Question 1

LlamaIndex vs LangChain — which is better?

Accepted Answer

It depends on what you're optimizing for. LangChain edges LlamaIndex on our editor rating (4.4 vs 4.3), but ratings are a coarse signal. The verdict above breaks down which one wins for budget, feature breadth, and self-hosting.

Question 2

Are these tools free?

Accepted Answer

Yes — every tool here has a free or freemium tier. The differences are in usage limits, advanced features, and how aggressive each free tier is.

Question 3

When should I pick LlamaIndex over LangChain?

Accepted Answer

Pick LlamaIndex when enterprise knowledge bases matters more than LangChain's strengths in building rag applications. The "best for" callouts above translate this into concrete personas.

Question 4

Are there other tools to consider?

Accepted Answer

Yes — every tool in this comparison has its own alternatives page that ranks the closest competitors. Click any tool name to drill into its full review and alternatives list.

	LlamaIndex The data framework for LLM apps — connect your data to AI with powerful indexing and retrieval.	LangChain The most popular framework for building LLM applications — chains, agents, and RAG made easy.
Rating	4.3	4.4
Pricing	Open source	Open source
Category	AI Agents	AI Agents
Features	• Data connectors for 160+ sources • Advanced indexing strategies • Production-grade RAG pipelines • Agent framework with tool use • LlamaCloud for managed parsing	• Composable LLM application framework • Agent and tool use support • RAG and retrieval pipelines • LangSmith for debugging and monitoring • Python and JavaScript SDKs
Pros	+ Best-in-class for data and RAG + Great data connector library + Clean API design	+ Largest LLM framework ecosystem + Excellent documentation + Huge integration library
Cons	− Narrower scope than LangChain − Less community content − Complex for simple use cases	− Can be over-abstracted for simple tasks − Frequent breaking changes − Learning curve for the full framework
Use Cases	Enterprise knowledge basesDocument Q&A systemsData-heavy AI applicationsRAG pipeline development	Building RAG applicationsCreating AI agentsLLM application developmentPrototyping AI workflows
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LlamaIndex vs LangChain.