LangChain vs LlamaIndex
A side-by-side comparison of capabilities, autonomy, integrations, and pricing to help you choose.
Short answer: choose LangChain if you want open-source framework and platform for building and deploying llm agents (Supervised agent, freemium); choose LlamaIndex if you want open-source data framework for rag pipelines and data-grounded agents (Supervised agent, freemium).
| LangChain | LlamaIndex | |
|---|---|---|
| What it is | Open-source framework and platform for building and deploying LLM agents | Open-source data framework for RAG pipelines and data-grounded agents |
| Type | framework | framework |
| Autonomy | Supervised agent | Supervised agent |
| Pricing | freemium · Framework free (MIT); LangSmith free Developer tier | freemium · Framework free (MIT); LlamaCloud has a free tier |
| Best for | developers, enterprise, mid-market | developers, enterprise, mid-market |
| Deployment | self-hosted, api, saas | self-hosted, api, saas |
| Modalities | text, code, api | text, code, api |
| Models | model-agnostic, gpt, claude, gemini, llama, open-source | model-agnostic, gpt, claude, open-source |
| Protocols | function-calling, mcp, rest-api | function-calling, mcp, rest-api |
| Integrations | OpenAI, Anthropic, Google, AWS Bedrock, Pinecone, Hugging Face | OpenAI, Anthropic, Pinecone, Qdrant, AWS Bedrock, Hugging Face |
| Capabilities | 4 documented | 4 documented |
LangChain
- +Largest open-source LLM/agent framework community with very broad integration coverage
- +Model-agnostic design future-proofs apps against LLM churn
- +LangGraph adds production-grade primitives (durability, checkpointing, human-in-the-loop) that bare API calls lack
- -Frequently criticized for heavy abstractions and churn between API versions; debugging deep chains can be painful
- -Most production value (observability, deploy) lives in the paid LangSmith platform
LlamaIndex
- +Best-in-class data and retrieval primitives (readers, indexes, retrievers, query engines) for grounding agents in your own data
- +Event-driven Workflows orchestrate multi-step agent processes with reflection and error-correction
- +Open source and model-agnostic, with LlamaCloud for managed document parsing and indexing
- -Framework, not a product: autonomy and quality depend entirely on what the developer builds
- -More oriented to data/RAG than to complex multi-agent orchestration compared with some peers
Which should you choose?
LangChain is open-source framework and platform for building and deploying llm agents, best for developers, enterprise, mid-market. LlamaIndex is open-source data framework for rag pipelines and data-grounded agents, best for developers, enterprise, mid-market. The right choice depends on the autonomy level you want, your existing integrations, and your budget, all compared above.
This comparison is generated from the sourced LangChain and LlamaIndex profiles. Open either profile to review its evidence and last-reviewed date.