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Should I use LangChain or LlamaIndex?

Quick Answer

Use LlamaIndex when your primary need is building a RAG system or data-centric AI application. Use LangChain when you need a general-purpose framework for complex AI workflows, agent systems, or multi-model chains. LlamaIndex excels at data ingestion, indexing, and retrieval. LangChain excels at workflow orchestration and tool integration. Many production systems use both frameworks together.

Summary

Key takeaways

  • LlamaIndex is optimised for data-centric AI applications and RAG systems
  • LangChain provides broader general-purpose AI application building tools
  • LlamaIndex excels at document ingestion, chunking, and retrieval
  • LangChain excels at agent workflows, chains, and tool orchestration

Framework Comparison

LlamaIndex and LangChain solve different core problems. LlamaIndex focuses on connecting AI models with your data. It provides sophisticated data connectors that ingest content from diverse sources, advanced chunking strategies that optimise retrieval quality, multiple index types for different query patterns, and query engines that handle complex retrieval scenarios. It is the best choice when building knowledge bases, document QA systems, and data analysis applications. LangChain provides a general-purpose toolkit for building LLM applications. It offers chain and pipeline abstractions for sequencing operations, agent frameworks for autonomous tool-using AI, integrations with hundreds of tools and services, and memory systems for maintaining conversation context. It is the best choice for chatbots, agent systems, and complex multi-step AI workflows.

Practical Selection Guidance

For a straightforward RAG application that searches your documents and generates answers, LlamaIndex provides the most streamlined development experience. Its data ingestion, indexing, and retrieval capabilities are more sophisticated than LangChain's equivalents. For applications that combine RAG with other capabilities like agents, tool use, or multi-model workflows, LangChain provides the broader framework needed to orchestrate these components. Its agent abstractions and chain composition model handle complex workflows effectively. For production systems, LangGraph (from the LangChain team) offers stateful, graph-based workflow definitions that are increasingly popular for complex applications. Many teams use LlamaIndex for data handling within a broader LangChain or custom orchestration framework, combining the strengths of both.

Related

Related questions

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FAQ

Frequently asked questions

Yes. Many production systems use LlamaIndex for data ingestion and retrieval and LangChain for workflow orchestration. The frameworks are complementary rather than competing, and both have integration points with the other.

Both are used in production by many organisations, though they are evolving rapidly. Be prepared for breaking changes between major versions. Pin your dependencies and test thoroughly when upgrading.

For simple applications, direct API calls to an LLM provider may be sufficient. Frameworks add value as complexity grows: multiple data sources, agent workflows, or sophisticated retrieval strategies. Start simple and add framework capabilities as needed.

Both are widely used in production but are evolving rapidly with frequent breaking changes. Pin your dependencies to specific versions, maintain comprehensive tests, and budget time for updates. Both teams are increasingly focused on production stability.

Haystack provides a production-focused pipeline framework with strong NLP roots. Semantic Kernel from Microsoft integrates well with the Azure ecosystem. Vercel AI SDK is popular for TypeScript applications. Choose based on your language, ecosystem, and specific requirements.

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