AI agent frameworks promise to turn a raw language model into something that can plan, call tools, remember context, and coordinate with other agents. Four names dominate the conversation: LangChain, LlamaIndex, AutoGen, and CrewAI. They overlap but were built with different priorities, so picking the right one depends on what you are actually building.
LangChain: the broad orchestration layer
LangChain is the most general of the four. It provides building blocks for chaining model calls, attaching tools, managing memory, and constructing agents that decide which actions to take. Its strengths are breadth and ecosystem: it integrates with hundreds of model providers, vector stores, and APIs, and it ships with LangSmith for tracing and evaluation. The cost of that breadth is a steeper learning curve and frequent API changes. LangChain suits teams building complex, multi-tool workflows who want one framework that covers most needs.
LlamaIndex: the data and retrieval specialist
LlamaIndex is built around one core problem: connecting language models to your own data. It excels at ingesting documents, chunking and indexing them, and running retrieval-augmented generation (RAG) cleanly. If your application is fundamentally “answer questions over my documents,” LlamaIndex gives you the most direct path with sensible defaults. It also offers agent features, but its centre of gravity is data. Many production stacks pair LlamaIndex for retrieval with another framework for orchestration.
AutoGen: conversational multi-agent control
Microsoft’s AutoGen treats agents as participants in a conversation. You define agents — an assistant, a user proxy, a code executor — and they exchange messages to solve a task, including writing and running code. AutoGen gives fine-grained control over how agents talk and when humans step in, which makes it powerful for research-style automation and complex problem decomposition. That control comes with more setup than higher-level alternatives.
CrewAI: role-based agent teams
CrewAI uses an intuitive metaphor: you create agents with roles and goals, assign them tasks, and group them into a crew that executes sequentially or in parallel. This makes it one of the fastest frameworks to get a working multi-agent prototype running. It trades some low-level control for clarity and speed, which is ideal for business-process automation where the workflow maps neatly to roles.
Choosing the right one
Pick LlamaIndex when the job is RAG over your data. Pick LangChain when you need a flexible, well-supported orchestration layer for diverse tools and chains. Pick CrewAI when you want readable, role-based multi-agent workflows up quickly. Pick AutoGen when you need precise control over agent conversations and code execution. And remember: for a single prompt with one tool, skip the framework entirely and use the provider SDK. Frameworks are worth adopting only once the orchestration genuinely becomes the hard part.