15 Best Open Source RAG Framework Selection Guide

1. LangChain - ⭐️105kLangChain is one of the earlier frameworks for building LLM applications and occupies an important position in the RAG ecosystem. It provides a framework for linking components and integrations together to develop AI applications while adapting to evolving technologies. LangChain provides interfaces for model, embedding, and vector storage, providing a structured approach to implementing retrieval-enhanced generation systems.

LangChain contains several functions related to RAG implementation:

• Data Connectivity – Link LLM to various data sources and systems through integration libraries
• Model flexibility – allows switching between different models as needs change
• Integration options – supports a variety of model providers, tools, vector stores and retrievers
• Retrieval component - supports building retrieval pipelines using different strategies
• Evaluation Tools - Provides methods to test and measure the performance of RAG systems Ecosystem Compatibility - Works with LangSmith for debugging and LangGraph for workflow management LangChain can be installed using pip install -U langchain.

2. dify - ⭐️90.5k

Dify is an open source LLM application development platform that combines visual workflow building with powerful RAG capabilities. Its intuitive interface does not require extensive coding, making it easy for developers and non-technical users to use. Dify fully supports document extraction, retrieval, and agent orchestration, providing an end-to-end solution for building production-ready AI applications.

Dify offers a range of features that make it a versatile tool for RAG implementations:

• Visual Workflow Editor - Build and test AI workflows on a visual canvas without writing code
• Broad model support—integrates with hundreds of proprietary and open source LLMs from various vendors
• RAG Pipeline - Processes documents from extraction to retrieval, supporting PDF, PPT and other formats
• Agent Functionality - Create agents using LLM function calls or ReAct, with over 50 built-in tools
• LLMOps - Monitor application logs and performance metrics to improve insights and models
• Backend as a Service - Integrate Dify functionality into existing business applications using APIs
• Enterprise Features - Access SSO, role-based access control, and other organization-centric features Getting started with Dify is easy with Docker Compose. Simply clone the repository, navigate to the docker directory, create an environment file based on the example, and rundocker-compose up -dOnce deployed, visit the dashboard at http://localhost/install to start the initialization process. For detailed installation instructions, visit the official documentation.

3. RAGFlow - ⭐️48.5k RAGFlow is an open source RAG engine built on deep document understanding capabilities. Unlike many other RAG frameworks, it excels at extracting structured information from complex documents such as PDF, including tables, layouts, and visual elements. With its comprehensive document parsing system and intuitive web interface, RAGFlow simplifies the entire process from document extraction to generation.

RAGFlow provides powerful features designed for advanced document-based retrieval:

• Deep Document Understanding - Extract text, tables, and structure from complex documents with high fidelity
• Visual web interface - provides a user-friendly dashboard for document management and RAG workflow creation
• GraphRAG support - create knowledge graphs from documents for more contextual retrieval
• Proxy reasoning - Implement proxy functionality to enable more complex query parsing
• Multiple embedding options - suitable for various embedding models to meet different retrieval needs
• Flexible storage backends - supports Elasticsearch and Infinity for document and vector storage
• Comprehensive API - Provides Python SDK and REST API for integration with other systems. It is easy to get started with RAGFlow using Docker. The system provides a Lite (2GB) and Full (9GB) Docker image, depending on whether you need to embed the model. For detailed installation instructions, see the official documentation and GitHub repository for environment requirements and configuration options.

4. LlamaIndex - ⭐️40.8k

LlamaIndex is a comprehensive data framework designed to connect LLM with private data sources, providing a solid foundation for building RAG applications. It provides a structured approach to data extraction, indexing, and retrieval, simplifying the development of knowledge-enhanced AI systems. With its modular architecture, LlamaIndex bridges the gap between raw data and LLM capabilities, enabling contextual reasoning on custom datasets.

Key features of LlamaIndex include:

• Flexible data connectors – extract data from a variety of sources and formats, including APIs, PDFs, documents, and SQL databases
• Customizable indexing - efficiently structure data using vector storage, keyword indexing, or knowledge graphs
• Advanced search mechanism - implement complex query engine with contextual relevance
• Modular architecture - mix and match components to create custom RAG pipelines
• Multimodal support - process text, images, and other data types in a unified workflow
• Broad integration ecosystem – over 300 integration packages to work with preferred LLM, embedding and vector storage
• Optimization tools - fine-tune retrieval performance through re-ranking and response synthesis techniques. LlamaIndex provides two ways to get started: usingpip install llama-indexStarter packs that include core functionality and common integrations, or start with the core pack for a custom installation ( pip install llama-index-core), and add specific integration packages as needed. Basic use requires only a few lines of code to extract documents, create indexes, and build a query engine to retrieve information from the data.

5. Milvus - ⭐️33.9k

Milvus is a high-performance, cloud-native vector database built for scalable vector similarity search. As a cornerstone technology for RAG applications, it can efficiently store and retrieve embedded vectors generated from text, images, or other unstructured data. Milvus provides optimized search algorithms that strike a balance between speed and accuracy, which is particularly important for production-level RAG systems that process massive amounts of data.

Milvus provides several key features to enhance RAG implementation:

• Advanced Vector Search - supports multiple ANN (Approximate Nearest Neighbor) algorithms to achieve the best vector similarity matching
• Hybrid search capabilities - combining vector similarity with scalar filtering and full-text search
• Horizontal scalability - processing billions of vectors across distributed clusters
• Multimodal support — suitable for embedding text, images, videos, and other data types
• Rich query options - providing distance metrics, search parameters and result filtering
• Seamless Integration - Compatible with LangChain, LlamaIndex and other RAG frameworks
• Enterprise features - including data consistency assurance, access control, and monitoring tools
• Specialized RAG optimization - provides advanced search technologies such as multi-vector search. It is easy to use Milvus with Docker. Only one command ( docker run -d --name milvus -p 19530:19530 -p 9091:9091 milvusdb/milvus:latest) to run a standalone instance and then interact with it using the Python client library. For detailed installation instructions, see the Docker installation guide. The quick start documentation provides code examples for creating collections, inserting vectors, and performing searches, while the RAG tutorial provides end-to-end implementation guidance.

6. mem0 - ⭐️27.3k

Mem0 (pronounced "mem-zero") is an intelligent memory layer designed to enhance RAG applications with persistent contextual memory capabilities. Unlike traditional RAG frameworks that focus primarily on document retrieval, mem0 enables AI systems to actively learn and adapt to user interactions. By combining LLM with dedicated vector storage, mem0 can create AI assistants that can maintain user preferences, conversation history, and important information across multiple sessions.

Mem0 provides powerful features to enhance RAG implementation:

• Multi-level memory architecture - maintains user, session, and agent memory for comprehensive context retention
• Automatic Memory Processing - Use LLM to extract and store important information from conversations
• Memory management - continuously updating stored information and resolving inconsistencies to maintain accuracy
• Dual storage architecture - combining a vector database for in-memory storage with a graph database for relationship tracking
• Intelligent retrieval system - uses semantic search and graph query to find relevant memories based on importance and recency
• Simple API integration - provides easy to use endpoints to add and retrieve memories
• Cross-platform support - for Python and Node.js applications Getting started with mem0 is easy with two main options: a fully managed platform for easy deployment or self-hosting using open source packages. For self-hosting, simplypip install mem0aiPython or Node.js installation and can be initialized with just a few lines of code. The basic implementation requires configuring LLM (GPT-4o-mini is used by default) and implementing memory retrieval and storage functions. The official documentation website npm install mem0ai provides comprehensive documentation, examples, and integration guides.

7. DSPy - ⭐️23k

DSPy is a framework developed by Stanford Natural Language Processing (NLP) for programming (rather than prompting) language models. Unlike traditional RAG tools that rely on fixed prompts, DSPy enables developers to create modular, self-improving retrieval systems through declarative Python code. Its unique approach systematically optimizes prompts and weights in the RAG process, resulting in more reliable and higher-quality output than manual prompt engineering alone.

DSPy provides a powerful set of features to build advanced RAG applications:

• Modular architecture - building composable AI systems using reusable, purpose-built components
• Automatic hint optimization - Leverage optimizers like MIPROv2 to systematically improve hints instead of manually tuning them
• Multiple search integrations - connect to various vector databases, including Milvus, Chroma, FAISS, etc.
• Evaluation Framework - Test and measure RAG system performance using built-in metrics
• The compiler approach - transforming declarative language model calls into self-improving pipelines
• Flexible pipeline design - supports a variety of RAG approaches from basic to multi-hop and complex reasoning Production-ready - tools for debugging, deployment, and observability Easy to get started with DSPy installationpip install dspyThe framework provides a clear programming model for defining the signatures (input/output specifications) and modules (components that implement those signatures) of a RAG system. DSPy's optimization capabilities can automatically improve your implementation based on sample data. For comprehensive documentation and tutorials, visit the official documentation site and check out the RAG-focused tutorial in particular for building your first retrieval-augmented generation system.

8. Haystack - ⭐️20.2k

Haystack is an end-to-end AI orchestration framework designed to build flexible, production-ready LLM applications. It excels at implementing Retrieval Augmented Generation (RAG) by providing a modular component architecture that connects models, vector databases, and file converters into customizable pipelines or agents. Haystack takes a technology-agnostic approach that allows developers to easily switch between different models and databases without rewriting applications, making it ideal for building complex RAG systems that can evolve as needs change.

Haystack provides a powerful set of features to implement advanced RAG solutions:

• Flexible component system - build pipelines by connecting reusable components for document processing, retrieval, and generation
• Technology-agnostic approach - use models from OpenAI, Cohere, Hugging Face, or custom models hosted on various platforms
• Advanced retrieval methods - implementing complex search strategies beyond basic vector similarity
• Document processing - convert, clean and split various file formats for efficient indexing
• Evaluation Framework - Test and benchmark your RAG pipeline to measure performance
• Custom Options - Create custom components when the standard behavior does not meet your requirements
• Visual Pipeline Builder - Design pipelines visually with Deepset Studio integration Haystack is easy to installpip install haystack-aiThe framework provides extensive documentation and guides to help you build your first LLM application in minutes. The installation guide covers multiple methods including Docker, while the getting started guide explains basic pipeline creation. For more advanced use cases, you can explore the cookbook, which contains various RAG-implemented scenarios.

9. LightRAG - ⭐️14.6k

LightRAG is a streamlined retrieval-augmented generation method that focuses on simplicity and performance. As the name suggests, it provides a lightweight implementation that delivers faster and more efficient RAG functionality compared to more complex alternatives. According to the benchmark results shown in the code repository, LightRAG consistently outperforms several other RAG methods across multiple evaluation dimensions, which is particularly valuable for applications that pursue both speed and quality.

LightRAG provides several noteworthy features for effective RAG implementation:

• Performance Optimization – Provides superior results compared to traditional RAG methods in benchmarks
• Simple architecture - Keep the implementation simple, easier to deploy and maintain
• Comprehensive retrieval - good at extracting relevant information from the document context
• Information diversity - Retrieve diverse, representative content, rather than redundant information
• User authorization - providing more efficient access to information
• Web Interface - Contains Web UI components for interactive exploration and use Batch Processing - Efficient insertion and processing of multiple documents Getting started with LightRAG includes installing the package and setting up a document processing pipeline. The code repository provides sample code for extracting context, inserting it into the system, generating queries, and retrieving related information. The code repository contains a complete set of reproducible scripts that demonstrate the core functionality. For more technical details and implementation guidelines, please refer to the GitHub code repository and its associated documentation.

10. LLMWare - ⭐️12.7k

LLMWare is a unified framework designed for building enterprise-scale RAG pipelines using small, purpose-built models, rather than relying solely on monolithic LLMs. This approach provides more efficient and cost-effective RAG implementations, and can often run on standard hardware including laptops. With its comprehensive documentation capabilities and flexible architecture, LLMWare enables organizations to implement production-ready RAG systems that balance performance and resource efficiency.

LLMWare provides a powerful set of features for building specialized RAG applications:

• Efficient model deployment - Leverage smaller, more specialized models that can run on CPUs and edge devices
• Comprehensive document processing - handles a variety of file formats, including PDF, Office documents, text, and Markdown
• Multiple vector database options - integrate with MongoDB, Postgres, SQLite, PG Vector, Redis, Qdrant and Milvus
• Diverse embedding models - supports more than 10 embedding models, including nomic, jina, bge, gte, ember and OpenAI
• Parallel parsing - Efficiently process large document collections through parallel operations
• Dual Search - Use sophisticated query techniques to improve search quality
• Document summaries - Generate document summaries as part of the process
• GPU Acceleration - Utilize GPU resources when model inference is available. Installation of LLMWare is very simple. The framework is in the Getting_Started directorypip install llmwareA collection of example scripts are provided in the repository, demonstrating core functionality such as document parsing, embedding generation, and retrieval. Other examples show how to use specific models such as Qwen2 and how to create a complete RAG pipeline. The repository also contains detailed documentation and quick-start scripts for quickly implementing common workflows.

11. txtai - ⭐️10.7k

txtai is an all-in-one open source embedding database designed to build comprehensive semantic search and language model workflows. Unlike frameworks that focus on retrieval or generation, txtai provides a complete ecosystem for RAG implementation by integrating vector storage, text processing pipelines, and LLM orchestration capabilities into a unified package. Its streamlined API makes it particularly suitable for developers who want to build production-level RAG applications without integrating multiple independent tools.

txtai provides a comprehensive set of features that make it flexible for RAG applications:

• Embedded databases - store, index and search text and documents with semantic understanding
• Pipeline Components – Access pre-built components for summarization, translation, transcription, and more
• LLM Integration - Text generation and completion using various language models
• Workflow Orchestration - Chaining components together to form complex NLP workflows
• Multimodal support - process and analyze text, images, and audio in a unified pipeline
• API and Services Layer - Deployed as REST API services with minimal configuration
• Containerized - Runs in Docker using the provided configuration for scalability
• Cross-platform compatibility - works across different operating systems and environments
• You can easily get started by installing through txtai.

The framework provides rich documentation and examples, including a dedicated notebook for building a RAG pipeline. The example demonstrates how to create embeddings, index documents, and build a complete RAG workflow that combines retrieval with language model generation. txtai also provides a recommended model guide to help users choose the right model for different components based on performance and licensing considerations.

12. RAGAS - ⭐️8.7k

RAGAS is a comprehensive evaluation toolkit designed for evaluating and optimizing RAG applications. Unlike frameworks that focus on building RAG systems, RAGAS provides objective metrics and intelligent test generation capabilities to help developers measure the effectiveness of their retrieval and generation components. Its main advantage is the creation of a data-driven feedback loop to achieve continuous improvement of LLM applications through rigorous evaluation.

RAGAS provides a powerful set of evaluation functions:

• Objective Metrics – Accurately evaluate RAG applications using LLM-based and traditional metrics
• Test data generation - automatically create comprehensive test datasets covering a variety of scenarios
• Seamless integration - works with popular LLM frameworks like LangChain and major observability tools
• Analytics Dashboard - Visualize and analyze assessment results via app.ragas.io
• Metric alignment — training a metric to match a specific evaluation preference and a small number of samples
• Specialized RAG metrics - assessing contextual precision, recall, fidelity, and response relevance
• Multi-framework support - compatible with various LLM models and RAG implementations. You can easily start using RAGAS through app.ragas.io pip install ragasDashboard analysis results. For detailed guidance, see the installation guide, evaluation documentation, and test set generation resources.

13. R2R (From RAG to Riches) - 6.3k

R2R is an advanced AI retrieval system that brings production-ready capabilities to Retrieval Augmentation Generation (RAG) workflows through a comprehensive RESTful API. Unlike many RAG frameworks that focus primarily on basic document retrieval, R2R incorporates agent reasoning capabilities in its Deep Research API, which can perform multi-step reasoning by fetching relevant data from knowledge bases and external sources. This combination of traditional RAG and intelligent agent behavior makes it particularly powerful in complex query parsing that requires detailed understanding.

R2R offers an impressive set of features designed for production deployments:

• Multimodal ingestion – handles multiple content formats including text, PDF, images and audio files
• Hybrid search capabilities – combining semantic and keyword search with mutual ranking fusion for better relevance
• Knowledge graph integration - automatically extract entities and relationships to build contextual knowledge graphs
• Agent Reasoning - Using Deep Research Agents to Perform Complex, Multi-Step Information Gathering and Synthesis
• Production infrastructure – including user authentication, collection management, and full API access
• Multiple deployment options - available as a cloud service or as a self-hosted solution with Docker support
• Client SDK - Provides Python and JavaScript libraries to simplify integration There are two main ways to get started with R2R: using SciPhi Cloud's hosted deployment (with a generous free tier, no credit card required) or self-hosting. For the fastest self-hosted setup, you can install R2R with pip ( pip install r2r), set your OpenAI API key, and runpython -m r2r.servefor lightweight deployments. Alternatively, Docker Compose provides a full-featured deploymentdocker compose -f compose.full.yaml --profile postgres up -dThe Python SDK provides intuitive document manipulation, search, and proxy RAG query methods through a simple client interface.

14. Ragatouille - ⭐️3.4k

Ragatouille is a framework that implements late interactive retrieval methods for RAG applications based on ColBERT. Unlike traditional dense retrieval using a single vector representation, Ragatouille retains token-level information during the matching process, thereby improving retrieval accuracy. This approach bridges the gap between advanced information retrieval research and practical RAG implementations, providing excellent search quality without excessive computational requirements.

Ragatouille provides several key features to enhance retrieval:

• Late Interactive Retrieval - More precise document retrieval using ColBERT’s token-level matching
• Fine-tuning capabilities - support training on domain-specific data without explicit annotations
• Metadata support - maintain document metadata throughout the indexing and retrieval process
• Flexible document handling - provides utilities for document processing and management
• Multi-query processing - efficient processing of batch queries
• Disk-based indexing - creates compressed indexes that can be easily integrated with production systems
• Integration options - works with Vespa, Intel FastRAG, and LlamaIndex Getting started with Ragatouille is a simple pip install. The documentation provides a comprehensive implementation guide, and the examples directory contains notebooks for various use cases. Particularly useful is the annotation-free fine-tuning example, which demonstrates how to train domain-specific models using synthetic query generation with Instructor embeddings.

15. FlashRAG - ⭐️2.1k

FlashRAG is a Python toolkit for retrieval augmentation generation (RAG) research, which provides 36 pre-processed benchmark datasets and 17 state-of-the-art RAG algorithms in a unified interface. Unlike implementation-focused frameworks, FlashRAG prioritizes reproducibility and experimentation, enabling researchers to quickly reproduce existing work or develop new methods without the overhead of data preparation and baseline implementation.

FlashRAG provides several key capabilities for research:

• Extensive dataset support - Access 36 pre-processed benchmark RAG datasets covering everything from question answering to entity linking
• Algorithm Implementation - 17 state-of-the-art RAG methods implemented through a consistent interface
• Modular architecture - easily swap out different retrievers, generators, and other components
• Web Interface - Intuitive UI for interactive experimentation and visualization
• Comprehensive documentation - detailed instructions for repeating experiments
• Performance benchmarks - ready-to-use evaluation metrics and comparisons
• Multimodal capabilities — support for text, images, and other modalities in RAG pipelines To get started with FlashRAG, you need to install the package via pip and explore its components. The toolkit provides examples of using different retrievers and building RAG pipelines in its GitHub repository. Preprocessed datasets are available through Hugging Face, and the web UI simplifies experimentation without writing code.

Comparative decision table for choosing the right RAG framework

Selection criteria

Easy to implement: Choose Dify, LlamaIndex, mem0, LightRAG, or txtai Document-intensive applications: Consider RAGFlow or LLMWare Production-scale: Refer to Milvus, Haystack, or LangChain Limited hardware resources: Prioritize LLMWare or LightRAG Complex reasoning needs: Explore R2R or DSPy Evaluation focus: Use RAGAS Research purpose: Choose FlashRAG

in conclusion

In 2025, the landscape of RAG frameworks has changed significantly, with various solutions covering all aspects of the RAG process, from document ingestion to retrieval, generation, and evaluation. When choosing a framework, consider your specific use case needs, technical expertise, and deployment constraints. Some frameworks, such as LangChain and LlamaIndex, provide comprehensive end-to-end solutions, while others, such as Ragatouille and FlashRAG, excel in specific areas such as advanced retrieval techniques or research experiments. Your choice should align with the scale, performance needs, and development timeline of your application.