Langchain vs Llamaindex

LangChain vs LlamaIndex

Overview

LangChain is a framework for building applications with large language models through composable chains, agents, and tools. It provides abstractions for prompt management, memory, document loading, and multi-step reasoning workflows. LlamaIndex (formerly GPT Index) is a data framework for connecting LLMs to external data sources. It specializes in indexing, retrieval, and query engines for building RAG (Retrieval-Augmented Generation) applications.

Key Differences

Primary focus: LangChain is agent-and-chain-first — it excels at orchestrating multi-step LLM workflows. LlamaIndex is data-and-retrieval-first — it excels at indexing documents and answering questions over them. Complexity: LangChain has a steeper learning curve with more abstractions (chains, agents, tools, memory, callbacks). LlamaIndex has a simpler API focused on index → query engine → response. RAG quality: LlamaIndex has more sophisticated indexing strategies (tree, keyword, vector, knowledge graph) and query modes. LangChain's retrieval is functional but less specialized. Agent capabilities: LangChain has richer agent frameworks with ReAct, Plan-and-Execute, and custom agent types. LlamaIndex added agents but they are less mature.

Pricing

Both are open-source and free. Revenue comes from cloud services: LangSmith (LangChain's observability platform) and LlamaCloud (LlamaIndex's managed service).

Verdict

Use LangChain if you are building complex agent workflows, multi-step reasoning chains, or applications that need tool use and memory. Use LlamaIndex if your primary need is connecting an LLM to your documents, building a Q&A system, or implementing RAG. Many production applications use both together — LlamaIndex for retrieval and LangChain for orchestration.