deerflow

name	deerflow
slug	deerflow
version	1.1.0
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.
metadata	{"openclaw":{"requires":{"bins":"[Truncated]"},"env":[],"os":["linux","darwin"]}}

DeerFlow Integration

Overview

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.

Architecture note: This skill targets the minimal API-only deployment — no Nginx, no frontend. Only two Docker services run:

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

This is the recommended deployment for resource-constrained environments (VPS, small servers). All task submission is done by calling the LangGraph API directly.

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.

Minimal Docker Deployment (API-Only)

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. Submit your first 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

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