LangChain vs LlamaIndex
Both are Python/JS frameworks for LLM apps. LangChain leans agents; LlamaIndex leans RAG.
/ quick answer
The two most-used LLM frameworks converge over time but each still has a philosophical center: LangChain wraps 'anything an LLM does' as chains and graphs; LlamaIndex specializes in getting the right data into the LLM. Both are Python/JS frameworks for LLM apps. LangChain leans agents; LlamaIndex leans RAG.
| Dimension | Option A | Option B |
|---|---|---|
| Sweet spot | LangChain: agent orchestration (LangGraph) | LlamaIndex: RAG pipelines |
| Data loaders | Many, community-maintained | Deeper, first-class (LlamaHub) |
| Observability | LangSmith (excellent) | Third-party (Arize, LangFuse) |
| API stability | Historically churny | More stable since v0.10+ |
| Community | Largest LLM framework | Strong, RAG-focused |
- →LangChain for multi-step agents, tool routing, stateful graphs.
- →LlamaIndex for advanced RAG (query engines, sub-question, graph indexes).
/ frequently asked
What is the difference in LangChain vs LlamaIndex?
The two most-used LLM frameworks converge over time but each still has a philosophical center: LangChain wraps 'anything an LLM does' as chains and graphs; LlamaIndex specializes in getting the right data into the LLM.
What are the main points of comparison?
Sweet spot: LangChain: agent orchestration (LangGraph) vs LlamaIndex: RAG pipelines · Data loaders: Many, community-maintained vs Deeper, first-class (LlamaHub) · Observability: LangSmith (excellent) vs Third-party (Arize, LangFuse) · API stability: Historically churny vs More stable since v0.10+ · Community: Largest LLM framework vs Strong, RAG-focused
Which one should I choose?
Pick LlamaIndex if the app is 'answer questions over my data'. Pick LangGraph if it's 'agent that does things'. It's fine to use both.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Agent
An autonomous AI system that plans and executes multi-step tasks.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →Prompt Chaining
Pipelining LLM calls where each step's output feeds the next.
- →LLM Orchestration
Coordinating multiple model calls, tools, and data sources into one reliable system.
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Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →Agent Research Stack
Web-search-enabled agent for autonomous research tasks.
- →AI Voice Agent Development Stack
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- →A2A Orchestration Stack
This stack comprises the essential tools and frameworks for designing, deploying, and managing Agent-to-Agent (A2A) communication protocols and multi-agent system orchestration. It provides the infrastructure for complex AI agent collaboration.
Comparisons & alternatives
Pick between the options.
- →LangChain vs Vercel AI SDK
Kitchen-sink orchestration vs lean SDK for shipping apps.