LangChain vs LlamaIndex
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
Our verdict
LangChain is a general-purpose Python framework for building LLM apps and agents, with a standard interface that makes it easier to swap model providers. LlamaIndex is narrower: it focuses on connecting LLMs to your own documents and data, mainly for retrieval-based question answering. Both are free, MIT-licensed Python libraries that require coding, so pick based on whether your main job is general app and agent building or document-grounded retrieval.
Pick LangChain if you want a broad toolkit for building LLM apps and agents and need to switch between model providers without rewriting code.
Pick LlamaIndex if your main goal is building a question-answering or RAG app over your own PDFs, documents, or databases.
| Key difference | LangChain | LlamaIndex |
|---|---|---|
| Main focus | General framework for LLM apps, agents, and model integrations | Connecting LLMs to your own documents and data for retrieval |
| Model switching | Standard interface lets you swap model providers with limited code changes | Modular design lets you swap LLM, embedding, and vector store providers |
| Ecosystem | Large library of integrations with model providers, tools, and vector stores | Over 300 integration packages, per the README; you install the ones you need |
| Project status | Last code update in the last day; related products like LangGraph add complexity | Last code update 3 days ago; README says the company's focus has shifted to LlamaParse |
| Setup and skill | Intermediate difficulty; advanced work needs extra products and learning | Intermediate difficulty; high-level API offers a short path to a basic prototype |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Choose LlamaIndex if you need…
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
Side-by-side
| LangChain | LlamaIndex | |
|---|---|---|
| Type | Framework | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 149k | 52.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 10 days ago |
What each one is
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
LlamaIndex
LlamaIndex is an open-source Python framework for connecting large language models to your own data, such as PDFs, documents, APIs and SQL databases. It provides connectors to load data, ways to structure it into indexes, and retrieval tools that feed relevant context to an LLM. The README also notes that the company's main focus has shifted toward its commercial LlamaParse document platform, with the open-source framework still available.
Best for
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
LlamaIndex
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
Strengths
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
LlamaIndex
- Large ecosystem of over 300 integration packages, per the README
- Modular design lets you customize or replace components like retrievers and query engines
- Offers both a high-level API for quick starts and lower-level APIs for advanced control
Not ideal for
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
LlamaIndex
- Non-programmers, since it is a Python library that requires writing code
- Projects that need a fully managed hosted service, because the README points to LlamaParse for the hosted offering and the framework is a toolkit you run yourself
- Users who only need simple one-off document parsing, where a standalone parser may be simpler
Trade-offs
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
LlamaIndex
- The README says the company's primary focus has shifted to LlamaParse, so the open-source framework is secondary to its commercial product
- The README is not updated as often as the documentation, so you should check the docs for current details
- Many separate integration packages means you must choose and install the ones you need
Getting started
LlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiFrequently asked questions
Should I use LangChain or LlamaIndex?
LangChain is a general-purpose Python framework for building LLM apps and agents, with a standard interface that makes it easier to swap model providers. LlamaIndex is narrower: it focuses on connecting LLMs to your own documents and data, mainly for retrieval-based question answering. Both are free, MIT-licensed Python libraries that require coding, so pick based on whether your main job is general app and agent building or document-grounded retrieval. Pick LangChain if you want a broad toolkit for building LLM apps and agents and need to switch between model providers without rewriting code. Pick LlamaIndex if your main goal is building a question-answering or RAG app over your own PDFs, documents, or databases.
What is the difference between LangChain and LlamaIndex?
LangChain: A Python framework for building AI agents and apps powered by language models. LlamaIndex: Python framework for building LLM apps that query your own documents and data. LangChain suits python developers building llm-powered applications, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are LangChain and LlamaIndex free?
LangChain: Free and open source. LlamaIndex: Free and open source.
Is LangChain or LlamaIndex more popular?
LangChain has more GitHub stars (149k) than LlamaIndex (52.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangChain and LlamaIndex?
Other options people compare them with include Semantic Kernel, CrewAI, OpenUI, VoltAgent, Quivr.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.