LangChain suits teams building AI agents, because it now centres on agent frameworks (LangChain, LangGraph and Deep Agents) plus LangSmith for tracing, evaluation and deployment. LlamaIndex suits teams whose AI has to read documents well, because its own GitHub page now calls it the document processing platform, with LlamaParse for parsing and extraction. Both are open source under the MIT licence, so you can combine them: LlamaIndex to turn documents into clean data and LangChain to build the agent on top.
A couple of years ago these two looked like rival toolkits for the same retrieval apps. Their own pages show they've drifted apart, and that's good news, because it makes the choice easier. I read LangChain's site, its Python docs and its GitHub repository, and LlamaIndex's site and GitHub repository.
LangChain and LlamaIndex side by side
| LangChain | LlamaIndex | |
|---|---|---|
| How it describes itself | The agent engineering platform | The document processing platform |
| Open-source parts | LangChain, LangGraph, Deep Agents | LlamaIndex framework, LiteParse |
| Hosted product | LangSmith: observability, evaluation, deployment | LlamaParse: parse, extract, index |
| Free option | Open source under MIT | Framework under MIT, plus 10,000 free LlamaParse credits a month |
| Model support | OpenAI, Anthropic, Google and others | Not covered on the pages I read |
| GitHub stars | 148k | 52.6k |
Why the two projects now answer different questions
LangChain's docs open with create_agent, a minimal, configurable agent harness you build from a model, tools, a prompt and middleware. Around it sit LangGraph for low-level control, Deep Agents for long-running work, and LangSmith for watching what your agents do, scoring them and shipping them to production. LlamaIndex has gone the other way. It describes itself as an applied AI lab building frontier models for document parsing and extraction, with LlamaParse for agentic OCR and structured extraction and LiteParse for fast local parsing without a vision language model.
When LangChain is the better starting point
Choose LangChain when the hard part of your project is behaviour: an agent that calls tools, asks a human before acting, remembers context and needs testing before customers see it. Its docs cover short-term and long-term memory, guardrails, human-in-the-loop, multi-agent setups and MCP, and it supports OpenAI, Anthropic, Google and other model providers. The repository has 148k stars and 24.9k forks, so you'll find examples for almost any pattern you're likely to need.
When LlamaIndex is the better starting point
Choose LlamaIndex when the hard part is the input: contracts, invoices, reports and scanned PDFs with tables and messy layouts. LlamaParse handles layout-aware parsing and pulls data into schemas you define, and its free plan includes 10,000 credits a month, which LlamaIndex puts at roughly 1000 pages. If your documents can't leave your own machines, LiteParse runs locally. The framework repository has 52.6k stars and 8.4k forks, and it's also MIT-licensed.
Questions for your build
- Is your main risk bad answers from messy documents, or an agent doing the wrong thing?
- Do documents have to stay on your own machines?
- Which model providers do you need to switch between?
- How will you test and monitor the system after launch?
- Who on your team will maintain the code a year from now?
If you're building retrieval into either one, my checklist for preparing a knowledge base for RAG saves rework, and this explainer on what a vector database does covers the storage side.
My advice on LangChain or LlamaIndex
If you're building an agent that has to act, start with LangChain and add LangSmith once you need traces and evaluations. If your project lives or dies on reading documents correctly, start with LlamaIndex and test LlamaParse on your ugliest files first, not your cleanest. They aren't either-or: both are MIT-licensed, so using LlamaIndex to clean the documents and LangChain to run the agent is a sensible split that I'd happily recommend.
Do you make AI agent or document AI tools? Tool makers can ask to be considered for a featured place in comparisons like this one.
Frequently asked questions
Is LangChain free?
The LangChain, LangGraph and Deep Agents frameworks are open source under the MIT licence. LangSmith is LangChain's separate platform for observability, evaluation and deployment.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
Is LlamaIndex free?
The LlamaIndex framework is open source under the MIT licence, and LlamaParse has a free plan with 10,000 credits a month, which LlamaIndex puts at roughly 1000 pages.
Can you use LangChain and LlamaIndex together?
Nothing on either project's pages stops you. Both are MIT-licensed, so one can prepare documents while the other runs the agent.
What is LlamaParse?
It's LlamaIndex's document processing product for agentic OCR, layout-aware parsing and structured extraction into schemas you define.
Which models does LangChain support?
LangChain's docs say it supports OpenAI, Anthropic, Google and other providers through one agent harness.
Does LangChain have a no-code option?
LangChain lists No-Code Fleet Agents for the whole company alongside its open-source frameworks and LangSmith.
What is LangGraph?
LangGraph is LangChain's open-source framework for building agents with low-level control, alongside LangChain itself and Deep Agents for long-running tasks.
