Top Open Source RAG Frameworks on GitHub in 2026: 8 Tools Compared

If you are building a RAG pipeline in 2026, your choice of framework determines everything -- how fast you iterate, how expensive your API calls are, and whether your app actually returns accurate results. Here is a data-driven comparison of the top open source RAG frameworks on GitHub. ## 1. LangChain (141,874 stars) LangChain remains the most popular agent and RAG framework. It provides chain-of-thought orchestration, built-in document loaders for 100+ formats, and native integration with every major LLM provider. Best for: Developers who need maximum flexibility. Real-world setup: LangChain RetrievalQA chain takes about 20 lines of code to go from PDF to question-answering. The 2026 version added native MCP support. ## 2. LlamaIndex (50,875 stars) Originally a data indexing tool, LlamaIndex has evolved into a full document agent and OCR platform. Its 2026 release focuses on agent-driven RAG. Best for: Document-heavy use cases (PDFs, databases, APIs). Standout feature: Router Query Engine automatically selects the right retriever for each question. ## 3. Haystack by deepset (25,908 stars) Haystack is the most enterprise-ready framework. It comes with pre-built pipelines for indexing, retrieval, and QA, plus a REST API out of the box. Best for: Production deployments where reliability matters most. ## 4. Chroma (28,799 stars) Chroma is the most developer-friendly vector database. It pairs perfectly with any framework above. Best for: Teams that want a simple, embed-and-search experience. ## 5. Qdrant (33,309 stars) Qdrant is the high-performance vector database for production RAG. Written in Rust, it handles billions of vectors with sub-10ms latency. Best for: Scale. If you have more than 10M documents, Qdrant is the right choice. ## 6. DeepEval (16,885 stars) DeepEval is the most important evaluation tool for RAG pipelines. It measures answer relevancy, faithfulness, context precision, and hallucination rate. ## Quick Decision Table | Framework | Stars | Best Use Case | |-----------|-------|---------------| | LangChain | 141k | Maximum flexibility | | LlamaIndex | 50k | Document-heavy RAG | | Haystack | 25k | Production pipelines | | Chroma | 28k | Vector storage | | Qdrant | 33k | High-scale retrieval | | DeepEval | 16k | RAG quality testing | ## How to Choose 1. Prototyping fast? Use Chroma + LangChain (2-day setup) 2. Production at scale? Use Qdrant + Haystack (battle-tested) 3. Document search? Use LlamaIndex (best indexing) 4. Need evaluation? Add DeepEval to any stack All data sourced from GitHub API on 2026-07-16.