LangChain vs LlamaIndex (2025)

LangChain for general agent pipelines. LlamaIndex for anything document and retrieval heavy.

Both are Python-first open-source frameworks that ship fast and change faster. The real question is whether your problem is primarily an agent orchestration problem or a data ingestion and retrieval problem.

	LangChain	LlamaIndex
Primary strength	Agent orchestration, chains, tool use, broad LLM integrations.	Document ingestion, chunking, indexing, RAG pipelines.
RAG quality	Works. Not the deepest tooling.	Best-in-class. Dozens of chunking, retrieval, and rerank strategies.
Agent support	LangGraph is excellent for stateful multi-agent flows.	Improving. Workflows are solid but lighter than LangGraph.
Learning curve	Steep. Abstractions are deep and change frequently.	Moderate. More focused surface area.
Observability	LangSmith is excellent for tracing and eval.	Built-in callbacks; pairs well with Arize/Phoenix.
Community & ecosystem	Larger. More third-party integrations.	Smaller but very active, especially in enterprise RAG.
Stability	Has broken APIs multiple times between major versions.	More stable surface area.

Pick LangChain when

Use LangChain/LangGraph when: you are building multi-agent workflows, need a broad set of tool integrations, or want LangSmith for evaluation.

Pick LlamaIndex when

Use LlamaIndex when: retrieval quality and document pipeline sophistication are the core of your product.

Bottom line

Many production teams use both: LlamaIndex to build and serve the retrieval layer, LangGraph to orchestrate the agent logic on top. They are not mutually exclusive.

Need help picking — or stitching them together?

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