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.