Comparison · AI
LangChain vs LlamaIndex: 2026 comparison
TL;DR: LlamaIndex is the data-and-retrieval framework — best for RAG over your documents. LangChain is the orchestration framework — best for agents and multi-step workflows (via LangGraph). They overlap, and plenty of production apps use both.
See also: What is retrieval-augmented generation?
The headline difference
Both LangChain and LlamaIndex are open-source frameworks for building applications on top of large language models, and both have large Python and TypeScript ecosystems. The difference is centre of gravity. LangChain is an orchestration framework: it gives you the abstractions to chain model calls, wire up tools and memory, and — through LangGraph — build stateful agents. LlamaIndex is a data framework: its strength is connecting your own documents and databases to an LLM and retrieving the right context. They overlap in the middle, which is why the comparison comes up so often.
Focus and philosophy
LangChain starts from the workflow. It treats an LLM app as a graph of steps — prompts, model calls, tools, retrievers, and control flow — and increasingly funnels complex cases through LangGraph, its runtime for stateful, branching, multi-agent execution.
LlamaIndex starts from the data. Its primitives are documents, nodes, indexes, and query engines, with a deep toolkit for ingestion, chunking, embedding, retrieval, and re-ranking. Where retrieval quality is the product, LlamaIndex tends to give you more for less assembly.
Feature scorecard
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Primary focus | Orchestration / agents | Data / retrieval (RAG) |
| Agent runtime | LangGraph | Workflows / agents (lighter) |
| Data connectors | Many integrations | LlamaHub (extensive) |
| Index types | Vector stores via integrations | Multiple native index types |
| Retrieval tuning | Configurable | Advanced (re-rank, postproc) |
| Observability | LangSmith | Callbacks + integrations |
| Languages | Python, JS/TS | Python, TS |
| Best fit | Agents, tools, workflows | Document Q&A, knowledge bases |
Retrieval and data ingestion
LlamaIndex's reason for existing is getting your data into a form an LLM can use well. It offers a large library of connectors (LlamaHub) for files, databases, APIs, and SaaS tools; several index structures beyond a plain vector store; and query-time machinery like node post-processing, re-ranking, and response synthesis. LangChain supports RAG through its retriever and vector-store integrations and is fully capable, but building an advanced retrieval pipeline often means wiring more pieces together yourself.
Agents, workflows, and observability
On the orchestration side LangChain pulls ahead. LangGraph gives you a durable, graph-based runtime for multi-step and multi-agent systems with branching, loops, and human-in-the-loop checkpoints, and LangSmith provides tracing, evaluation, and monitoring that production teams lean on heavily. LlamaIndex has added its own agent and workflow abstractions and they are improving, but if your application is primarily about complex agent behaviour rather than retrieval, LangChain's tooling is more mature.
When to choose which
Choose LlamaIndex when the heart of your app is retrieval over your own data — document Q&A, internal knowledge bases, search assistants — and you want strong retrieval primitives with minimal assembly.
Choose LangChain when the heart of your app is orchestration — agents, tool use, multi-step or multi-agent workflows, durable state — and you want LangGraph plus LangSmith for execution and observability.
Use both when it fits: LlamaIndex for ingestion and retrieval, LangChain/LangGraph for the agent and workflow layer around it. They interoperate.
Verdict
These are complements as much as competitors. If you must pick one, let the problem decide: data-and-retrieval apps lean LlamaIndex, agent-and-workflow apps lean LangChain. The frameworks have converged enough that either can technically do both — but starting with the one whose centre of gravity matches your problem saves real effort. Whichever you pick, invest in observability and evaluation early.
Frequently asked questions
What is the core difference between LangChain and LlamaIndex?
LangChain is a broad orchestration framework for building LLM applications and agents — chaining model calls, tools, memory, and control flow. LlamaIndex is focused on the data side: ingesting, indexing, and retrieving over your own documents for RAG. LangChain asks 'how do I orchestrate an LLM workflow?'; LlamaIndex asks 'how do I connect my data to an LLM?' The two overlap and are often used together.
Which is better for RAG?
LlamaIndex was built for retrieval and ships richer primitives out of the box: data connectors (LlamaHub), multiple index types, advanced query engines, node post-processing, and re-ranking. LangChain can do RAG too and is perfectly capable, but LlamaIndex tends to require less assembly for sophisticated retrieval pipelines. For pure document Q&A and knowledge bases, many teams start with LlamaIndex.
Which is better for agents and complex workflows?
LangChain, particularly via LangGraph — its graph-based runtime for stateful, multi-step, multi-agent workflows with branching, loops, and human-in-the-loop. If your application is more than retrieval — tool use, planning, multiple cooperating agents, durable state — LangChain's ecosystem (LangGraph plus LangSmith for tracing and evals) is the stronger fit.
Can I use both together?
Yes, and many teams do. A common pattern is LlamaIndex for the ingestion and retrieval layer and LangChain/LangGraph for the surrounding orchestration and agent logic. The two interoperate, so you are not forced to pick one for the whole stack. The frameworks are complements as often as they are alternatives.
Which framework should a new project pick in 2026?
If your application is fundamentally about retrieving over your own data — document Q&A, knowledge bases, search — start with LlamaIndex. If it is fundamentally about orchestrating LLM behaviour — agents, tools, multi-step workflows — start with LangChain (LangGraph). Many production apps end up using both. Whichever you choose, budget for observability (LangSmith or an equivalent) early.