Overview
Compare LangChain and LlamaIndex across RAG, data connectors, indexing, retrieval, agent capabilities, and application development.
Python · MIT
LangChain is a framework for building applications powered by language models. It provides core capabilities such as chaining, memory management, and agent orchestration, making it a go-to choice for AI agent development.
Python · MIT
LlamaIndex is a data framework that provides the data connection layer for LLM applications, with strong RAG capabilities across diverse data sources and vector databases.
| Best for | LangChain | LlamaIndex |
|---|---|---|
| Core strength | General-purpose LLM application orchestration and agent ecosystem, suitable for end-to-end application development | Strong data framework and RAG retrieval capabilities, suitable for knowledge bases and enterprise data Q&A |
| RAG capability | Covers the full RAG workflow through combinations of ecosystem components | Focuses on indexing, retrieval, query engines, and data connectors |
| Agent capability | Rich agent abstractions and tool ecosystem | Supports agents, but is more oriented toward data- and retrieval-driven applications |
| Metric | LangChain | LlamaIndex |
|---|---|---|
| Stars | 136.5k | 49.3k |
| Forks | 22.6k | 7.4k |
| Language | Python | Python |
| License | MIT | MIT |
| Last commit | May 11, 2026 | May 11, 2026 |
Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.