25+ Best Open Source RAG Frameworks in 2025

Retrieval Augmented Generation (RAG) has emerged as a standard paradigm for enhancing the factual accuracy and contextual relevance of Large Language Models (LLMs) by integrating retrieval mechanisms.

RAG significantly enhances the robust capabilities of LLMs by tailoring them to specific domains or an organization’s internal knowledge base, all without necessitating model retraining.

As the demand for AI development and deployment continues to rise, selecting the appropriate RAG framework becomes essential for effectively retrieving data. This blog provides comprehensive insights into top RAG frameworks while highlighting their key features. Let us begin.

  Generate  Key Takeaways Generating...

• LangChain and LangGraph are leaders in RAG's open-source tools. They provide easy-to-use tools for document retrieval, memory management, and integration with large language models (LLMs).

• Frameworks that feature multi-level memory and smart retrieval systems enhance context retention and accuracy. This makes them suitable for high-quality conversational AI in businesses.

• Open-source RAG tools give businesses the flexibility to customize solutions according to their specific workflows, security needs, and growth plans.

• Choosing the right RAG framework can make AI development smoother, improve information retrieval accuracy, and speed up the benefits of your AI projects.

Top Open Source Retrieval Augmented Generation (RAG) Frameworks

Open-source RAG AI frameworks are transforming the way businesses develop intelligent AI pipelines. With features such as modular chains and enhanced memory management, these frameworks offer significant advantages. Let us examine the distinctive qualities that set each of them apart.

1. Langchain

LangChain is a popular open-source framework for building applications that use large language models (LLMs).

It is available as a Python and JavaScript library and helps businesses improve their AI capabilities with better access to contextual knowledge. Langchain understands natural language queries efficiently and then retrieves relevant documents.

Key Features of LangChain

Langchain offers several features that make it an excellent choice for implementing RAG:

• Versatile Document Loaders lets extract data from files, websites, databases, and third-party services with built-in loader support.
• Efficient Text Splitters breaks large documents into manageable chunks to overcome LLM context window limits.
• Vector Store Integrations connects with Chroma, Pinecone, FAISS, and Weaviate for fast and semantic similarity search.
• Powerful Embedding Support converts text into dense vectors to enhance meaning-based information retrieval.
• Advanced Retriever like VectorStoreRetriever for precise and customizable search experiences.
• Unified LLM Interface supports both proprietary and open-source models under one interface for easy switching and testing.
• Built-in memory support retains conversation history in chat-based RAG applications for better context and continuity.
• Simplified development offers high-level abstractions that reduce complexity and speed up RAG solution development.

2. RAGFlow

RAGFlow is a self-hosted, open-source framework designed for Retrieval-Augmented Generation based on advanced document understanding.
This platform provides a streamlined RAG workflow suitable for businesses of all sizes. It combines the large language model's capabilities to deliver accurate responses. RAGFlow is supported by well-documented citations from various complex data formats.

Core Features of RAGFlow

• Advanced Knowledge Extraction: This feature works with unstructured data and can handle any amount of text.

• Grounded Citations: You can trace references and see them broken down visually.

• Template Chunking: You can customize and explain templates to suit your needs.

• Data Compatibility: This tool supports many different formats, including Word, Excel, images, and web content.

• Automated RAG Workflow: You can configure language models, recall multiple pieces of information, and use APIs easily.

3. Lang Graph

LangGraph is a new framework built on LangChain that aids in generating contextually relevant responses. It helps solve a big problem in modern AI: creating and managing processes that can think through multiple steps. LangGraph adds a powerful graph-based structure made for complex workflows that utilize RAG technology.

For business owners who want to use advanced AI solutions, LangGraph is an important advancement. It helps create RAG systems that are more sophisticated, reliable, and easier to maintain.

• Its node-based architecture structures your RAG pipeline using nodes for LLM calls, retrieval, agent actions, and logic execution.
• With Edge-Driven Flow, developers can define the execution path between nodes using edges. This ensures smooth data and operation flow.
• Graph Execution Engine manages the execution and state transitions of complex RAG workflows with precise control.
• It seamlessly integrates with LangChain tools like document loaders, vector stores, and LLMs.
• It supports multi-stage and conditional logic pipelines for sophisticated RAG applications.
• The multi-agent support enables collaborative task handling by incorporating multiple LLM-powered agents with defined roles.
• It maintains and passes the state management context between nodes for consistent, context-aware processing.
• High Customization & Control empower teams to define and tailor RAG pipelines to meet specific operational needs.
• It facilitates the building of intelligent apps like automated reports, data analysis tools, and AI assistants.

4. Llama Index

LlamaIndex is a data framework that helps connect LLMs with structured data and private data sources. It serves as a strong foundation for creating RAG applications.

LlamaIndex organizes data ingestion, indexing, and retrieval, making it easier to develop AI systems that use knowledge. Its modular design links raw data with LLM capabilities. This allows for contextual reasoning over custom datasets.

Key Features of Llama Index Include:

• LlamaIndex has a modular architecture that combines different components to create personalized RAG pipelines.
• This RAG framework can handle text, images, and other types of data in a unified way.
• It has the ability to gather data from various sources and formats, including APIs, PDFs, documents, and SQL databases.
• It uses advanced retrieval mechanisms, smart query engines that focus on finding the most relevant information.
• It has access to over 300 integration packages to work with your preferred LLMs, embeddings, and vector stores.
• This framework can enhance retrieval performance through techniques such as reranking and response synthesis.

5. LLM Ware

LLMWare is a retrieval-augmented generation framework that utilizes small, specialized models instead of large language models, significantly reducing computational and financial costs.

This approach offers cost-effective RAG solutions that can run on standard hardware, like laptops. With robust document processing and a flexible design, LLMWare enables organizations to create efficient RAG systems that optimize performance and resource use.

Key Features of LLM Ware

• Efficient model deployment that can run on CPUs and edge devices.
  Easily processes documents across formats like PDF, Office, text, and markdown.
• With multiple vector database options, it can connect to databases such as MongoDB and Postgres.
• Through parallelized parsing, large collections of documents can be quickly processed.
• It improves retrieval quality with advanced query techniques and dual-pass retrieval.
• LLMWare can create summaries of documents as part of the processing steps.
• It uses GPU resources for model inference when available.

6. Light RAG

LightRAG is a simple and efficient way to combine document retrieval with generation. It focuses on being fast and easy to use. According to benchmark tests in the repository, LightRAG outperforms many other methods in several key areas, making it an ideal choice for tasks that require both speed and quality.

Several essential features contribute to LightRAG's effective implementation:

• It offers optimized performance and outshines traditional RAG methods in testing outcomes.
• It is excellent at identifying relevant information from documents.
• It can collect a wide range of useful content, avoiding redundancy.
• Light RAG has a user-friendly interface that provides tools that enhance information accessibility.
• It includes an interactive web UI for a seamless user experience.
• It can efficiently handle the bulk processing of multiple documents.

7. Flash RAG

FlashRAG is a Python toolkit for research on Retrieval-Augmented Generation. Unlike other tools that focus on implementation, FlashRAG emphasizes reproducibility and experimentation. This enables researchers to quickly replicate existing studies or develop new methods without needing to spend time on data preparation and basic implementation.

FlashRAG provides several key features for research:

• It provides access to 36 pre-processed benchmark RAG datasets for tasks such as question answering and entity linking.
• FlashRAG implements 17 state-of-the-art RAG algorithms with consistent, user-friendly interfaces.
• It features a modular architecture that allows for easy swapping of retrievers, generators, and components.
• It provides a web-based UI for interactive experimentation and result visualization.
• It includes comprehensive documentation for reproducing and customizing experiments.
• FlashRAG delivers built-in performance benchmarks and evaluation metrics for quick comparisons.
• It supports multimodal RAG pipelines that incorporate text, images, and other data types.

8. Retrieval Augmented Generation to Riches (R2R)

R2R is an advanced system that helps you find information quickly and efficiently. It provides a clear API for seamless integration into your workflows.

R2R integrates agentic reasoning capabilities with its Deep Research API. This enables multi-step reasoning by gathering relevant data from your knowledge base and external sources. This combination of traditional retrieval methods and intelligent reasoning makes R2R highly effective in tackling complex questions that require a deeper understanding.

R2R provides a range of useful features for deploying production systems. This includes:

• It ingests multimodal content, including text, PDFs, images, and audio files.
• It supports hybrid search by combining semantic and keyword methods with rank fusion.
• R2R automatically builds contextual knowledge graphs by extracting entities and relationships.
• It enables complex, multi-step information gathering with agentic reasoning via the Deep Research agent.
• It also offers production-ready features like user authentication, collection management, and full API access.
• It offers flexible deployment options, available via cloud or self-hosted, with support for Docker.

9. HayStack

Haystack combines advanced language models with traditional information retrieval techniques. It is designed for real-world use and provides scalable systems that can efficiently manage heavy workloads in businesses.

Haystack is a strong choice for organizations that want to use retrieval-augmented generation development solutions on a large scale. It supports flexible backends and many ready-to-use components.

Key Features of HayStack

• Haystack integrates seamlessly with Elasticsearch, OpenSearch, and other information retrieval systems.
• It has been tested in real-world applications, which makes it a reliable choice for critical projects.
• Haystack combines retrieval with generative AI to provide accurate answers.
• It offers pre-configured pipelines for document search, Q&A systems, and hybrid search scenarios.

10. RagaTouille

RAGatouille is a lightweight framework that helps you create RAG pipelines. It combines the strength of pre-trained language models with effective retrieval methods to produce relevant and coherent content. The framework facilitates the management of retrieval and generation tasks.

The architecture of RAGatouille is flexible and modular. This enables users to experiment with various retrieval algorithms and generation models. It works with multiple data sources, like text documents, databases, and knowledge graphs.

Key Features of RAGatouille

• It handles large datasets with better retrieval efficiency.
• RAGatouille generates responses using OpenAI, Hugging Face Transformers, or Anthropic Claude.
• It offers retrieval methods, including keyword-based options and dense passage retrieval.
• It allows for customizable prompt templates to ensure a clear understanding of questions.
• It supports distributed processing with Dask and Ray.

11. DSPY

DSPY is an open-source framework designed to help build flexible language model systems. Developed by the Stanford NLP community and supported by over 250 contributors, DSPY stands for Declarative Self-improving Python. It focuses on creating AI code that is easy to understand and put together.

Key Features of DSPY

• With DSPY, you can define AI parts and objectives using clear Python code.
  It auto-generates prompts and parses language model outputs behind the scenes.
• It has built-in optimizers like dspy.MIPROv2 fine-tune prompts or model weights based on custom metrics.
• DSPY enables rapid iteration and improvement without manual prompt engineering.
• It offers a modular design for composing, optimizing, and refining LLM pipelines.

12. Txtai

Txtai is an open-source database designed for semantic search and language model workflows. It provides a comprehensive system for RAG by integrating vector storage, text processing, and language model management. Its simple API allows developers to create RAG applications effortlessly.

Core Features of Txtai

• Through embedding indexing, it stores and retrieves documents using a search based on vectors.
• It uses SQLite for quick and efficient storage and retrieval of vector data.
• It offers multi-modal support.
• It's scalable & lightweight, which makes it seamlessly run on various platforms, including edge devices, local machines, and the cloud.  

13. Dify

Dify is an open-source platform for creating AI applications. It combines Backend-as-a-Service with LLMOps, supporting popular language models and offering an easy-to-use prompt orchestration interface.

Dify includes high-quality RAG engines, a flexible AI agent framework, and a simple low-code workflow, allowing both developers and non-technical users to create innovative AI solutions.

Key Capabilities of Dify

• Dify manages the backend infrastructure so developers can focus on building their applications without worrying about servers.
• It provides clear insights into model performance, user interactions, and application behavior to enhance workflows.
• It seamlessly integrates with third-party APIs, external tools, and popular LLMs, offering flexibility for custom workflows.

14. Ragas

RAGAS is a comprehensive RAG evaluation toolkit that is designed specifically for the evaluation and optimization of applications.

It provides clear metrics and smart test generation features for developers to use when evaluating RAG systems. It also measures how well their retrieval and generation components work. The main benefit of RAGAS is its ability to create data-driven feedback that supports the ongoing improvement of LLM applications through comprehensive evaluation.

RAGAS offers a powerful set of features for evaluation:

• It supports objective evaluation using both LLM-based and traditional metrics.
• It automatically generates diverse test datasets for comprehensive coverage.
• RAGAS integrates seamlessly with LangChain and leading observability tools.
• It offers a visual analytics dashboard for result tracking.
• It allows the training of metrics to align with custom evaluation preferences.
• It provides specialized metrics for context precision, recall, faithfulness, and relevance.

15. JinaAI

Jina AI is an open-source framework for machine learning and artificial intelligence. It is designed for tasks such as neural search, generative AI, and integrating various types of data. Developers can use Jina AI to create scalable search systems, chatbots, and applications that combine information and generation.

Core Features of Jina AI

• It uses deep learning to find documents.
• With multi-modal data support, it works seamlessly with text, images, and audio.
• Using vector database integration, it has built-in support for Jina Embeddings.
• Since it offers both cloud and on-premise support, it can be easily deployed on Kubernetes.

16. Neurite

Neurite is an open-source project that provides a system for creating mind maps. It helps users organize thoughts using a fractal graph structure. This system can be used for AI agents, web links, notes, and code.

The key features of Neurite

• It implements a unique Graph-of-Thought approach using fractal structures for knowledge representation.
• It supports rhizomatic mind-mapping for non-linear, interconnected idea mapping.
• Neurite enables integration with AI agents to enhance knowledge management and retrieval.
• It offers an innovative structure for organizing complex data and information.
• It's an open-source framework that allows for customization and extensibility.

17. Cohere

Cohere provides advanced tools for businesses to create secure and private language model applications. Their platform features top models for generating text, ranking search results, creating embeddings, and augmenting retrieval processes.

Key Features of Cohere

• Cohere offers Command models for chat, summarization, and copywriting.
• The rerank feature in Cohere enables intelligent search.
• The Embed improves classification, clustering, and retrieval.
• Cohere’s AI can be integrated via API, cloud platforms, private cloud, or on-premise.
• It supports fine-tuning, RAG optimization, and privacy-focused deployments.

18. MemO

Mem0 is a smart memory tool that makes RAG applications better by providing contextual memory and ensuring access to up-to-date information, reducing the chances of inaccurate responses. It helps AI learn from user interactions over time. By combining LLMs with special storage, Mem0 allows AI assistants to remember user preferences, conversation history, and important details from different sessions.

Mem0 offers robust features designed to enhance the use of RAG.

• It combines semantic search and graph queries to fetch relevant memories by importance and recency.
• Multi-Level Memory Architecture retains user, session, and agent-level memory for full contextual awareness.
• Through automatic memory processing, it uses LLMs to extract and store critical information from conversations automatically.
• It continuously updates stored data and resolves contradictions to maintain reliability.
• It integrates vector databases for memory and graph databases for relationship tracking.

19. Milvus Vector Databases

Milvus is a fast, cloud-based database that acts like a search engine and helps you quickly find similar vectors. It is important for RAG applications, as it allows you to store and retrieve embedding vectors from text, images, and other unstructured data efficiently. Milvus provides advanced search methods that balance speed and accuracy.

Milvus provides a set of essential features that enhance RAG implementations:

• It has hybrid search capabilities that combine vector search with scalar filtering and full-text search for precise results.
• It uses multiple ANN algorithms for high-performance vector similarity matching.
• Milvus can be seamlessly integrated with LangChain, LlamaIndex, and other leading RAG frameworks.
• It provides enterprise-grade features like access control, monitoring, and data consistency for production use.
• Milvus efficiently scales across distributed clusters to manage billions of vectors.
• It offers multi-modal support.

20. Storm by Standard Oval

STORM is a system that uses AI to help gather information. It was developed by the Stanford Open Virtual Assistant Lab (OVAL). STORM makes research easier by creating detailed reports on different topics, complete with citations.

The core features of Storm by Standard Oval include:

• Perspective-Guided Questioning: Generates questions from diverse viewpoints to deepen and broaden research quality.
• Simulated Expert Conversations: Mimics dialogues between topic experts and writers using real-world data for better contextual understanding.
• Multi-Agent Collaboration: Utilizes a system of AI agents to simulate structured expert discussions with an emphasis on citation and sourcing.
• Automated Research Workflow: Streamlines information gathering and synthesis for efficient, in-depth research.
• Comprehensive Report Generation: Delivers rich, multi-perspective reports by combining question generation and expert simulations.
• Open-Source Flexibility: Customizable and easy to integrate into varied research workflows due to its open-source nature.

21. Cognita

Cognita is an open-source tool for easily building, customizing, and deploying RAG systems that effectively utilize retrieved knowledge. It has a user-friendly interface for testing and supports various RAG setups. You can scale it for production use. It supports open-source embeddings and reranking.

Key Features of Cognita

Central Repository: It keeps parsers, loaders, embedders, and retrievers all in one place.
Incremental Indexing: It enables efficient batch document uploads without requiring re-indexing.
Advanced Retrievals: This includes similarity searches, query breakdown, and document reranking.
Interactive UI: It features an interactive UI that enables non-technical users to upload documents and ask questions easily.
Truefoundry Compatibility: It provides logging, metrics, and feedback for user queries.

22. Verba

Verba is an open-source framework designed to simplify the implementation of RAG systems. Developed by Weaviate, it combines large language models (LLMs) with organizational data to provide accurate, context-aware responses. Verba integrates seamlessly with Weaviate's vector database, allowing businesses to create intelligent systems that retrieve and generate information from their proprietary data.

• It has a conversational UI with autocomplete suggestions for smooth user interaction.
• Verba provides effortless data ingestion that allows easy upload of varied file types without the need for complex scripts.
• It has a modular architecture that enables customization of embeddings, retrieval strategies, and generation methods to suit business needs.
• With its hybrid search functionality, it combines semantic and keyword-based search for highly relevant information retrieval.
• Full CRUD capabilities allow users complete control over their documents.
  Flexible Deployment offers pip install, source builds, and Docker support for various infrastructure setups.

23. Mastra

Mastra is an open-source framework built with TypeScript. It makes it easier to develop AI applications, especially those that use Retrieval-Augmented Generation (RAG). Mastra provides developers with a set of tools to create AI applications efficiently.

Key Features

• It simplifies the RAG pipeline setup with built-in chunking, embedding, storage, retrieval, and reranking.
• Mastra automatically chunks documents into semantically meaningful sections for better context processing.
• It generates text embeddings using models like OpenAI for vector-based retrieval.
• It enables agents to maintain memory across interactions for persistent contextual awareness.
• It allows agents to call external tools or APIs for real-time task execution.
  It provides agents access to structured and unstructured knowledge bases.
• Mastra has built-in observability using OpenTelemetry for monitoring and debugging.
• It offers a unified model for routing across LLMs like OpenAI.
• It allows easy switching between LLMs based on cost, performance, or business needs.

24. Letta

Letta is an open-source framework that helps create AI agents with memory and reasoning skills. Previously called MemGPT, Letta enables LLMs to remember and access information from past sessions, providing a consistent and personalized experience for users.

Core Features of Letta

• Persistent Long-Term Memory allows agents to retain and recall information over time consistently.
• The Advanced Reasoning Engine supports informed decision-making using accumulated knowledge.
• The Agent Development Environment offers a visual interface for building, testing, and debugging agents, along with insights into memory and reasoning.
• Model-agnostic flexibility allows integration with various LLMs, enabling businesses to choose the models that best meet their needs.
• Scalable Cloud Deployment supports large-scale AI agent deployment via REST APIs on Letta Cloud, ensuring reliable performance.

25. Flowise

Flowise is an open-source tool that enables users to create workflows for large language models (LLMs) easily without advanced coding skills. Its drag-and-drop interface allows for straightforward development of RAG pipelines with no coding required. Flowise provides a visual workspace to connect data sources, vector stores, prompt chains, and LLMs into one cohesive workflow.

Core Features of Flowise

• Its multi-LLM support allows working with leading providers like OpenAI, Cohere, Azure OpenAI, and Hugging Face for versatile model testing.
•  With the prebuilt RAG components, developers can use ready-made nodes for document loading, chunking, embeddings, vector storage, prompts, and LLM connections.  
• Through business data integration, it easily connects to files, cloud storage, and databases for accessing proprietary data.  
• Developers can deploy RAG pipelines as APIs for easy integration into systems and applications.  
• It implements OAuth and JWT authentication to secure access and protect sensitive data.  

26. Kernel Memory

Kernel Memory (KM) is a lightweight, flexible RAG framework built in .NET that helps connect LLMs with company data. With KM, you can organize, find, and use information from different sources like files, emails, wikis, and SharePoint. 
This information can then be used in prompts to create accurate and detailed responses. KM prioritizes safety, real-world use, and easy deployment, which is ideal for companies managing sensitive information.

Core Features

• It gathers unstructured data from PDFs, Office docs, websites, GitHub, Azure Storage, and Microsoft 365.  
• With semantic indexing & search, it transforms content into vector embeddings for context-aware searching and retrieval.  
• It provides role-based access to data for compliance with enterprise regulations. KM deploys as a standalone API, embedded in applications, or via Azure Kubernetes Service and Functions.  

Conclusion

As businesses use Retrieval-Augmented Generation (RAG) to create smarter AI applications, picking the right framework is essential. Each open-source RAG framework, like LangGraph or Kernel Memory, or any other, has its own unique advantages for different business needs.

Depending on your unique business needs, like scalability, customization, or easy integration, these frameworks can enhance your AI projects.

For businesses wanting to excel in AI, implementing Retrieval Augmented Generation services can boost your AI projects with increased accuracy, enhanced personalization, and actionable insights from your data. The open-source nature of these frameworks allows for customization and innovation without vendor lock-in, especially when considering training data.