LangChain vs LlamaIndex
Most pages answering this question describe a division from two years ago. Both projects have moved, and they now overlap more than they differ.
The short answer
Choose LangChain when the hard part is controlling what an agent does step by step, and choose LlamaIndex when the hard part is getting your own documents into a usable shape.
The familiar rule was that LangChain does agents and LlamaIndex does retrieval. Neither vendor writes that any more. LangChain describes an agent as a model plus the scaffolding around it. LlamaIndex calls itself a framework for building agents over your data, and treats retrieval as one tool an agent can use.
What survives is a difference of instinct. One starts from the flow of control. The other starts from the documents.
Side by side
Positioning taken from each project's own documentation in August 2026, not from the comparison posts that repeat older framing.
| Dimension | LangChain | LlamaIndex |
|---|---|---|
| How it describes itself | Configurable scaffolding around a model | A framework for agents over your data |
| Where it starts | The flow of control | The documents |
| Fine control | LangGraph, described as low-level orchestration | Workflows, driven from the data side |
| Retrieval | A capability you assemble | Core heritage, now framed as one tool |
| Watching it in production | LangSmith, for tracing and scoring runs | Not the project's centre of gravity |
| Best-fit problem | An agent that must follow rules and stop | Messy source files nobody has structured |
| Wrong fit | A single call to a model with a prompt | A workflow with no document in it |
- Source filesLlamaIndex starts here
- ParsingThe unglamorous half of most builds
- RetrievalBoth frameworks reach this point
- Agent loopLangChain starts here
- ActionWhere control matters most
Same pipeline, different end to pick it up from. That is most of the choice.
LangChain
Where it wins
- LangGraph is published as low-level orchestration, which is what you want when an agent must follow a defined path.
- LangSmith covers tracing and scoring, so an agent that misbehaves in production can be inspected rather than guessed at.
- The community is large, so an unusual integration has usually been attempted by someone already.
- Swapping model providers is a first-class idea rather than a rewrite.
Where it hurts
- There is more than one way to build the same thing, and older tutorials teach patterns the project has moved past.
- The abstraction can hide the actual prompt, which turns a simple debugging job into archaeology.
- The observability story leads toward a paid hosted product, so plan that decision rather than meeting it late.
- For a single model call with a prompt, the framework is more machinery than the problem deserves.
LlamaIndex
Where it wins
- Document handling is the project's origin, and it shows in how much of the ingestion path is already built.
- Getting from a folder of files to answers with sources is a shorter road than assembling the same thing yourself.
- The agent framing is explicit: retrieval becomes one tool among several, rather than the whole design.
- It suits teams whose real problem is that the source material is a mess.
Where it hurts
- Fine-grained control over a long agent run is less of a focus than it is on the other side.
- Production monitoring is not the project's centre, so you will bring your own or buy one.
- The parsing convenience can become a dependency you notice only when a document type breaks.
- If your data is already clean and structured, the main advantage disappears.
How to choose
- Choose LangChain if the agent must stop, ask a person, or refuse, and you need that path to be explicit.
- Choose LangChain if you want tracing and scoring from the same vendor as the framework.
- Choose LlamaIndex if most of the work is turning PDFs, spreadsheets and scans into something searchable.
- Choose LlamaIndex if a small team needs answers with citations quickly and does not want to assemble a pipeline.
- Choose either and stay shallow if the app is one model call with a prompt. A framework earns its place at the second or third step, not the first.
- Choose neither if nobody can list the questions the system must answer. That gap is not a library problem, and both will hide it for a month.
Document-heavy systems we have shipped
Questions engineers ask before committing
01Can you use LangChain and LlamaIndex in one project?
Yes, and pairing them is common. Teams often use LlamaIndex for ingestion and retrieval, then run the agent loop in LangGraph where the control flow needs to be explicit. Keep the seam narrow: one module owning retrieval, one owning orchestration, so replacing either later is a contained job.
02Is LlamaIndex only for RAG?
No, and its own documentation has moved past that description. LlamaIndex now presents itself as a framework for building agents over your data. Retrieval pipelines are one of several tools such an agent can use. Comparisons still calling it a RAG-only library are quoting older positioning.
03What is LangGraph, and do we need it as well as LangChain?
LangGraph is published as an agent runtime and low-level orchestration framework, for cases needing tighter control than a standard agent setup gives. A straightforward agent does not need it. Reach for it when the process has branches, approvals or a defined stopping point that must be enforced rather than suggested.
04Does using a framework lock us in?
The lock-in that hurts is not the library, it is the hosted services around it. Swapping frameworks is a rewrite of glue code, which is unpleasant but finite. Depending on a hosted tracing, evaluation or parsing service is the commitment worth deciding deliberately, because the data and the workflow both end up there.
05Do we need a framework at all?
Not for a first version. Calling a model API directly is often clearer for a prototype, and it keeps the prompt visible while you learn what good output looks like. Bring in a framework once you are juggling several tools, retries and a real control flow. That is where hand-rolled code starts costing more than it saves.
