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

When building a RAG pipeline in 2026, framework choice decides iteration speed, API cost, and answer accuracy. This data-driven comparison ranks open-source RAG frameworks by GitHub stars: LangChain leads with 141,874 stars, LlamaIndex follows at 50,875, with 6 more frameworks broken down.

💡 What You Will Learn

When building a RAG pipeline in 2026, framework choice decides iteration speed, API cost, and answer accuracy. This data-driven comparison ranks open-source RAG frameworks by GitHub stars: LangChain l

📜 Table of Contents

• 1. LangChain (141,874 stars)
• 2. LlamaIndex (50,875 stars)
• 3. Haystack by deepset (25,908 stars)
• 4. Chroma (28,799 stars)
• 5. Qdrant (33,309 stars)
• 6. DeepEval (16,885 stars)
• Quick Decision Table

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