Side-by-side

LlamaIndex vs LangChain / LangSmith

LlamaIndexLangChain / LangSmith

Bottom line: LlamaIndex for document-heavy RAG applications; LangChain / LangSmith for teams building LLM apps and agents.

	LlamaIndex Data framework for LLM apps and knowledge agents Visit	LangChain / LangSmith Framework and platform for building LLM apps and agents Visit
Votes	0	0
Pricing	Freemium	Freemium
Category	Ai Agents	Ai Agents
Tags	llm-frameworkragdocument-parsingopen-sourceknowledge-agents	llm-frameworkai-agentsobservabilityopen-sourcerag
Best for	Document-heavy RAG applications Enterprise knowledge agents Teams needing reliable PDF parsing	Teams building LLM apps and agents RAG and chatbot development Production LLM observability
Pros	Open-source frameworks are free (MIT) Strong focus on retrieval and data quality LlamaParse excels at complex document parsing Managed LlamaCloud for production data workflows Python and TypeScript support	Open-source frameworks are free (MIT) Huge ecosystem of integrations LangGraph enables robust stateful agents LangSmith is strong for tracing and evaluation LangSmith works even without LangChain
Cons	Overlaps with LangChain, adding decision fatigue LlamaCloud credit costs scale with document complexity Framework surface area has grown large Fast-moving APIs can change between versions Advanced agent features less mature than dedicated agent frameworks	Framework abstractions can feel heavy or leaky Rapid changes and occasional breaking updates Some teams prefer calling model APIs directly LangSmith seat-plus-usage pricing adds up for teams Learning curve across a large surface area

Comparison generated from each tool's listing. Add or remove tools above to change it.