Should I use LangChain or LlamaIndex for my AI agent?
Key differences
LlamaIndex is built around data indexing and retrieval. It shines when your agent needs to query large document sets, databases, or APIs with a focus on RAG. Its abstractions are more focused and often easier to grasp for data-centric tasks.
LangChain is a general-purpose framework for chaining LLM calls, tools, and memory. It has more integrations and community support, but its abstractions can be leaky and the API changes frequently. It's better for complex workflows that involve many external services.
- LlamaIndex: best for RAG, document Q&A, knowledge bases
- LangChain: best for multi-step workflows, tool use, broad integrations
- Both support agents, but LlamaIndex's agents are more retrieval-focused
- LangChain has LangGraph for stateful, multi-actor agents
When to use neither
If your agent only needs to call one or two tools and doesn't require complex memory, you can build it directly with an LLM API. This avoids framework overhead and makes debugging simpler.
Many production teams start with a framework for prototyping, then rewrite core logic in plain code for performance and control. That's a valid path.
Common mistakes
- Assuming you must pick one forever; you can use both in different parts of a system.
- Choosing LangChain just because it's popular, even when your task is pure retrieval.
- Underestimating the learning curve and time needed to master either framework.
