OpenRAG is a comprehensive Retrieval-Augmented Generation platform designed to enable intelligent document search and AI-powered conversations through a unified package. Built on Langflow, Docling, and OpenSearch, it provides users with the ability to upload, process, and query documents via a chat interface backed by large language models and semantic search capabilities. The platform is constructed using FastAPI for the backend and Next.js for the frontend, creating a modern, accessible interface for document management and retrieval workflows.
The platform emphasizes ease of deployment and immediate usability. Its core features include pre-packaged, ready-to-run functionality where all essential tools are integrated and operational without extensive configuration. The system supports agentic RAG workflows with advanced orchestration capabilities, including re-ranking and multi-agent coordination for sophisticated document processing. Document ingestion is handled intelligently to manage messy, real-world data with sophisticated parsing mechanisms. Users benefit from a drag-and-drop workflow builder powered by Langflow, enabling rapid iteration and customization of retrieval pipelines without requiring deep technical expertise.
OpenRAG is designed for enterprise-scale operations, leveraging OpenSearch to deliver production-grade performance across datasets of any size. The architecture supports modular enterprise add-ons, allowing organizations to extend functionality incrementally as their needs evolve. The platform follows a streamlined workflow that transforms documents into searchable, intelligent knowledge through three primary steps: launching OpenRAG, adding knowledge through document ingestion, and initiating conversations with the system.
The project provides multiple integration pathways for developers. Official SDKs are available for both Python and TypeScript/JavaScript, enabling seamless integration into existing applications. OpenRAG includes a built-in Model Context Protocol server accessible via streamable HTTP at the /mcp endpoint, allowing AI assistants like Cursor and Claude Desktop to connect directly to the knowledge base using standard API key authentication. This eliminates the need for subprocess management or separate installations.
According to GitGenius activity tracking, the repository demonstrates strong community engagement with a median issue and pull request response latency of 0.0 hours and a mean response time of 11.2 hours across 565 tracked items. The most frequently addressed issue categories are bugs with 201 occurrences, enhancements with 126, and documentation with 74. The project's core contributors include aimurphy with 361 tracked events, lucaseduoli with 282 events, and edwinjosechittilappilly with 279 events, indicating sustained active development and maintenance. The repository shares overlapping contributors with opendatahub-io/notebooks, projectdiscovery/nuclei, and mlflow/mlflow, suggesting cross-pollination within the broader data and machine learning ecosystem.
Installation options range from quickstart guides to Docker and Podman deployments for self-managed services, with comprehensive documentation available at docs.openr.ag. The project maintains active support channels through GitHub Discussions and Issues pages for troubleshooting and feature requests, alongside contribution guidelines for developers interested in extending the platform.
