Integration · MODEL CONTEXT PROTOCOL
oraichain ragflow mcp
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
WHAT IT CONNECTS
What oraichain ragflow mcp gives an AI Agent
The source catalog does not include a structured feature list. Review the repository documentation before use.
SECURITY
An MCP listing is not a security review
An MCP server may receive model context, credentials, local files, or permission to call external systems. Review its code, requested environment variables, network behavior, package provenance, and maintenance status before connecting it to an agent.
README
Repository documentation
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.
- Create a new conda environment:
conda create -n ragflow_mcp python=3.12
conda activate ragflow_mcp- Clone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcp- Install dependencies:
pip install -r requirements.txt- Install uv (A fast Python package installer and resolver):
curl -LsSf https://astral.sh/uv/install.sh | sh- Clone the repository:
git clone https://github.com/oraichain/ragflow-mcp.git
cd ragflow-mcp- Create a new virtual environment and activate it:
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate- Install dependencies:
uv pip install -r pyproject.toml-
Start the MCP server
-
Start the inspector using the following command:
# you can choose a different server
SERVER_PORT=9000 npx @modelcontextprotocol/inspectorCOMMUNITY