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Comparison

LangChain vs LlamaIndex

Compare LangChain and LlamaIndex for RAG, agents, tools, data connectors, and production LLM application development.

Quick verdict

Use LangChain for broad app orchestration and integrations. Use LlamaIndex when data ingestion and retrieval are central.

Choose which

Choose LangChain for tool orchestration, agents, and integration breadth.

Choose LlamaIndex for RAG-heavy apps and data connectors.

Feature table

Main strengthIntegration breadthData/RAG workflows
Agent patternsStrongGood
Retrieval focusGoodStrong

Recommendation

Pick the framework that matches the hardest part of your app. If retrieval is the product, start with LlamaIndex. If orchestration is the product, start with LangChain.

Setup difficulty

Both are intermediate.

Best use cases

  • RAG apps
  • Agents
  • Tool use
  • Data-connected AI applications

Limitations

  • Frameworks do not replace good evals, simple architecture, or source grounding

Related links

/tools/langchain//tools/llamaindex//guides/how-to-evaluate-local-models-production/

FAQ

Can I use both?

Yes, but avoid unnecessary complexity. Start with one unless there is a clear reason to combine them.

Sources

LangChain GitHubLlamaIndex GitHub

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