LangChain vs. LlamaIndex

Two frameworks, one underlying problem

Both LangChain and LlamaIndex exist because raw LLM API calls aren't enough to build real applications. You need to chain steps together, retrieve relevant documents before asking a question, manage conversation memory, and call external tools. Both frameworks provide abstractions for all of this — but they grew from different starting points, and that history still shapes which one fits a given project better.

LangChain started as a general-purpose toolkit for composing LLM calls into chains and agents. LlamaIndex (originally GPT Index) started specifically to solve retrieval: how do you connect an LLM to your own documents so it can answer questions about them accurately?

LangChain's philosophy: compose anything

LangChain's core abstraction is the chain — a sequence of calls, where each step's output feeds the next step's input. Chains can include prompt templates, LLM calls, output parsers, retrieval steps, and tool calls, wired together with its newer LangChain Expression Language (LCEL) using the pipe operator.

On top of chains sits the agent abstraction: an LLM that decides, at each step, which tool to call next, rather than following a fixed sequence. This makes LangChain the natural choice when the application's logic is genuinely dynamic — multi-step reasoning, tool use, branching based on intermediate results — not just "retrieve then answer."

LlamaIndex's philosophy: retrieval done right

LlamaIndex treats indexing as the central problem, not an afterthought. It provides dozens of index types — vector indexes, keyword table indexes, tree indexes, knowledge graph indexes — each optimized for different data shapes and query patterns, plus a rich set of data connectors for pulling in PDFs, Notion pages, SQL databases, Slack exports, and more.

Its query engines go beyond "embed and cosine-similarity search." LlamaIndex supports hierarchical summarization, sub-question decomposition (splitting a complex query into sub-queries answered against different parts of the index), and routing queries to the right index automatically. This depth is the payoff for retrieval-heavy applications: better chunking strategies, better re-ranking, and tighter control over what context actually reaches the model.

LangChain vs. LlamaIndex

A practical decision rule

If the core problem is "answer questions accurately over a large, messy document collection," start with LlamaIndex — its chunking, indexing, and query-engine defaults are tuned for exactly that, and you'll write less glue code. If the core problem is "orchestrate a multi-step process that may call several tools, branch on intermediate results, or maintain complex conversational state," start with LangChain — its chain and agent primitives are built for that kind of control flow.

Many production systems use both: LlamaIndex to build and query the retrieval index, LangChain (or LangGraph, its graph-based successor) to orchestrate the surrounding agent logic that decides when to retrieve, when to call other tools, and how to assemble the final answer.

What's next

Orchestration frameworks like LangChain are also the foundation for autonomous agents. The next lesson compares three popular agent frameworks — AutoGPT, CrewAI, and LangGraph — and the different philosophies they take toward letting an LLM plan and act on its own.