Server Deerflow Kinthai

MCP Server that exposes DeerFlow deep capabilities via standard Model Context Protocol.

Any MCP client (OpenClaw, Claude Desktop, Cursor, etc.) can discover and invoke DeerFlow skills through this server.

Architecture

MCP Client (OpenClaw / Claude Desktop / Cursor)
    |
    | MCP protocol (SSE on :8808)
    v
mcp-server-deerflow-kinthai
    |
    | LangGraph REST API (:2024)
    v
DeerFlow (bytedance/deer-flow)
    |
    +-- deep research (multi-source web search + cross-verification)
    +-- data analysis (DuckDB)
    +-- chart visualization (26+ chart types)
    +-- PPT generation
    +-- image generation
    +-- consulting analysis (SWOT, Porter's, etc.)

The server is a thin wrapper: it translates MCP tool calls into DeerFlow LangGraph runs, extracts the response text and artifacts, and returns them in MCP format. DeerFlow itself remains untouched upstream.

Tools

All tools accept a query string (required) and an optional agent_name for specialized DeerFlow agent personas.

Quick Start

# Install
pip install mcp-server-deerflow-kinthai

# Run (requires a running DeerFlow instance)
export DEERFLOW_LANGGRAPH_URL=http://localhost:2024
mcp-server-deerflow-kinthai

The server starts on port 8808 with SSE transport at /sse.

Requires Python >= 3.12.

Prerequisites

You need a running DeerFlow instance. Follow the DeerFlow README to set it up, then point this server at it:

# Default: DeerFlow LangGraph on localhost:2024
export DEERFLOW_LANGGRAPH_URL=http://localhost:2024

# Optional: DeerFlow Gateway for artifact downloads (charts, PPTs, images)
export DEERFLOW_GATEWAY_URL=http://localhost:8001

Configuration

Environment Variables

OpenClaw

Add to your openclaw.json:

{
  "mcp": {
    "servers": {
      "deerflow-kinthai": {
        "url": "http://localhost:8808/sse"
      }
    }
  }
}

Claude Desktop

Add to your Claude Desktop config:

{
  "mcpServers": {
    "deerflow-kinthai": {
      "command": "mcp-server-deerflow-kinthai"
    }
  }
}

Claude Code

claude mcp add deerflow-kinthai http://localhost:8808/sse --transport sse

Mount in Existing App

The server can be embedded in an existing FastAPI/Starlette application:

from fastapi import FastAPI
from mcp_server_deerflow_kinthai.server import create_starlette_app

app = FastAPI()
app.mount("/mcp", create_starlette_app())

Development

git clone https://github.com/kinthaiofficial/mcp-server-deerflow-kinthai
cd mcp-server-deerflow-kinthai
pip install -e ".[dev]"
pytest

Related Projects

• DeerFlow — The upstream multi-agent research framework by ByteDance
• openclaw-kinthai — OpenClaw channel plugin for KinthAI
• kinthai-agent-cli — Universal CLI bridge for connecting any agent to KinthAI

License

MIT — KinthAI