OpenRAG: An open-source stack for RAG — Phil Nash

2026-04-08 · Source: AI Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

IBM has released OpenRAG, an open-source, agentic RAG stack designed to simplify the development of robust retrieval-augmented generation systems. Addressing the complexities of RAG, OpenRAG integrates three existing open-source projects: Docling for document processing, OpenSearch for search indexing, and LangFlow for visual orchestration and agent management. Docling handles diverse document types, including challenging PDFs, with pipelines for text, tables, images, and even audio/video, supporting OCR and VLM models like Granite Docling 258M. OpenSearch provides hybrid vector and keyword search, multi-embedding model support, and utilizes the JVector KNN plugin for efficient, disk-based indexing. LangFlow ties these components together, enabling visual flow editing, agentic retrieval, and extensive customization, including cloud connectors for Google Drive and SharePoint, and local model support via Ollama. OpenRAG, currently at version 0.4.0, offers a powerful baseline for building customizable RAG systems.

Key takeaway

For AI Architects and NLP Engineers building RAG systems, OpenRAG offers a powerful, customizable open-source baseline. You should explore its integration of Docling, OpenSearch, and LangFlow to streamline document ingestion, advanced search, and agentic retrieval, significantly reducing development overhead while maintaining flexibility for diverse data and user requirements. Consider contributing to its open-source development to shape its future capabilities.

Key insights

OpenRAG is an open-source, agentic RAG stack combining Docling, OpenSearch, and LangFlow for flexible, customizable retrieval-augmented generation.

Principles

• RAG complexity demands high-quality baselines.
• Agentic retrieval enhances search precision.
• Open-source components foster extensibility.

Method

OpenRAG ingests documents via Docling, embeds chunks, indexes them in OpenSearch using JVector, and orchestrates agentic retrieval and generation flows visually with LangFlow.

In practice

• Use Docling for robust PDF and multimedia processing.
• Implement hybrid search with OpenSearch and JVector.
• Customize RAG flows visually using LangFlow.

Topics

• OpenRAG
• Retrieval-Augmented Generation
• Docling
• OpenSearch
• LangFlow

Best for: AI Architect, NLP Engineer, AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• langflow-ai / openrag — OpenRAG provides an integrated platform for intelligent document search and AI-powered conversations using RAG.
• What is OpenRAG? Unlocking the Future of RAG in Generative AI — RAG remains critical for cost, accuracy, and domain-specific knowledge, even with large LLM context windows.
• The Open Source RAG Stack: A Complete Guide to Building Retrieval-Augmented Generation Systems — The open-source RAG stack offers a modular, customizable approach to building context-rich AI systems.
• I used OpenClaw to replace my brain — A personal RAG-based knowledge base ingests URLs, extracts entities, and enables natural language search.
• Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions — Hybrid search combining semantic and text-based methods significantly enhances AI assistant accuracy and adaptability.
• MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval — Topic metadata, selected and abstracted, can efficiently guide RAG retrieval over coarse chunks, improving precision and latency.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Engineer.