LangChain vs LlamaIndex

Both are MIT licensed, both do agents and RAG. Here is where each one actually earns its place in 2026, and what the hosted platforms cost.

At a glance

Pricing

Both frameworks are MIT licensed open source. You can build and ship with either one for free and pay only your model provider. The companies monetize hosted platforms, and that is where a real budget line can appear.

LangSmith, LangChain's observability and deployment platform, has a free Developer plan (one seat, 5,000 base traces per month) and a Plus plan at $39 per seat per month with 10,000 base traces included. Enterprise pricing is custom. Past the included volume you pay usage-based fees, and retention matters: base traces keep data for 14 days, while 400-day extended retention costs extra.

LlamaCloud, LlamaIndex's hosted parsing and indexing platform, is credit based: 1,000 credits cost $1.25. The free tier includes 10,000 credits per month, Starter is $50 per month with 40,000 credits, Pro is $500 per month with 400,000 credits, and Enterprise is custom. Basic LlamaParse parsing starts around 1 credit per page; the agentic modes that run LLMs or vision models over each page cost several times more.

Neither platform is required to use its framework. But note where each vendor charges you: LangChain bills traces, LlamaIndex bills pages. A high-traffic agent gets expensive on LangSmith, and a large document backlog gets expensive on LlamaCloud.

When to pick LangChain

LangChain hit 1.0 in October 2025, and that release mattered more than most version bumps. The team cut down the abstraction sprawl the framework was known for, and it committed to no breaking changes until 2.0. The core agent API, create_agent, runs on the LangGraph runtime, so a standard LangChain agent gets durable execution, persistence, and human-in-the-loop pauses without extra wiring. When an agent needs custom control flow beyond what create_agent offers, you drop down to LangGraph itself rather than switching frameworks.

I would pick LangChain when orchestration is the hard part of the build: agents that call many tools, branch on results, need retries and checkpoints, or wait for a human to approve a step. The integration catalog is the widest of any LLM framework, and adoption is a practical advantage too. At about 145,000 GitHub stars, it is the framework a new hire is most likely to already know, and most model and vector store vendors ship a LangChain integration early.

The honest caveat: it is still a lot of framework. If your app is one or two prompt calls with some formatting, LangChain adds a layer you will spend time learning without getting much back.

When to pick LlamaIndex

I would pick LlamaIndex when the data is the hard part. Its core loop (ingest, index, retrieve, synthesize) treats chunking strategy, index type, and retrieval method as first-class decisions instead of configuration afterthoughts. If your app depends on retrieval quality over a difficult corpus, LlamaIndex gives you more levers: routing across indexes, sub-question decomposition, fusion retrieval, rerankers, and structured extraction.

The company's center of gravity is documents. LlamaParse exists specifically to turn ugly PDFs, scans, and tables into clean input, and LlamaIndex now describes itself as a document agent and OCR platform. If your pipeline starts with contracts, filings, invoices, or scanned paperwork, that focus works in your favor: the parsing layer is a product, not a side feature.

The caveat here is maturity. The core library is still pre-1.0 (0.14.x as of August 2026), and interfaces have moved between minor versions. It is also a smaller project than LangChain, at about 52,000 GitHub stars, which in practice means fewer third-party tutorials and a thinner trail of answered questions when something breaks.

What most comparisons miss

First, this is not an either-or choice. The frameworks compose. A pattern I like is LlamaIndex as the retrieval layer inside a LangGraph agent: LlamaIndex handles parsing and indexing, and the agent framework handles control flow. Most comparison posts treat the two as rivals for the same slot, and that framing pushes people into a false decision.

Second, the monetization split predicts the roadmaps. LangChain raised a $125 million Series B in 2025 and calls itself an agent engineering platform, so expect continued investment in observability, evals, and deployment. LlamaIndex sells parsing by the page, so expect continued investment in document understanding. Pick the framework whose commercial incentive lines up with your problem, because that is where the polish will accumulate.

Third, the versioning gap is an operational fact, not a scorecard point. LangChain 1.x comes with a stability commitment; LlamaIndex 0.x does not. If you run a small team that cannot absorb upgrade churn, that difference is worth more than most feature rows above.

Finally, you might need neither. If your app is a single prompt chain with no retrieval and no branching, the model provider's SDK plus fifty lines of your own code is easier to debug than either framework.