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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.

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✨ Highlight Features

• 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

🔄 How OpenRAG Works

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

🚀 Install OpenRAG

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

✨ Quick Start Workflow

📦 SDKs

Integrate OpenRAG into your applications with our official SDKs:

Python SDK

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

TypeScript/JavaScript SDK

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

🔌 Model Context Protocol (MCP)

Connect AI assistants like Cursor and Claude Desktop to your OpenRAG knowledge base:

pip install openrag-mcp

Quick Example (Cursor/Claude Desktop config):

{
  "mcpServers": {
    "openrag": {
      "command": "uvx",
      "args": ["openrag-mcp"],
      "env": {
        "OPENRAG_URL": "http://localhost:3000",
        "OPENRAG_API_KEY": "your_api_key_here"
      }
    }
  }
}

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

📖 Full MCP Documentation

🛠️ Development

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

🛟 Troubleshooting

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.