# OpenRAG

Intelligent Agent-powered document search

[![Langflow](https://img.shields.io/badge/Langflow-1C1C1E?style=for-the-badge&logo=langflow)](https://github.com/langflow-ai/langflow) [![OpenSearch](https://img.shields.io/badge/OpenSearch-005EB8?style=for-the-badge&logo=opensearch&logoColor=white)](https://github.com/opensearch-project/OpenSearch) [![Docling](https://img.shields.io/badge/Docling-000000?style=for-the-badge)](https://github.com/docling-project/docling) [![YouTube Channel](https://img.shields.io/youtube/channel/subscribers/UCn2bInQrjdDYKEEmbpwblLQ?label=Subscribe&style=social)](https://www.youtube.com/@OpenRAG/) [![GitHub stars](https://img.shields.io/github/stars/langflow-ai/openrag?style=social)](https://github.com/langflow-ai/openrag/stargazers) [![GitHub forks](https://img.shields.io/github/forks/langflow-ai/openrag?style=social)](https://github.com/langflow-ai/openrag/network/members) [![Documentation](https://img.shields.io/badge/Documentation-773eff)](https://docs.openr.ag) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langflow-ai/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](https://docs.openr.ag/) or get started with the [quickstart](https://docs.openr.ag/quickstart). Built with [FastAPI](https://fastapi.tiangolo.com/) and [Next.js](https://github.com/vercel/next.js). Powered by [OpenSearch](https://github.com/opensearch-project/OpenSearch), [Langflow](https://github.com/langflow-ai/langflow), and [Docling](https://github.com/docling-project/docling). ---

OpenRAG Demo

## ✨ 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:

OpenRAG Workflow Diagram

## 🚀 Install OpenRAG To get started with OpenRAG, see the installation guides in the OpenRAG documentation: * [Quickstart](https://docs.openr.ag/quickstart) * [Install the OpenRAG Python package](https://docs.openr.ag/install-options) * [Deploy self-managed services with Docker or Podman](https://docs.openr.ag/docker) ## ✨ Quick Start Workflow

Use uv run openrag to start

**1. Launch OpenRAG** ↓ Add files or folders as knowledge

Add files or folders as knowledge

**2. Add Knowledge** ↓ Start Chatting with your knowledge

Start Chatting with your knowledge

**3. Start Chatting**

## 📦 SDKs Integrate OpenRAG into your applications with our official SDKs: ### Python SDK ```bash pip install openrag-sdk ``` **Quick Example:** ```python 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](https://pypi.org/project/openrag-sdk/) ### TypeScript/JavaScript SDK ```bash npm install openrag-sdk ``` **Quick Example:** ```typescript 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](https://www.npmjs.com/package/openrag-sdk) ## 🔌 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-mcp` PyPI package is deprecated. Connect your MCP client directly to the `/mcp` endpoint instead. **Quick Example (Cursor/Claude Desktop config):** ```json { "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](https://github.com/langflow-ai/openrag/tree/main/sdks/mcp) ## 🛠️ Development For developers who want to [contribute to OpenRAG](https://docs.openr.ag/support/contribute) or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md). ## 🛟 Troubleshooting For assistance with OpenRAG, see [Troubleshoot OpenRAG](https://docs.openr.ag/support/troubleshoot) and visit the [Discussions page](https://github.com/langflow-ai/openrag/discussions). To report a bug or submit a feature request, visit the [Issues page](https://github.com/langflow-ai/openrag/issues).