LangChain vs LlamaIndex: Choosing an AI Framework

LangChain is a general-purpose toolkit for composing LLM calls, tools, and multi-step logic — chains, agents, memory, and integrations with dozens of model providers and vector stores. LlamaIndex is narrower by design: it’s focused specifically on getting your own data into a form an LLM can retrieve from and reason over. Both are commonly used for retrieval-augmented generation, but they start from different centers of gravity.

What each one is built around

LangChain’s core abstraction is the chain — a sequence of steps where each step’s output feeds the next, whether that’s a prompt template, an LLM call, a tool invocation, or a parser that structures the output. On top of chains, LangChain adds agents, which let a model decide at runtime which tools to call and in what order, plus memory abstractions for carrying conversation state across turns. It also ships broad integrations: connectors to dozens of vector stores, document loaders for many file formats, and wrappers for most major LLM providers.

LlamaIndex’s core abstraction is the index — a structure built by ingesting documents, splitting them into chunks, embedding those chunks, and organizing them for retrieval. Its strength is the ingestion and retrieval pipeline itself: connectors for pulling data from sources like databases, APIs, and file systems, a range of chunking and indexing strategies, and retrieval logic tuned specifically for finding the right context to hand an LLM. LlamaIndex has also grown agent and workflow features over time, but retrieval is still where it’s deepest.

Comparison

	LangChain	LlamaIndex
Core abstraction	Chains and agents	Indexes and retrievers
Primary strength	General orchestration, broad integrations	Data ingestion and retrieval quality
Agent support	Extensive, general-purpose	Present, retrieval-focused
Best fit	Multi-step workflows, tool-using agents	RAG-heavy applications over large document sets
Learning curve	Larger surface area	Narrower, more opinionated
Typical combination	Often used with a separate retrieval layer	Sometimes embedded inside a LangChain pipeline

Why the retrieval layer matters this much

For any RAG system, the quality of what gets retrieved bounds the quality of what the model can answer — no amount of prompt engineering fixes a retriever that hands the model the wrong chunks. That’s the problem LlamaIndex is built to solve well: it offers a range of indexing structures (flat vector indexes, hierarchical summary indexes, keyword-based indexes) and retrieval strategies beyond plain top-k similarity search, along with tooling for evaluating retrieval quality itself. The chunking decisions that feed into any of these indexes matter as much as the tool you use to build them — see RAG chunking strategies for how chunk size and overlap affect what gets retrieved.

LangChain can do retrieval too — it has its own vector store integrations and retriever abstractions — but it isn’t as specialized. Many production systems actually use both: LlamaIndex to build and query the index, LangChain (or a similar orchestration layer) to wire that retriever into a broader agent or chain that also calls other tools, holds conversation memory, or performs multi-step reasoning.

Where these frameworks fit in the bigger picture

Neither framework generates embeddings or runs the LLM itself — both sit on top of an embedding model and an LLM provider, coordinating calls between them. For background on what’s happening underneath, see what vector embeddings are and retrieval-augmented generation, explained. If you’re building something with tool-calling or multi-step planning rather than pure retrieval, what an AI agent is and ReAct-pattern agents cover the agent side of what both frameworks are trying to make easier to build.

A framework isn’t required

Both tools add real value once a project’s orchestration logic gets complex — many chained steps, several tools, conditional branching based on model output. For a simple case (embed some documents, retrieve the top few chunks, stuff them into a prompt), it’s often just as easy to call an embedding API and an LLM API directly without either framework, and doing so avoids a dependency that will need to track fast-moving API changes on both sides. Reach for a framework when the orchestration itself — not the individual API calls — is where the complexity lives.

When to choose which

Start with LlamaIndex when the core problem is retrieval over a large, possibly heterogeneous set of documents and you want strong defaults for chunking, indexing, and retrieval evaluation. Start with LangChain when the application is more than retrieval — multiple tools, an agent that needs to decide what to do next, or a workflow with several dependent steps. For anything that’s genuinely both — heavy retrieval wrapped in a broader agentic workflow — combining the two, or picking whichever has the stronger integration for your specific vector store and model provider, is a reasonable default.

The takeaway

LangChain and LlamaIndex both help you build applications on top of LLMs, but they optimize for different layers of the stack: LangChain for general orchestration — chains, agents, tools, memory — and LlamaIndex for the ingestion and retrieval pipeline that RAG depends on. Many real systems use both together rather than treating the choice as exclusive.