LangChain and LlamaIndex are both open-source frameworks for building AI applications. If you are trying to build a product that uses large language models, someone will almost certainly recommend one or both of them to you. Most explanations assume significant technical background. This one does not.
What problem these frameworks solve
When you use ChatGPT or Claude directly, you type a message and get a response. That is useful for personal assistance but limited for building products.
Products built on AI need to do more complex things: retrieve relevant information from a database before answering, maintain context across multiple interactions, call external APIs to take action, or chain multiple AI steps together (ask question, then based on the answer, do something, then ask another question).
Doing all of this from scratch in code is time-consuming. LangChain and LlamaIndex are frameworks that provide pre-built components for common AI application patterns, so developers can build faster.
LangChain: the Swiss Army knife
LangChain started as a framework for building chains of AI calls with different tools and data sources. It has expanded significantly since and now covers a broad range of AI application patterns.
What LangChain is good at:
- Building AI agents that can use tools (search, database queries, API calls)
- Creating chains of AI steps with complex logic
- Connecting AI models to a wide range of external systems
- Building conversational applications with memory
When to choose LangChain:
- You are building an AI agent or workflow that needs to use multiple tools
- Your application involves complex multi-step reasoning
- You need a large ecosystem of pre-built integrations with external services
- Your team is familiar with Python and comfortable with a moderately steep learning curve
Downsides of LangChain:
- It has grown rapidly and can feel complex and over-engineered for simple use cases
- Documentation quality varies
- The framework changes frequently, which can break existing implementations
LlamaIndex: the data-focused framework
LlamaIndex was built specifically for Retrieval-Augmented Generation (RAG): connecting an AI to your own data sources and retrieving relevant information before answering questions.
What LlamaIndex is good at:
- Building Q&A systems over your documents
- Indexing and retrieving information from PDFs, databases, wikis, and other data sources
- Creating knowledge base assistants that answer questions from your content
- Data pipeline building for AI applications
When to choose LlamaIndex:
- Your primary use case is querying your own data or documents
- You are building an internal knowledge base assistant
- You need sophisticated document parsing and chunking
- Your product's main value is answering questions accurately from specific information sources
Downsides of LlamaIndex:
- Less suitable for agent-type applications where the AI needs to take multiple actions
- Narrower scope than LangChain for applications beyond data retrieval
The practical comparison
| Use case | Better choice | |----------|---------------| | AI that answers questions from your documents | LlamaIndex | | AI agent that browses web, calls APIs, takes actions | LangChain | | Customer support bot trained on your knowledge base | LlamaIndex | | Workflow automation with AI decision points | LangChain | | Product feature that searches and summarises internal data | LlamaIndex | | AI that chains multiple steps and tools | LangChain |
Can you use both?
Yes. LlamaIndex is often used for the data retrieval layer inside a LangChain application. LangChain handles the agent logic and tool routing. LlamaIndex handles the document indexing and retrieval. This is a common architecture for production AI applications.
What this means if you are not a developer
If you are a founder or business owner, the choice between LangChain and LlamaIndex is your developer's decision, not yours. The relevant question for you is: "What does my AI application need to do?"
If the answer is "answer questions from our documents and knowledge base," tell your developer you need a RAG application. They will likely use LlamaIndex for the data layer.
If the answer is "take actions based on data from multiple systems," tell your developer you need an AI agent. They will likely use LangChain.
Building the right architecture from the start
Choosing the wrong framework for your use case is a recoverable mistake but an expensive one. Our AI integration team has experience with both frameworks and can advise on the right architecture before any code is written.
Use the AI feasibility checker to describe your use case. Then get in touch and we will tell you which approach makes sense and what it would cost to build.