openrag: an agent-powered RAG platform for intelligent document search and AI conversations

What it solves

OpenRAG provides a comprehensive platform for intelligent document search and AI-powered conversations. It solves the complexity of setting up Retrieval-Augmented Generation (RAG) by offering a pre-packaged system that handles document ingestion, semantic search, and chat interfaces, allowing users to query their own documents using large language models.

How it works

OpenRAG transforms documents into searchable knowledge through a streamlined workflow. It uses Docling for intelligent parsing of messy real-world data, OpenSearch for production-grade semantic search, and Langflow for orchestrating retrieval workflows and agentic coordination. The platform is built with FastAPI and Next.js and provides a chat interface for users to interact with their knowledge base.

Who it’s for

This tool is for individuals and enterprises looking to deploy a production-ready RAG system quickly. It is also suitable for developers who want to integrate RAG capabilities into their own applications via Python and TypeScript SDKs or connect AI assistants like Cursor and Claude Desktop via the Model Context Protocol (MCP).

Highlights

• Agentic RAG workflows: Supports advanced orchestration, including re-ranking and multi-agent coordination.
• Visual Workflow Builder: Features a drag-and-drop interface powered by Langflow for rapid iteration.
• Enterprise Scalability: Uses OpenSearch to ensure search performance at any scale.
• MCP Integration: Includes a built-in Model Context Protocol server to connect external AI assistants directly to the knowledge base.