LangChain vs LlamaIndex

Both want to be the framework for AI apps. LangChain does everything. LlamaIndex does RAG really well. Choose your complexity.

The AI Framework Wars

Building on LLMs requires orchestration: prompts, chains, retrieval, memory, tools. LangChain and LlamaIndex both provide this, but with very different philosophies.

LangChain: 'Here's an abstraction for everything.' LlamaIndex: 'Here's the best way to connect your data to LLMs.'

LlamaIndex for RAG

If you're building retrieval-augmented generation (and you probably are), LlamaIndex is purpose-built for it.

Document loaders, text splitters, embedding pipelines, vector store integrations, query engines — the RAG pipeline is a first-class citizen. The abstractions make sense because they map to a well-understood problem.

The managed service (LlamaCloud) handles parsing complex documents like PDFs and tables, which is genuinely hard to do well.

LangChain for Everything Else

LangChain's scope is broader: agents, chains, tools, callbacks, memory, streaming. If you need to build an agent that uses tools, searches the web, and maintains conversation history, LangChain has abstractions for all of it.

LangSmith for observability is excellent. Trace your chains, debug failures, evaluate outputs. The tooling around LangChain is mature.

But the breadth is also the problem. Too many abstractions, too many ways to do the same thing, too many breaking changes between versions.

Quick Comparison

Factor	LangChain	LlamaIndex
RAG Pipeline	Good	Excellent (core focus)
Agent Framework	Comprehensive	Basic
Abstraction Level	Many layers	Focused, fewer layers
Document Parsing	Basic loaders	LlamaParse (advanced)
Observability	LangSmith (excellent)	Basic
Learning Curve	Steep	Moderate
API Stability	Breaking changes common	More stable

The Verdict

Use LangChain if: You're building complex agents with tools, need LangSmith observability, or want the broadest framework for any AI app pattern.

Use LlamaIndex if: You're building RAG applications, want focused abstractions that make sense, or need advanced document parsing.

Consider: Many teams use both: LlamaIndex for the data/retrieval layer and LangChain for the agent/orchestration layer. They're not mutually exclusive.

The Bottom Line

LlamaIndex wins

LlamaIndex is focused, well-designed for RAG, and doesn't try to be everything. LangChain is powerful but the abstraction layers can feel like fighting the framework. For most AI apps, LlamaIndex gets you to production faster.

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