LangChain, LangGraph, LlamaIndex: Which Framework for Your AI?

Building a serious AI application—an assistant, an agent, a RAG system—requires orchestrating multiple steps: fetching data, calling a model, using tools, chaining tasks. Three frameworks stand out: LangChain, LangGraph and LlamaIndex. They don’t serve the same purpose.

LangChain: The General-Purpose Toolkit

LangChain is the Swiss Army knife: connectors to models, prompt management, step chains, tool integration. Useful for quickly assembling AI logic, from simple to complex. Its breadth is also its drawback: it can be overkill for basic needs.

LangGraph: Stateful Agents

LangGraph models AI processes as a graph of stateful steps. It’s the tool when building true agents: loops, decisions, retries, and memory between steps. More structured and robust than linear chains for advanced use cases.

LlamaIndex: Data and RAG Specialist

LlamaIndex focuses on data indexing and RAG: ingesting documents, chunking them, indexing them, and serving search to augment the model. When the project’s core is “making documents speak,” it’s often the most straightforward choice.

Which to Choose—or None at All

• LlamaIndex if the project is primarily document-based RAG.
• LangGraph for agents with logic and state.
• LangChain for quickly assembling varied logic.
• None, sometimes: for a simple case, a few well-written API calls are enough, without the complexity of a framework.

The right choice depends on the use case — which is the focus of our AI project scoping, before building our AI assistants connected to your data.

An AI project to architect? We’ll help you choose (or avoid) the right framework.