OpenRAG

OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations.

Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience.

Check out the documentation or get started with the quickstart.

Built with FastAPI and Next.js. Powered by OpenSearch, Langflow, and Docling.

• Pre-packaged & ready to run - All core tools are hooked up and ready to go, just install and run
• Agentic RAG workflows - Advanced orchestration with re-ranking and multi-agent coordination
• Document ingestion - Handles messy, real-world data with intelligent parsing
• Drag-and-drop workflow builder - Visual interface powered by Langflow for rapid iteration
• Modular enterprise add-ons - Extend functionality when you need it
• Enterprise search at any scale - Powered by OpenSearch for production-grade performance

OpenRAG follows a streamlined workflow to transform your documents into intelligent, searchable knowledge:

To get started with OpenRAG, see the installation guides in the OpenRAG documentation:

• Quickstart
• Install the OpenRAG Python package
• Deploy self-managed services with Docker or Podman

Integrate OpenRAG into your applications with our official SDKs:

pip install openrag-sdk

Quick Example:

import asyncio
from openrag_sdk import OpenRAGClient
async def main():
 async with OpenRAGClient() as client:
 response = await client.chat.create(message="What is RAG?")
 print(response.response)
if __name__ == "__main__":
 asyncio.run(main())

📖 Full Python SDK Documentation

npm install openrag-sdk

Quick Example:

import { OpenRAGClient } from "openrag-sdk";
const client = new OpenRAGClient();
const response = await client.chat.create({ message: "What is RAG?" });
console.log(response.response);

📖 Full TypeScript/JavaScript SDK Documentation

OpenRAG ships a built-in MCP server over streamable HTTP, mounted on your instance at /mcp. Connect AI assistants like Cursor, Claude Desktop, and IBM Bob to your OpenRAG knowledge base — no subprocess and no separate install. Authenticate with the same OpenRAG API key you use for the REST API, passed via the X-API-Key header.

Important: The standalone openrag-mcp PyPI package is deprecated. Connect your MCP client directly to the /mcp endpoint instead.

Quick Example (Cursor/Claude Desktop config):

{
 "mcpServers": {
 "openrag": {
 "url": "http://localhost:3000/mcp",
 "headers": {
 "X-API-Key": "orag_your_api_key_here"
 }
 }
 }
}

The MCP server provides tools for RAG-enhanced chat, semantic search, document ingestion, knowledge filters, and settings management.

📖 Full MCP Documentation

For developers who want to contribute to OpenRAG or set up a development environment, see CONTRIBUTING.md.

For assistance with OpenRAG, see Troubleshoot OpenRAG and visit the Discussions page.

To report a bug or submit a feature request, visit the Issues page.