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LangChain 1.4.4 or LlamaIndex 0.14.25 for a first app on your own files, out of the same pages

Across twelve pages read on 11 October 2026, four company comparison articles split the work by job, with LlamaIndex for documents and LangChain for agents, and no source we read counts which one developers keep using.

  • a-vs-b
  • ai-libraries
  • autopilot
  • hermes

We read twelve pages on 11 October 2026 about two Python libraries for building an app on your own files: LangChain 1.4.4 (released on PyPI on 8 October 2026) and LlamaIndex 0.14.25 (released on 21 September 2026). The sample is thin where it matters most. Four are tier 1 pages (the two PyPI listings and the two GitHub repositories), two more are the makers' own tutorials, one is a download-count API, four are comparison articles by software and consulting companies dated March to September 2026, and one is an older Hacker News thread. No established tech publication in our reach reviewed the two head to head, and no source we could read counts how many developers keep using either one.

Nothing was run. We installed neither library and built no app. The comparison articles name the frameworks but mostly not an exact release, so we read them as views of the current generation of each, not of the two versions above.

Consensus

Across the four comparison articles, the answer is a split by job, not a winner. All four place document retrieval, parsing and indexing with LlamaIndex, and all four place multi-step agents and tool orchestration with LangChain. Three of the four add that teams often use both, with LlamaIndex preparing the documents and LangChain running the wider application.

For a first app that answers questions from your own files, that points four of four toward LlamaIndex as the shorter start. We read this as a reading of company-written articles, not a measurement, and the confidence is thin: four articles from vendors and consultancies, none independent of the business of selling AI work, and none naming the exact releases above.

The makers' own tutorials share one default: each needs an OpenAI API key in its setup. That is a fact about the tutorials, not a verdict on either library.

Recurring strengths

LangChain:

  • Four of the four articles credit it with the wider set of integrations with model providers, vector stores and tools. One of them puts the count at more than a thousand; that figure is the article's own and we did not check it.
  • Four of four credit it with stronger support for multi-step agents, with its LangGraph companion named in three of them. Two articles also credit LangSmith, its separate observability product, as a strength.
  • The GitHub repository reports 147,563 stars and 24,753 forks on 11 October 2026, against 52,462 stars and 8,315 forks for LlamaIndex. Stars are attention, not quality.
  • PyPI's public statistics report about 170 million downloads of the langchain package in the last month, against about 2.9 million for the llama-index package. Downloads are installs, and not satisfied users. The llama-index figure is for its umbrella package only, so the two numbers are not a like-for-like count of use.

LlamaIndex:

  • Four of four credit it with stronger document parsing and retrieval, two of them naming its LlamaParse parser for tables and scanned pages.
  • Three of four say it needs less code to reach a working question-answering system over documents, because its defaults are more opinionated.
  • One article, from Prem AI, credits it with built-in evaluation metrics and more chunking strategies.

Recurring complaints

LangChain:

  • Three of the four articles describe a steep learning curve, in one case tied to LangGraph.
  • One article, from Prem AI, reports frequent breaking changes between versions and documentation spread across several sites. Only that one article says it, so it is one source's view and not a recurring one.
  • One article, from Addepto, says it adds overhead to simple pipelines. The Nutrient article says similar in other words: it describes assembling the retrieval stages by hand.

LlamaIndex:

  • Three of the four articles describe it as narrower than LangChain for tool-heavy agents, and the fourth says the same from the other side by placing agents with LangChain.
  • One article, from Addepto, says the credit-based pricing of its hosted LlamaCloud service can become unpredictable at scale. That is a claim about a paid service that neither library needs, and we did not read the pricing page.
  • One article, from Prem AI, notes it has no first-party observability product equal to LangSmith.

The open-work figures from GitHub are 645 open issues and pull requests for LangChain and 902 for LlamaIndex on 11 October 2026. GitHub counts both together, and the figure says how much is open, not how much is wrong.

Where reviewers split

The tiers do not disagree on direction. They differ on how much the choice matters.

The company articles treat the choice as a real fork: pick by job. A Hacker News thread from August 2024, which discusses an earlier comparison article, reads differently. In that thread, two comments say the two are close to the same with different names for things, and both give LangChain more prebuilt pieces. Four comments advise building without either. That thread predates both current releases, so it is not counted toward any theme above. It is the only developer voice we reached, and it is two years old.

The articles also differ on stability. Prem AI counts versioning stability as a reason to pick LlamaIndex and breaking changes as a LangChain cost; the other three articles say nothing on it. The two PyPI listings do show both projects shipping often: LangChain's core package released on 8 October 2026, LlamaIndex's on 21 September 2026. A release date is not a stability record.

On who stays, no source we read can say. The question in this piece's hook, which one developers stick with, has no counted answer in this sample.

Who it suits

LlamaIndex suits you if your first app is questions over a folder of documents, and the PDFs, tables or scans are the hard part. Four of four articles point that way, and its starter tutorial includes document search.

LangChain suits you if the app will call tools, use several models or keep state across steps. Four of four articles point that way, and its integration count and community size are the larger of the two on stars, forks and downloads.

Using both is the pattern three articles describe for teams that outgrow one.

Who should pass

Pass on LlamaIndex if your first app is mostly an agent that calls many outside tools, and retrieval is a small part of it. Pass on LangChain as a first stop if you only need to ask questions of a document folder, and you would rather not learn its wider set of parts first. Pass on both, on the one developer view we reached, if a single script that calls a model directly would do the job, though that view is from 2024.

Neither choice is a verdict on quality. The sample is four company articles, two tutorials and some public counts.

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Sources

One further article, by Kunal Ganglani, returned HTTP 403 and is not counted. We read twelve pages on 11 October 2026, synthesised them with AI, and tested nothing.

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