deerflow

name	deerflow
description	Deep research and async task execution via DeerFlow LangGraph engine. Submit multi-step research tasks through a lightweight API-only Docker deployment (no frontend). Triggers: /deerflow, deep research, async research, deerflow.
version	1.1.3
license	mit-0
metadata	{"openclaw":{"requires":{"bins":"[Truncated]","anyBins":"[Truncated]","env":"[Truncated]"},"os":["linux","darwin"]}}

DeerFlow Integration

What This Skill Does

DeerFlow is a LangGraph-based deep research engine that chains web search, reasoning, and synthesis into structured reports. This skill provides OpenClaw integration for submitting and monitoring research tasks against a running DeerFlow API.

Runtime contract: The skill itself runs entirely locally (python3 only) and communicates with DeerFlow over HTTP. It does not install or run any external Docker images — those are operated independently by the user as the DeerFlow host.

This skill does NOT install DeerFlow or manage its services. It assumes a DeerFlow instance is already running and reachable.

Prerequisites

⚠️ External dependency — your DeerFlow host. DeerFlow itself runs as a separate service on your infrastructure (or a VPS). Its Docker images are maintained by the bytedance/deer-flow project. Review their security posture before deployment.

Required at runtime

Binary	Purpose	Notes
python3	Run submit_task.py / check_status.py	Declared in skill metadata

Required only for initial DeerFlow deployment (one-time setup)

Binary	Purpose	Notes
git	Clone the DeerFlow repository	Not needed once DeerFlow is deployed
docker	Run DeerFlow services	Not needed once DeerFlow is running

If DeerFlow is already running somewhere (yours, a colleague's, or a cloud instance), skip straight to Quick Start — no git/docker needed.

Deploying DeerFlow (if you don't have one)

🔴 Security note: The following steps pull Docker images and run services from the bytedance/deer-flow GitHub repository. You are responsible for reviewing those images and configurations before running them in your environment.

1. Clone and configure

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

Edit .env with your model API keys:

# Required: at least one LLM provider
OPENAI_API_KEY=sk-...
# Or MiniMax
MINIMAX_API_KEY=...
MINIMAX_API_BASE=https://api.minimax.com

# Optional: Tavily for web search
TAVILY_API_KEY=tvly-...

2. Start API-only services

# No nginx, no frontend — just gateway + langgraph
docker compose up -d deer-flow-gateway deer-flow-langgraph

Verify:

curl http://localhost:2024/openapi.json | head   # should return OpenAPI spec
curl http://localhost:8001/health               # should return 200

3. Test with a manual task

curl -X POST http://localhost:2024/threads \
  -H "Content-Type: application/json" \
  -d '{}'
# Returns: { "thread_id": "..." }

Then submit a task:

curl -X POST http://localhost:2024/threads/{thread_id}/runs \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "lead_agent",
    "input": {
      "messages": [{
        "type": "human",
        "content": [{ "type": "text", "text": "Your research query here" }]
      }]
    },
    "config": {
      "recursion_limit": 200,
      "configurable": {
        "model_name": "minimax-m2.7",
        "thinking_enabled": true,
        "is_plan_mode": false,
        "subagent_enabled": false
      }
    }
  }'
# Returns: { "run_id": "..." }

Poll for completion:

curl http://localhost:2024/threads/{thread_id}/runs/{run_id}

When status = success, fetch results:

curl http://localhost:2024/threads/{thread_id}/history

Quick Start

/deerflow <research topic>

Example: /deerflow Analyze the Chinese AI companion market

The skill returns a thread_id and run_id for status tracking.

Architecture

This skill targets the minimal API-only DeerFlow deployment. Only two services are relevant to this skill:

Service	Port	Role
deer-flow-gateway	8001	Business logic & channel glue
deer-flow-langgraph	2024	Core agent orchestration (the only endpoint this skill calls)

Model Configuration

Set model_name in the configurable block:

Model	Config Value	Notes
MiniMax M2.7	minimax-m2.7	Default, reasoning-capable
MiniMax M2.5	minimax-m2.5	Lighter alternative
Kimi	kimi	Requires DeerFlow .env to have Kimi credentials

Set thinking_enabled: true to enable extended chain-of-thought reasoning (recommended for research tasks).

Skill Scripts

This skill includes two helper scripts in scripts/:

submit_task.py

cd ~/.openclaw/workspace/skills/deerflow
python3 scripts/submit_task.py "Your research topic"
# Returns thread_id and run_id

check_status.py

python3 scripts/check_status.py <thread_id> <run_id>
# Polls until completion, then prints the full report

OpenClaw Tool Injection

The skill is auto-injected into OpenClaw as the deerflow tool. OpenClaw agents call it directly when the user triggers the keyword.

Resource Comparison

Deployment	Services	RAM Est.	Use Case
API-only (this skill)	gateway + langgraph	~2 GB	Self-hosted agents, VPS
Full stack	+ nginx + frontend	~4+ GB	Team shared UI

Troubleshooting

LangGraph returns 404

Verify the container is healthy:

docker ps | grep langgraph
curl http://localhost:2024/openapi.json

Task hangs or returns "error" status

Check LangGraph logs:

docker logs deer-flow-langgraph --tail 50

Model API errors

Ensure credentials in DeerFlow's .env are valid and the model_name in your request matches a configured provider.

File Structure

skills/deerflow/
├── SKILL.md           # This file
└── scripts/
    ├── submit_task.py  # Submit a research task
    └── check_status.py # Poll and retrieve results