15 Best Open-Source RAG Frameworks in 2025

Mark Ponomarev

Mark Ponomarev

6 June 2025

15 Best Open-Source RAG Frameworks in 2025

Large Language Models (LLMs) are revolutionary, but they have a fundamental limitation: their knowledge is frozen in time, limited to the data they were trained on. They can't access your private documents, query real-time data, or cite their sources. This is where Retrieval-Augmented Generation (RAG) comes in.

RAG is the architectural pattern that gives LLMs a superpower: the ability to retrieve relevant information from external knowledge bases before answering a question. This simple but powerful idea transforms a generic LLM into a specialized expert, capable of providing accurate, up-to-date, and context-aware responses.

In 2025, building a simple "chat with your PDF" app is just the beginning. The RAG ecosystem has exploded with sophisticated open-source frameworks designed to build production-grade, scalable, and verifiable AI systems. Whether you're a solo developer, a data scientist, or an enterprise architect, there's a framework built for you. This guide breaks down the top 15 open-source RAG frameworks you need to know.

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Of course. Here is a refined version of the article, with all citation links removed for a cleaner reading experience.


The Core Contenders: Foundational Frameworks for RAG

These frameworks have established themselves as the go-to choices for many developers, offering comprehensive features and strong community support.

1. LangChain: the Go-to Open Source RAG Framework

LangChain remains a dominant force in the LLM application development space, and its RAG capabilities are a cornerstone of its appeal. It provides a modular and extensible architecture that allows developers to chain together various components, including document loaders, text splitters, embedding models, vector stores, and retrievers.

2. LlamaIndex

Originally conceived as a data framework for LLMs, LlamaIndex has carved out a niche as a premier tool for building robust and production-grade RAG applications. Its strength lies in its sophisticated indexing and retrieval strategies, designed to handle complex and multi-modal data with ease.

3. Haystack by deepset: The Enterprise-Ready RAG Solution

Haystack, developed by deepset AI, is a mature and modular framework designed for building production-ready NLP systems, with a strong emphasis on RAG. It offers a flexible pipeline-based approach that allows for the seamless integration of various components, including retrievers, readers, and generators.

The New Wave of RAG Frameworks: Emerging and Specialized Frameworks

This next set of frameworks is pushing the boundaries of what's possible with RAG, offering innovative approaches and catering to specific needs.

4. RAGFlow: The Visual and User-Friendly Open Source RAG Framework

RAGFlow is a rising star that emphasizes a "quality-in, quality-out" philosophy for RAG. It provides a visual, low-code interface for building and managing RAG pipelines, making it accessible to a broader audience beyond just seasoned developers.

5. DSPy: The Programming-Not-Prompting Paradigm

DSPy, developed by the Stanford NLP Group, introduces a novel programming model for RAG that shifts the focus from manual prompt engineering to a more structured and programmatic approach. It allows developers to define the components of their RAG pipeline and then uses an optimizer to automatically generate and refine the prompts.

6. Verba: the RAG Chatbot Powered by Weaviate

Verba is an open-source RAG application built by the team behind the Weaviate vector database. It offers an end-to-end, user-friendly interface for interacting with your data through a conversational AI.

7. RAGatouille: Easy-to-use ColBERT in any RAG pipeline

RAGatouille is a specialized library focused on making ColBERT, a powerful late-interaction retrieval model, more accessible for RAG applications. It simplifies the process of training, indexing, and using ColBERT models, which can often outperform standard dense retrieval methods.

8. Unstructured.io

While not a full-fledged RAG framework in itself, Unstructured.io is an indispensable tool for any serious RAG implementation. It provides a suite of open-source libraries for parsing and pre-processing complex, unstructured documents like PDFs, HTML files, and images, preparing them for ingestion into a vector database.

The Enterprise Ready RAG Frameworks

These frameworks are tailored for enterprise use cases and the burgeoning field of AI agents.

9. Cohere Coral: The Enterprise-Grade Conversational AI

Cohere's Coral is a conversational AI platform that leverages RAG to provide accurate and verifiable information. While Cohere offers managed services, its underlying technologies and concepts are influencing the open-source landscape.

10. LLMWare: The Private and Secure RAG Solution

LLMWare is a framework designed for building enterprise-grade RAG applications with a focus on privacy and security. It enables the use of smaller, specialized, and privately-hosted LLMs, giving organizations more control over their data.

11. Flowise: The Visual No-Code/Low-Code Contender

Flowise is an open-source UI-based tool that allows you to build customized LLM-powered applications with a drag-and-drop interface. It's an excellent choice for rapid prototyping and for teams with varying levels of technical expertise.

12. AutoGen: The Multi-Agent Maestro

AutoGen, a framework from Microsoft Research, enables the development of LLM applications using multiple, collaborating agents. This makes it particularly well-suited for building sophisticated RAG systems where different agents can be responsible for different aspects of the retrieval and generation process.

Best Niche RAG Frameworks

These frameworks are carving out their own unique spaces in the RAG landscape.

Of course. Here are the rewritten sections for Marten, Cheshire Cat AI, and a replacement for Mendable, complete with updated descriptions and their official links.

To maintain the integrity of the "open-source" list, Mendable, which is primarily a commercial product, has been replaced with RAGAs, a leading open-source RAG evaluation framework.


13. Marten: The .NET Data Powerhouse

For developers rooted in the .NET ecosystem, Marten provides a robust foundation for building data-intensive applications, including sophisticated RAG systems. It cleverly transforms PostgreSQL into a full-fledged document database and event store, allowing .NET developers to work with objects and events natively without leaving their preferred environment. Its powerful JSONB support is ideal for storing and indexing the unstructured text and vector embeddings at the heart of RAG. You can explore its capabilities further at the Marten official website.

14. Cheshire Cat AI: The Customizable Agent Framework

Cheshire Cat AI is a production-ready, open-source framework designed for creating highly customizable conversational AI agents. Its philosophy centers on an extensible plugin architecture, which allows developers to easily integrate various LLMs, vector stores, and custom tools to shape the agent's behavior. This makes it an agile platform for prototyping and deploying RAG applications where specific, chained functionalities are required for retrieval and reasoning. Learn more about its architecture on the Cheshire Cat AI GitHub page.

15. RAGAs: The RAG Evaluation Specialist

Once a RAG pipeline is built, how do you know if it's actually effective? RAGAs is a dedicated open-source framework designed specifically to answer that question. It provides a suite of metrics to evaluate RAG pipelines based on their retrieval and generation quality, without relying on human-annotated ground truth labels. This enables continuous monitoring and improvement of RAG systems by assessing key aspects like faithfulness, answer relevancy, and context precision. You can find the framework and its documentation on the RAGAs official website.

Conclusion: A Flourishing and Diverse Ecosystem

The open-source RAG landscape in 2025 is a testament to the rapid pace of innovation in the field of generative AI. From mature, all-encompassing frameworks like LangChain and LlamaIndex to specialized tools like RAGatouille and innovative programming paradigms like DSPy, developers have an unprecedented array of options for building the next generation of intelligent applications. The choice of framework will ultimately depend on the specific needs of the project, the expertise of the team, and the desired level of control and customization. One thing is certain: the future of AI is not just about generating text; it's about generating a grounded, accurate, and contextually aware understanding of the world, and these open-source RAG frameworks are leading the charge.

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