This release is a pre-release and may not be stable for production use.
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.
✨ 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:
✨ Quick Start Workflow
1. Launch OpenRAG
↓
2. Add Knowledge
↓
3. Start Chatting
📦 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)
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-mcpPyPI package is deprecated. Connect your MCP client directly to the/mcpendpoint 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.
🛠️ 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.
3 maintainers
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