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
Metadata
3 maintainers
Release files for openrag 0.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| openrag-0.8.0.tar.gz | 7.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openrag-0.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.3 MB
Release files / openrag-0.8.0.tar.gz
| Download URL | openrag-0.8.0.tar.gz |
|---|---|
| Size | 7.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
dbf09cfda55740a80d2d78b3a4dd37a6638fbfac89ad413ff918abce65467251
|
|
BLAKE2b-256 checksum How to use checksums |
1f89dea9277529bd1256893e8f82d07492635f6d1830cb522b3a580c2cb7664d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.12.21 {"installer":{"name":"uv","version":"0.12.21","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
|
Release files / openrag-0.8.0-py3-none-any.whl
| Download URL | openrag-0.8.0-py3-none-any.whl |
|---|---|
| Size | 7.7 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ff4ea01e22408c8711a88ae2db14c07d2333c9f7bc02ab52bfb1f677f56368a2
|
|
BLAKE2b-256 checksum How to use checksums |
e650089b1696dbdeb08b15813e6422341c78d0f76297924f5c8bfa9813965d87
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.12.21 {"installer":{"name":"uv","version":"0.12.21","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
|
