LangChain vs LlamaIndex: which to build on in 2026
LangChain’s first commit landed in October 2022, a few weeks before ChatGPT existed. LlamaIndex followed about a month later, originally under the less catchy name GPT Index. Both were built to solve a problem nobody had a name for yet: a raw call to an LLM API gets you a demo, not a product. You need retrieval, memory, tool calling, retries, and some way to figure out why the model did something strange in production.
They’re not actually solving the same problem, which most comparisons skip past. LangChain started as a general orchestration layer (chains, agents, memory) and has grown into LangGraph for stateful multi-agent workflows plus LangSmith for tracing and evals. LlamaIndex started as a way to shove your documents into a format an LLM could query, and it has stayed closer to that job even as it added its own agent and workflow features.
If you’re building something that needs to reason across several tools and pause for human approval mid-task, LangChain, specifically LangGraph, is the better starting point. If you’re building something that mostly needs to answer questions correctly against a pile of PDFs, contracts, or Confluence exports, LlamaIndex gets you there faster with less code. Plenty of production stacks use both: LlamaIndex for the retrieval layer, LangChain for the agent loop around it. More on that split below, plus where each one actually falls short.
TL;DR comparison table
| LangChain | LlamaIndex | |
|---|---|---|
| pricing | free, MIT licensed core. LangSmith adds paid tiers for tracing and evals | free, MIT licensed core. LlamaCloud (LlamaParse) bills per page for managed parsing |
| features | agent orchestration, stateful workflows via LangGraph, a huge integration registry | retrieval and indexing over your own documents, strong document parsing via LlamaParse |
| support | community forums and GitHub issues, paid enterprise support from LangChain Inc | community forums and GitHub issues, paid enterprise support from LlamaIndex Inc |
| target user | teams building multi-tool agents with branching logic | teams building question-answering or search over a private document set |
LangChain at a glance
LangChain was started by Harrison Chase and is MIT licensed, with the source sitting on GitHub split across a stable core package, the main library, and a long tail of community-maintained provider integrations. That split is relatively new. For most of 2023 and 2024, LangChain shipped fast enough that upgrading between minor versions was its own recurring headache, and in 2025 the team pushed toward a stable 1.0 specifically to stop that bleeding.
Two products sit alongside the core library now. LangGraph, introduced in 2024, is a lower-level way to build agents as explicit state graphs, with checkpointing so a long-running task can pause, wait on a human, and resume exactly where it left off. LangSmith is the observability and eval layer, tracing runs, building eval datasets, and flagging regressions, and it works even if the app you’re tracing wasn’t built with LangChain at all. It’s worth reading the LangSmith product page directly if tracing is the actual reason you’re evaluating this stack.
LlamaIndex at a glance
Jerry Liu started what became LlamaIndex as GPT Index in November 2022, renamed it in 2023, and has kept it closer to that original job ever since: get your data into an LLM in a form it can actually query. The core abstractions, documents, nodes, indices, query engines, are documented in full on the LlamaIndex docs site, and the GitHub repo is MIT licensed the same as LangChain’s.
LlamaHub is the connector registry, loaders for Notion, Slack, Google Drive, S3, and dozens of file formats. The feature that actually drives a lot of adoption, though, is LlamaParse, a proprietary document parser built for exactly the cases that break naive PDF extraction: tables, scanned contracts, multi-column layouts. It’s the standout product in this whole comparison, honestly, and it’s why teams that have specifically fought with messy PDFs end up here rather than staying with a plain text splitter. LlamaIndex also shipped its own answer to LangGraph, called Workflows, an event-driven way to build agent-like behavior, though it arrived later and has a smaller footprint than LangGraph does.
head-to-head
model quality and benchmarks
Neither product trains or serves a model, so there’s no LangChain benchmark or LlamaIndex benchmark the way there’s a benchmark for Claude or GPT. What differs is how much sits between you and the model’s actual behavior. LangChain’s chat model wrapper standardizes tool calling and structured output across dozens of providers behind one interface, convenient until a provider-specific quirk gets abstracted away right when you needed to see it. LlamaIndex’s LLM abstraction is thinner by design, since its job is retrieval rather than agent behavior, so less gets hidden on that side.
If model choice is the actual decision on your plate, that’s a separate question from either framework. I’ve gone into the tradeoffs between Anthropic’s and OpenAI’s APIs for production use directly, and it applies no matter which orchestration layer you put on top.
context window
Also bring-your-own-model here, so the ceiling is whatever your provider gives you. What each framework gives you is a way to work around that ceiling. LangChain has memory classes, buffer, summary, token-limited trimming, for keeping a conversation inside budget. LlamaIndex’s entire premise is chunking and retrieving only the relevant slice of a much larger corpus, so you rarely hit the wall at all. If your real problem is forty thousand pages against a 200k-token model, that’s a LlamaIndex-shaped problem before it’s a context-window problem.
latency and throughput
Same caveat: the model call itself runs at the same speed no matter which framework wraps it. Where they differ is overhead. LangChain’s older chain classes, LLMChain, SequentialChain, and their relatives, route calls through several layers of callback handlers, and that’s exactly the kind of thing that gets blamed, sometimes fairly, for chains feeling slower and harder to profile than a raw API call. LangGraph is more explicit about what executes and when, which in my experience makes it easier to find the real bottleneck instead of guessing which chain layer added the delay. LlamaIndex’s query engines add their own overhead too, embedding calls, retrieval steps, sometimes a re-ranking pass, but with fewer abstraction layers in between, it’s usually easier to see where the milliseconds went.
I haven’t run a formal side-by-side latency benchmark between the two on identical hardware, and I’d be skeptical of anyone who claims one number settles it. It depends entirely on which retrieval steps and providers you’ve plugged in behind either framework.
pricing per million tokens
Neither framework charges per token. That bill goes straight to whichever model API you’re calling, and I’ve written separately about what prompt caching does to that bill if you haven’t looked at it yet. The framework layer’s own pricing is structured completely differently: LangSmith bills by traces and seats, with a free tier for individual developers and paid Plus and Enterprise tiers for teams needing longer retention or SSO. LlamaCloud’s LlamaParse bills per page parsed, with a daily free allowance and paid tiers above that. The real per-token math lives entirely in your model provider’s docs, not in either of these products.
API ergonomics and SDK quality
This is where LangChain has taken the most public criticism, and it’s mostly earned. Between 2022 and 2024 the API moved fast enough that upgrade threads on GitHub issues became their own genre of complaint. The push to a stable 1.0 in 2025 was a direct response to that, splitting the stable core out from the faster-moving provider integrations. LlamaIndex has had its own churn, ServiceContext was deprecated in favor of a global Settings object, for one, but the surface area is smaller because the product does less, so there’s less to break in any given release.
self-host vs managed
Both are fully open source and self-hostable end to end: run the library, bring your own vector store and model, done. Where they add optional managed layers is different. LangChain’s is LangSmith for observability plus LangGraph Platform for deploying and hosting agents. LlamaIndex’s is LlamaCloud for managed parsing and indexing pipelines. Neither forces you into the managed tier. Both make it meaningfully easier once you’re past the prototype stage, and both let you keep the observability piece in-house if you don’t want traces leaving your infrastructure.
data retention and training policy
Read the actual current terms before trusting anyone’s summary here, mine included, since these change. Directionally: both LangSmith and LlamaCloud publish data processing terms stating customer data isn’t used to train their own models by default, which is standard practice for this category now. The thing worth checking specifically is what happens to documents sent through LlamaParse for parsing, since that’s genuinely customer content passing through a hosted service, not just trace metadata. If vendor data policy is a live topic for your team beyond just these two, The Privacy Wire tracks retention and training-data policy changes across a much wider set of vendors than I cover here.
ecosystem and integrations
LangChain wins this one by a wide margin on raw integration count: hundreds of vector stores, tool wrappers, and provider integrations, plus the community volume that comes from being the default answer to “how do I build an LLM agent” for three years running. LlamaIndex’s LlamaHub registry is smaller but deeper on the data-connector side specifically. In practice the two ecosystems are more complementary than competitive, and a fair number of LangChain users end up importing a LlamaHub loader anyway because nobody’s rebuilding a Notion connector from scratch twice.
Both are still standing after four years, in a category where most competitors from that era quietly died. That alone says something about picking either one.
use-case verdicts
- RAG over messy internal documents, contracts, scanned PDFs with tables: LlamaIndex. LlamaParse was built for exactly this, and it’s the reason a lot of teams adopt LlamaIndex in the first place.
- A multi-step agent with tool use and a human-approval gate partway through: LangChain, specifically LangGraph. Its checkpointing model is built for pausing a run, waiting on a person, and resuming exactly where it left off, which matters a lot if an agent dies halfway through a long job and you need to recover state instead of restarting from zero.
- A solo developer or small startup shipping a question-answering MVP over a document set in a weekend: LlamaIndex. Fewer abstractions between “here are my files” and “ask a question,” which matters more than architectural flexibility at that stage.
- A regulated enterprise team that needs audit trails, prompt and response logging, and evals before anything ships: LangChain, via LangSmith. If you haven’t set up evals yet, that’s worth doing first, regardless of which framework you land on.
who should pick LangChain
Pick LangChain if the hard part of your product is the agent’s behavior, not the data underneath it: multiple tools, conditional branching, a need to pause and resume, or a supervisor pattern coordinating several sub-agents. Pick it too if you’re going to need LangSmith-grade observability, since that integration is native rather than bolted on later. And pick it if your team has already sunk time into learning LangGraph, since re-platforming a live agent’s state machine is genuinely painful.
One honest flag: if your use case is actually described by the difference between an agent and a workflow and it turns out you just need a workflow, LangChain will still work, but you’re carrying orchestration weight you don’t need. Check that distinction before reaching for the heaviest tool on the shelf.
who should pick LlamaIndex
Pick LlamaIndex if your product’s hard part is retrieval quality: getting the right ten chunks out of forty thousand pages, parsing tables and scanned documents correctly, and keeping the index current as source documents change. Pick it if you want to move fast without learning a large agent framework first, the core query-engine pattern is learnable in an afternoon. And pick it if LlamaParse solves a real, current pain point, teams that have specifically fought with PDF table extraction tend to become LlamaIndex users because of that one feature alone.
verdict overall
It depends, and I mean that more precisely than the hedge it sounds like. These two products optimize for different bottlenecks, not the same bottleneck at different price points. If your bottleneck is retrieval, LlamaIndex is the right starting point and probably the only one you need. If your bottleneck is orchestrating multi-step, multi-tool agent behavior, start with LangChain and specifically LangGraph rather than the older chain classes.
A lot of the production stacks I’ve looked at use both at once, LlamaIndex handling indexing and querying, LangChain handling the agent loop that calls it as a tool. That combination is more common than picking a single winner and standardizing on it. If you’re still deciding which agent framework to commit to more broadly, our rundown of the current AI agent framework landscape covers where both of these sit relative to the rest of the field. You can find more comparisons like this one on the blog.
Written by Xavier Fok
disclosure: this article may contain affiliate links. if you buy through them we may earn a commission at no extra cost to you. verdicts are independent of payouts. last reviewed by Xavier Fok on 2026-09-12.