are shown as unavailable rather than turned into guessed links. Source snapshots do not add a knowledge-management page or expose the RAGFlow API key. Durable batch exports and standalone Markdown files do not carry these interactive conversation source records. Ordinary document-title links in a Sources section open the same evidence as inline citations. When a tool-output budget applies, only complete evidence entries that fit remain citable; omitted sources are reported rather than retaining a source record for a cut-off excerpt.

DeerFlow can optionally connect to a tenant-scoped RAGFlow deployment. The knowledge_search Agent tool resolves the configured dataset scope, groups datasets by embedding model, and retrieves those groups in parallel so mixed embedding models do not cause a provider error. Dataset IDs and API keys are never exposed to the model. The optional list_knowledge_bases tool returns names only.

Main and custom-agent chats can optionally expose a page-local, icon-only Knowledge selector beside the mode control. Its persistent highlight indicates that knowledge retrieval is active; the neutral state means retrieval is off. Set knowledge_base.scope_selection_enabled: true in config.yaml while using the built-in RAGFlow knowledge_search provider to allow all permitted datasets, selected datasets/files, or no retrieval for a turn. The same config flag controls both chat types; when disabled, neither composer shows the selector or submits a scope. The choice resets to the custom agent’s saved default (or all when unbound) when the page is refreshed or another conversation is opened; each sent human message keeps an immutable scope snapshot for replay and history. The Gateway validates every snapshot, intersects it with the operator's dataset allowlist, propagates the execution-only scope to native and durable subagents, and removes it from model inputs and external traces. Client-supplied internal runtime controls and credentials are also stripped from run context before execution or checkpoint persistence. Idempotent retries accept both canonical snapshots and legacy raw run inputs, preserving retry compatibility across upgrades. Custom agents can save a Default knowledge selection from Agents → Agent settings, including optional file filters or retrieval off. Selecting all knowledge bases clears the binding. The same knowledge_scope field is available on agent create/update APIs and in the agent's stored configuration; omitted updates preserve it and null clears it. It is a default, not an authorization boundary: an explicit per-message selection overrides it, and the operator's allowlist still applies at retrieval time. Gateway runs without a message scope (including scheduled and channel turns) use and snapshot the saved default even when the composer selector is hidden. Regenerate/resume retain the original turn's scope, including legacy unscoped turns, rather than picking up later configuration changes. Unknown or unavailable selections never broaden retrieval. Idempotent retries keep the original run and scope when a default is added, changed, or cleared, including unscoped runs accepted before this feature. This default applies to Gateway-hosted custom-agent turns; direct harness/client integrations continue to supply their own execution scope.

The knowledge_base block is provider-neutral and only controls whether the knowledge capability and selector are enabled. RAGFlow connection, dataset allowlist, and retrieval parameters (base_url, api_key, datasets, page_size, thresholds, and output limits) must be configured on the tools[].name: knowledge_search entry; they are never read from knowledge_base. Custom-agent chat requests carry the selected agent name as both assistant_id and context.agent_name, so Gateway scope admission and runtime agent loading use the same identity. Main chat requests use lead_agent; both identities are admitted only when the shared configuration enables the RAGFlow provider. When answering a pending clarification, an explicitly submitted current selector snapshot wins; clients that omit it inherit the prior turn's accepted scope. Edit-and-regenerate follows the same fallback, and the file catalog is loaded only after a dataset is switched from all files to selected files. This release does not add an independent Knowledge item to the workspace sidebar or a DeerFlow knowledge-management page; create, upload, parse, and delete datasets and documents directly in RAGFlow.

Each message can still select up to 1000 documents. When more than 100 documents are selected from a single dataset, DeerFlow validates them in batches of at most 100 while preserving the complete selection. If any batch contains an inaccessible or non-searchable document, retrieval is rejected.

Advanced deployments can enable pluggable authorization with authorization.enabled in config.yaml. A configured AuthorizationProvider filters denied tools before they reach the model or deferred-tool catalog, then the same provider is checked again before every business-tool execution through the existing guardrail middleware. Gateway threads:* and runs:* route permissions are derived from the same provider, while existing owner checks and admin-only management gates remain in force. Every HTTP route that starts or enables a future Agent run requires runs:create: this includes the stateless POST /api/runs/stream and POST /api/runs/wait endpoints plus scheduled-task create, update, resume, and manual-trigger mutations. Scheduled-task mutations retain their existing threads:write requirement, and the stateless routes separately enforce ownership when the optional thread ID is supplied in the request body. A generated tool_search may bypass the second tool check only when it fronts the current build's already-filtered deferred catalog. Model access follows the same provider: the Gateway models list is filtered per principal, model:use is enforced on model detail requests and again when the runtime resolves the agent's model, and a denied default model falls back to the first remaining candidate that also passes model:use. The built-in RBAC provider supports per-role tools, routes, models, skills, and sandbox allow/deny policies and validates that default_role names a configured role; authorization is disabled by default. See config.example.yaml and the authorization RFC.

Follow-up suggestions also check model:use before calling the selected model, including the default model when no name is supplied. A denied model returns HTTP 403 without an LLM call; authorization-provider failures follow the configured fail_closed policy.

For vision-capable agents, view_image and the subsequent model-context image read also require sandbox:execute. Allowing the tool name alone does not grant image-file access; a role denied sandbox execution cannot reread previously recorded image metadata after its permissions change. External run input and thread-state updates cannot set viewed_images or thread_data; when a saved image is read from a host copy, its path must resolve to the current user's thread and the recorded virtual image path.

Advanced deployments can also extend the agent runtime itself by declaring AgentMiddleware classes under extensions.middlewares in config.yaml or extensions_config.json. Each entry is a module.path:ClassName string (zero-argument constructor) or an object {class, kwargs} whose kwargs are passed to the constructor. kwargs values must be JSON types (object, array, string, number, boolean, or null); YAML dates and timestamps are coerced to ISO strings so they match JSON. DeerFlow loads the same configured list into the lead-agent and subagent pipelines after their built-in runtime middlewares and loop/token guards, but before the terminal-response/safety/clarification tail, so enterprise forks can add domain guardrails, tool-call governance, or observability hooks without patching the built-in middleware builders. Missing packages, invalid classes, broken modules, and constructor errors fail loudly at agent creation. Treat config.yaml and extensions_config.json as trusted operator-controlled files: middleware paths are code execution, just like custom tool, model, sandbox, guardrail, MCP server, and MCP interceptor declarations. Gateway skill/MCP toggle endpoints preserve this field but do not expose an API write path for extensions.middlewares. Separate lead-only/subagent-only middleware lists are not supported yet.

For packaged and configurable runtime integrations, use DeerFlow's extension manager. It accepts a Python package requirement, a public HTTPS Git URL, or a local directory, installs the package into the backend's dedicated extensions dependency group, updates backend/uv.lock, and adds an enabled entry to the startup-only top-level plugins: list in config.yaml:

# PyPI — pin a version for a reproducible deployment
make extension-install SOURCE="deerflow-extension-acme==1.2.3"

# Public HTTPS Git — pin an immutable commit
make extension-install \
  SOURCE="git+https://github.com/acme/deerflow-extension-acme.git@0123456789abcdef0123456789abcdef01234567"

# Local package — an absolute path avoids Make's backend-relative working directory
make extension-install SOURCE="$PWD/examples/deerflow-extension-example"

make extension-list
make extension-upgrade SOURCE="$PWD/examples/deerflow-extension-example"
make extension-disable NAME=acme
make extension-enable NAME=acme
make extension-remove NAME=acme

Installation is interactive because package installation can execute Python build hooks, and the loaded extension later runs with Gateway privileges. For an already-reviewed source, automation can acknowledge that boundary explicitly with cd backend && uv run --frozen --no-group extensions deerflow extensions install --yes. The manager requires uv 0.8.0 or newer; the provided Docker images pin uv 0.11.1. The other direct commands are deerflow extensions upgrade SOURCE, list, enable NAME, disable NAME, and remove NAME; NAME may be the extension name, Python distribution, or module:install value. Do not put credentials in a source URL — a URL carrying embedded userinfo or a credential-looking query parameter is rejected before uv runs. Remote Git sources must use public HTTPS; SSH Git URLs are rejected because the stock Docker builder does not forward host SSH credentials. Installing from a loopback URL is allowed for local tooling but warns, because 127.0.0.1 recorded in the lock is a different machine inside the Docker builder.

A managed package declares exactly one standard PEP 621 entry point:

[project.entry-points."deerflow.extensions"]
acme = "acme_deerflow_extension:install"

That callable uses the standalone deerflow-extension-api contract and can register several contribution kinds: isolated middleware at semantic lead/subagent model or tool positions, lead and subagent task-lifecycle hooks, observers for DeerFlow-owned model calls that are not wrapped by middleware model-call hooks (goal, memory, title, and summarization), Gateway-lifetime services, and eager FastAPI HTTP routers. The contract package has no framework dependencies; extensions must declare FastAPI, LangChain, LangGraph, or other libraries they import. The fetched-content screening example contributes one such middleware at the visible tool position. After operator opt-in it classifies the sanitized text a remote

deer-flow

🦌 DeerFlow - 2.0

Official Website

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Coding Plan from ByteDance Volcengine

InfoQuest

Table of Contents

One-Line Agent Setup

Quick Start

Configuration

Running the Application

Deployment Sizing

Option 1: Docker (Recommended)

Upgrading an existing checkout

Option 2: Local Development

Startup Modes

LangGraph Studio (Optional)

Docker Production Deployment

Advanced

Sandbox Mode

MCP Server

IM Channels

Request Trace Correlation

LangSmith Tracing

Langfuse Tracing

Monocle Tracing

Using Multiple Providers

Existing-Run Stream Actions

Personal Access Tokens

From Deep Research to Super Agent Harness

Core Features

Skills & Tools

Exporting Custom Skills

Claude Code Integration

Private Knowledge Retrieval (RAGFlow)