Dify is the top RAG framework by GitHub stars, with 160,628 on 7 November 2026, followed by LangChain with 149,172 and RAGFlow with 93,730. Dify and RAGFlow are full RAG apps; LangChain, LlamaIndex and Haystack are code libraries. Every pick has open-source code, and Dify Cloud is $590 per workspace a year.
3,974 on the AnswerDotAI/RAGatouille repository, checked 7 November 2026
Teams that want ColBERT retrieval in a RAG pipeline without learning the research details
Pick by what you want
Most GitHub stars
Dify
Most retrieval building blocks
LangChain
Best for messy documents
RAGFlow
Best for graph-based RAG
LightRAG
Best for explicit pipelines
Haystack
Best for ColBERT retrieval
RAGatouille
The ranking
1
Dify
Answrs VerdictMost GitHub stars here, but it is a full app platform rather than a RAG library.
GitHub stars: 160,628 on the langgenius/dify repository, checked 7 November 2026Price: Dify Cloud Professional is $590 per workspace a year and Team is $1590 per workspace a year; the Community Edition is self-hostedKnowledge limits: Professional includes 500 knowledge documents and 5GB of knowledge data storage; Team includes 1,000 documents and 20GBRAG pipeline: Its README lists a RAG pipeline with extensive capabilities from document ingestion to retrieval, with out-of-box text extraction from PDFs, PPTs and other common document formatsLicense: A modified version of the Apache License 2.0 with extra conditions on multi-tenant use and on removing the logo or copyright in its frontend
Dify has 160,628 GitHub stars. Dify Cloud Professional is $590 per workspace a year with 500 knowledge documents and 5GB of knowledge storage; Team is $1590 a year with 1,000 documents and 20GB. Its README lists a RAG pipeline covering ingestion to retrieval with text extraction from PDFs and PPTs. Its license is a modified Apache 2.0 with conditions on multi-tenant use and on its frontend logo.
Go ifTeams that want a visual workspace with a RAG pipeline, workflows and agents in one app
Skip ifYou need a plain Apache-2.0 library, or plan to run a multi-tenant service from the source
Why #1 over #2: It has more stars than LangChain, but it is an app platform and carries license conditions LangChain does not.
2
LangChain
Answrs VerdictThe widest set of retrieval building blocks, documented in its own retrieval guide.
GitHub stars: 149,172 on the langchain-ai/langchain repository, checked 7 November 2026Price: Free and open source under the MIT licenseRetrieval blocks: Its retrieval docs list document loaders, text splitters, embedding models, vector stores and retrievers, each modular so you can swap them without rewriting the appRAG architectures: The docs describe 2-Step RAG, Agentic RAG and a Hybrid approach with validation stepsEcosystem: The README points to LangGraph for controllable agent workflows and LangSmith for evals, observability and debugging
LangChain has 149,172 GitHub stars and is MIT-licensed. Its retrieval docs list document loaders, text splitters, embedding models, vector stores and retrievers, each swappable, and describe 2-Step RAG, Agentic RAG and Hybrid RAG. The README points to LangGraph for agents and LangSmith for evals and observability.
Go ifTeams that want swappable loaders, splitters, vector stores and retrievers plus agent tooling from one ecosystem
Skip ifYou want a purpose-built RAG engine with a UI
Why #2 over #3: It documents more modular retrieval building blocks than RAGFlow, which is a self-contained engine.
3
RAGFlow
Answrs VerdictA dedicated RAG engine with explainable chunking and traceable citations.
GitHub stars: 93,730 on the infiniflow/ragflow repository, checked 7 November 2026Price: Open source under the Apache-2.0 license; a hosted cloud service is offered at cloud.ragflow.io, with no price on the repository pageChunking: Template-based chunking, with visualization of text chunking so a person can step inRetrieval: Agentic retrieval with Low, Medium, High or Ultra thinking modes, plus multiple recall paired with fused re-rankingData formats: Supports Word documents, PowerPoint presentations, Excel spreadsheets, TXT files, images, PDFs, scanned copies, structured data and web pages
RAGFlow has 93,730 GitHub stars and an Apache-2.0 license, with a hosted cloud service whose price the repository page does not give. It offers template-based chunking with visualization, agentic retrieval in Low, Medium, High and Ultra thinking modes, and multiple recall with fused re-ranking. It handles Word, PowerPoint, Excel, PDFs, scanned copies and web pages.
Go ifTeams ingesting messy documents that want visible chunking and cited answers
Skip ifYou want a lightweight Python library rather than a Docker-deployed engine
Why #3 over #4: It has more stars than LlamaIndex and ships chunking visualization that LlamaIndex does not list.
4
LlamaIndex
Answrs VerdictStrong data and index tooling, with a README that discloses its focus shift.
GitHub stars: 52,842 on the run-llama/llama_index repository, checked 7 November 2026Price: Free and open source under the MIT license; LlamaParse is a separate enterprise platform and its pricing is not given on the repository pageData framework: It provides data connectors, ways to structure data as indices and graphs, and a retrieval and query interface over your dataCustomization: Lower-level APIs let you customize data connectors, indices, retrievers, query engines and reranking modulesFocus shift: Its README says the current focus is document parsing and extraction through LlamaParse, while the OSS framework remains available as an open toolkit
LlamaIndex has 52,842 GitHub stars and an MIT license. It provides data connectors, indices and graphs, and a retrieval and query interface, with lower-level APIs for retrievers, query engines and reranking modules. Its README says the maker's current focus is LlamaParse, with the OSS framework kept available as an open toolkit.
Go ifDevelopers who want to ingest, index and query their own data with a five-line start and deep customization
Skip ifYou want the maker's main focus to be the framework, which its README says has shifted to LlamaParse
Why #4 over #5: It has more stars than LightRAG, though its README says its maker's focus has moved to LlamaParse.
5
LightRAG
Answrs VerdictGraph-based RAG with the broadest storage-backend list in this ranking.
GitHub stars: 40,597 on the HKUDS/LightRAG repository, checked 7 November 2026Price: Free and open source under the MIT licenseStorage: Its news lists PostgreSQL, MongoDB, Neo4J and OpenSearch as storage backendsReranker: Reranker support was added in August 2025 and is the default for mixed queriesChunking: Four selectable text chunking strategies: Fix, Recursive, Vector and Paragraph
LightRAG has 40,597 GitHub stars and an MIT license. Its news lists PostgreSQL, MongoDB, Neo4J and OpenSearch storage backends, reranker support that became the default for mixed queries in August 2025, and four chunking strategies: Fix, Recursive, Vector and Paragraph.
Go ifTeams that want graph-based RAG with a choice of database backends
Skip ifYou need a hosted service with a published price
Why #5 over #6: It has more stars than DSPy and lists storage backends and rerankers that DSPy does not.
6
DSPy
Answrs VerdictOptimizes the pipeline itself, but RAG is one of several uses.
GitHub stars: 38,969 on the stanfordnlp/dspy repository, checked 7 November 2026Price: Free and open source under the MIT license, installed with pip install dspyApproach: Described as the framework for programming rather than prompting language models, with algorithms for optimizing their prompts and weightsRAG use: Its README names sophisticated RAG pipelines among the systems you can build
DSPy has 38,969 GitHub stars and an MIT license, installed with pip install dspy. It is described as a framework for programming rather than prompting language models, with algorithms that optimize prompts and weights, and its README names sophisticated RAG pipelines among its uses.
Go ifTeams that want to optimize RAG prompts and weights in code rather than hand-tune prompts
Skip ifYou want ready-made loaders, a UI or a vector store integration list
Why #6 over #7: It has more stars than Microsoft GraphRAG and is not in maintenance mode.
7
Microsoft GraphRAG
Answrs VerdictInfluential graph approach, but the README says it is in maintenance mode.
GitHub stars: 36,590 on the microsoft/graphrag repository, checked 7 November 2026Price: Free and open source under the MIT licenseMethod: A data pipeline and transformation suite that uses LLMs to extract structured data from unstructured text, using knowledge graph memory structuresStatus: Its README says the project is largely in maintenance mode and will not accept new PRs or implement new features, only bug fixes and dependency updatesCost warning: The README warns that GraphRAG indexing can be an expensive operation and advises starting small
Microsoft GraphRAG has 36,590 GitHub stars and an MIT license. It is a pipeline that uses LLMs to extract structured data from unstructured text into knowledge graph structures. Its README says the project is largely in maintenance mode with only bug fixes and dependency updates, and warns that indexing can be expensive.
Go ifResearchers and teams exploring graph-based context for question answering over private text
Skip ifYou need a project that is still adding features
Why #7 over #8: It has more stars than Haystack, but Haystack is not in maintenance mode.
8
Haystack
Answrs VerdictThe clearest explicit control over retrieval, ranking and routing steps.
GitHub stars: 26,942 on the deepset-ai/haystack repository, checked 7 November 2026Price: Free and open source under the Apache-2.0 license, installed with pip install haystack-ai; Haystack Enterprise Starter offers paid support and platform, with no price on the repository pagePipelines: Modular pipelines with explicit control over how information is retrieved, ranked, filtered, combined and routed, including loops, branches and conditional logicVendor support: Integrates with OpenAI, Mistral, Anthropic, Cohere, Hugging Face, Google, Azure OpenAI, AWS Bedrock and local modelsServing: Hayhooks wraps pipelines and agents as REST APIs or MCP servers
Haystack has 26,942 GitHub stars and an Apache-2.0 license, installed with pip install haystack-ai. Its pipelines give explicit control over retrieval, ranking, filtering and routing, with loops and branches, and integrate with OpenAI, Mistral, Anthropic, Cohere, Hugging Face, Google, Azure OpenAI and AWS Bedrock. Haystack Enterprise Starter offers paid support, with no price listed.
Go ifTeams that want transparent, branching retrieval pipelines they can trace and deploy as APIs
Skip ifYou want a point-and-click app instead of Python pipelines
Why #8 over #9: It has more stars than txtai and lists vendor integrations and a REST and MCP serving option.
9
txtai
Answrs VerdictBundles the vector index, graph and RAG in one library.
GitHub stars: 13,094 on the neuml/txtai repository, checked 7 November 2026Price: Free and open source under the Apache-2.0 license, installed with pip install txtaiCore: Its key component is an embeddings database, a union of sparse and dense vector indexes, graph networks and relational databasesRAG: Its README explains RAG as constraining LLM output with a knowledge base as context, and links a RAG Quickstart Example
txtai has 13,094 GitHub stars and an Apache-2.0 license, installed with pip install txtai. Its key component is an embeddings database that unites sparse and dense vector indexes, graph networks and relational databases, and its README links a RAG Quickstart Example.
Go ifDevelopers who want one library for the embeddings database, semantic search and RAG
Skip ifYou want the largest ecosystem of third-party integrations
Why #9 over #10: It has more stars than RAGatouille and covers more of the pipeline than ColBERT retrieval alone.
10
RAGatouille
Answrs VerdictNarrow ColBERT focus and the fewest stars and least recent activity here.
GitHub stars: 3,974 on the AnswerDotAI/RAGatouille repository, checked 7 November 2026Price: Free and open source under the Apache-2.0 license, installed with pip install ragatouilleFocus: Makes ColBERT late-interaction retrieval easy to use, train and fine-tune inside any RAG pipelineLimits: Its README says Windows is not supported outside WSLActivity: The repository was last pushed on 17 May 2025, the oldest of the ten
RAGatouille has 3,974 GitHub stars and an Apache-2.0 license, installed with pip install ragatouille. It makes ColBERT late-interaction retrieval easy to train and use in a RAG pipeline. Its README says Windows is not supported outside WSL, and the repository was last pushed on 17 May 2025.
Go ifTeams that want ColBERT retrieval in a RAG pipeline without learning the research details
Skip ifYou run Windows without WSL or need a frequently updated project
How we ranked these
We ranked RAG frameworks by GitHub stars on each project's own repository as of 7 November 2026, a public and checkable measure of adoption. Stars do not measure answer quality, so each pick also lists its license, published price and documented RAG features. Vector databases such as Chroma, Weaviate, Qdrant and Milvus are not frameworks and are left out; the Cohere Toolkit repository is archived on GitHub. Facts are taken from each maker's own published information; check the official site before you buy.
Questions people ask
What is the best RAG framework?
By GitHub stars, Dify leads with 160,628, then LangChain with 149,172 and RAGFlow with 93,730. Dify is a full app platform; LangChain and Haystack are code libraries.
Are these RAG frameworks free?
All ten have open-source code. LangChain, LightRAG, DSPy and Microsoft GraphRAG use the MIT license; RAGFlow, Haystack, txtai and RAGatouille use Apache-2.0; Dify uses a modified Apache 2.0 license. Dify Cloud starts at $590 per workspace a year.
Which RAG framework is best for graph-based retrieval?
LightRAG and Microsoft GraphRAG are the graph-based picks. GraphRAG's README says it is largely in maintenance mode, while LightRAG's news lists Neo4J, PostgreSQL, MongoDB and OpenSearch backends.
What is the difference between LangChain and LlamaIndex for RAG?
LangChain documents modular loaders, splitters, embeddings, vector stores and retrievers plus 2-Step, Agentic and Hybrid RAG. LlamaIndex documents data connectors, indices and a query interface, and its README says its main focus is now LlamaParse.
GitHub stars on the project repository, 7 November 2026 at a glance
RAGatouille3,974 stars
txtai13,094 stars
Haystack26,942 stars
Microsoft GraphRAG36,590 stars
DSPy38,969 stars
LightRAG40,597 stars
LlamaIndex52,842 stars
RAGFlow93,730 stars
LangChain149,172 stars
Dify160,628 stars
GitHub stars on the project repository, 7 November 2026, as published by each maker. Picks without a published figure are left off.
