LangChain vs LlamaIndex: The Old Split No Longer Holds

The comparison everyone repeats — LangChain for agents, LlamaIndex for retrieval — was accurate once and isn't any more. LlamaIndex now describes itself as "the leading framework for building LLM-powered agents over your data with LLMs and workflows", with agents, workflows, and context augmentation as its three headline concepts. Both frameworks build agents. Both do retrieval.

What separates them now is where each one's centre of gravity sits, and what it assumes is hard about your problem.

• LangChain assumes the hard part is the agent loop — models, tools, and orchestration — and gives you abstractions plus a very large integration surface.
• LlamaIndex assumes the hard part is your data — connecting, indexing, and querying it — and builds agents on top of that foundation.
• If your problem is "answer questions over ten thousand internal documents," that difference still points clearly in one direction.
• If your problem is "an agent that calls six APIs and decides what to do," it points the other way.

Quick comparison

LangGraph is the part people miss

A lot of LangChain confusion disappears once you separate the two libraries, and the documentation is now explicit about it.

LangChain is the agent framework — abstractions and integrations for models, tools, and agent loops, including prebuilt architectures for common tool-calling patterns.

LangGraph is a separate, lower-level library — an orchestration framework and runtime for long-running, stateful agents. What it adds is the stuff that matters in production rather than in a demo:

• Durable execution, so an agent resumes where it left off after a failure
• State and memory, both short-term working state and longer-lived session memory
• Human-in-the-loop, where you inspect and modify agent state mid-run
• Hybrid graphs mixing deterministic hand-coded steps with LLM-driven ones

Crucially, you can use LangGraph without LangChain. Plenty of teams do exactly that: LangGraph for the runtime, their own thin layer above it. If your objection to LangChain was too much abstraction, that's the option you may have been looking for.

What LlamaIndex is actually best at

The data layer, still. Connectors, indexes, query and chat engines, plus the ingestion and indexing machinery that turns a pile of documents into something a model can use reliably.

Its expansion into agents and event-driven workflows is real rather than cosmetic, and its framing is consistent: agents over your data. The stated use cases — question answering, document extraction, chatbots, autonomous agents, multi-modal work — all sit on top of that context-augmentation foundation.

Which gives a practical rule. If your project's difficulty is concentrated in getting the right context in front of the model, start with LlamaIndex. Retrieval quality is where most document-heavy applications actually succeed or fail, and it's the thing LlamaIndex has been iterating on longest.

When the choice barely matters

Worth saying plainly: for a lot of projects this decision is over-weighted.

Both frameworks call the same models, both support tool calling, both have large integration libraries, and both will get a prototype working in an afternoon. If you're building something modest, pick the one whose documentation reads better to you and move on. Our overview of agentic AI frameworks covers the wider category.

The choice starts to matter at production scale, and then it's usually not about the framework's feature list but about which failure mode you'll be debugging: retrieval that returns the wrong context, or an agent loop that won't terminate. Pick the framework whose core competence matches the failure you expect.

Using both

Common and sensible. LlamaIndex for ingestion, indexing, and retrieval; LangChain or LangGraph for the agent loop and orchestration on top. Neither prevents it, and both expose the pieces you'd need.

The cost is two dependency trees and two sets of abstractions for your team to hold in their heads, which is a real tax on a small project and usually worth it on a large one.

The step before either framework

Both assume you're a developer with a Python environment, API keys, somewhere to deploy, and enough context to debug an agent that misbehaves. That's fine for engineering teams and it's the wall for everyone else.

The common pattern is seeing an agent or workflow someone shared that does exactly the job you need, and never getting it running — dependencies, keys, environment, a README that assumed knowledge you don't have.

Taku is built for that gap: an AI-native desktop workspace where you mirror an AI app or workflow that already works, run it on your own machine, and remix it into your own. It isn't a framework and doesn't compete with these — it's the step where people stall before a framework becomes useful. Taku is in Beta, and the Mac app is available now, and the free app library shows what that looks like.

Choosing

1. Document-heavy, retrieval is the hard part? → LlamaIndex.
2. Agent loop and many tool integrations are the hard part? → LangChain.
3. Need durable execution, state, human-in-the-loop? → LangGraph, with or without LangChain.
4. Found LangChain too abstract? → LangGraph directly, or other options.
5. Both problems are hard? → use both; it's a normal combination.
6. Small prototype? → either. Don't spend a week on this decision.

FAQ

Is LlamaIndex only for RAG?

Not any more. It describes itself as a framework for building agents over your data, with agents and event-driven workflows alongside context augmentation. Retrieval remains its deepest strength.

Is LangGraph part of LangChain?

It's a separate library that integrates closely. LangChain is the agent framework; LangGraph is the lower-level orchestration runtime for long-running stateful agents. You can use LangGraph on its own.

Which is better for production?

Neither wins outright. LangGraph's durable execution and state management are aimed squarely at production concerns; LlamaIndex's retrieval maturity matters most when context quality is what breaks.

Can I use both together?

Yes, and it's a common architecture — LlamaIndex for the data layer, LangChain or LangGraph for orchestration.

Which has more integrations?

LangChain's ecosystem is larger overall. LlamaIndex's is more concentrated on data sources and readers.

Do I need a framework at all?

For a single model call with a couple of tools, often not — direct API calls are simpler and easier to debug. Frameworks earn their keep when you need retrieval pipelines, state, or multi-step orchestration.