LangChain vs LlamaIndex

A detailed comparison to help you choose the right tool for your use case.

L

LangChain

Framework

The most popular framework for building LLM-powered applications

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L

LlamaIndex

Framework

Data framework for connecting custom data sources to LLMs

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Feature
LangChain
LlamaIndex
Primary Focus
General LLM applications & agents
Data indexing & RAG
Agent Support
Comprehensive (LangGraph)
Basic agent capabilities
Data Connectors
Moderate
Extensive (160+ loaders)
Learning Curve
Steep — many abstractions
Moderate — focused API
Community Size
Very large (95K+ stars)
Large (37K+ stars)
Production Readiness
High (LangSmith observability)
High (LlamaCloud)
TypeScript Support
Full (LangChain.js)
Available (TypeScript SDK)
Streaming Support
Built-in
Built-in

LangChain

Strengths

  • Massive ecosystem & integrations
  • Comprehensive documentation
  • Active community

Limitations

  • Heavy abstraction layer
  • Breaking changes between versions
  • Can be overkill for simple use cases

LlamaIndex

Strengths

  • Best-in-class data ingestion
  • Excellent RAG capabilities
  • Clean API design

Limitations

  • Less flexible for non-RAG use cases
  • Smaller agent ecosystem
  • Python-first

Verdict

LangChain is better for complex agent systems and diverse LLM workflows. LlamaIndex wins for RAG-focused applications and data-heavy use cases. Many teams use both together.

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