Python AI Agent Frameworks Compared
How LangGraph, CrewAI, AutoGen, and smolagents actually differ, and which one fits a new multi-agent Python project
Every framework here solves the same broad problem — coordinating one or more LLM calls to complete a task that’s too complex for a single prompt — but they differ sharply in how much structure they impose and how much control they hand back to you.
The core distinction: graphs, conversations, or roles
LangGraph models a workflow as an explicit graph — nodes are steps, edges define what happens next, including loops back to an earlier step. You can see and control exactly which path execution took, which makes complex, branching reasoning easier to debug than an open-ended agent loop.
AutoGen takes a conversational approach instead — agents message each other in a chat-like loop (a planner, a coder, a critic reviewing output), debating and refining a solution rather than following a pre-defined path.
CrewAI organizes agents by role — a “researcher,” a “writer,” a “reviewer” — each with a defined goal, collaborating on a shared task through a structured process. It’s the most opinionated of the three, and often the fastest to get a working multi-agent setup running.
The lightweight option
smolagents doesn’t fit neatly alongside the three above — it’s deliberately minimal, with agents that write and execute Python code directly to use tools rather than going through a heavier abstraction layer. Worth reaching for specifically when you want to understand or modify the agent loop itself; the whole library is small enough to read in one sitting.
What none of these solve on their own
None of these are about the data or tool layer. LlamaIndex handles ingesting and retrieving your own data for RAG; Instructor gets structured, validated output from a single LLM call. Both are commonly used inside an agent built with one of the frameworks above, not competitors to them.
Which one to pick
- Fastest start, role-based structure, comfortable with less low-level control → CrewAI
- Complex, branching workflow where you need to see and control exactly what happens → LangGraph
- A team of agents debating/refining a solution together → AutoGen
- Want to fully understand and modify the agent loop yourself → smolagents
There’s no single best answer — the right choice depends more on how much structure you want imposed versus how much control you want to keep, not which framework is objectively more capable.
Frequently Asked Questions
Which Python agent framework should you use?
CrewAI for the fastest start with a role-based team structure. LangGraph when you need explicit control over a complex, branching workflow. AutoGen for a conversational multi-agent setup. smolagents when you want something small enough to fully understand and modify yourself.
What’s the difference between LangGraph and CrewAI?
LangGraph models a workflow as an explicit graph you define — nodes, edges, and loops — giving precise control over execution paths. CrewAI organizes agents by role in a more structured, opinionated process, closer to modeling a human team, with less low-level control but a faster start.
Is AutoGen the same as LangGraph?
No — AutoGen structures agents as a set of conversable agents that message each other in a chat-like loop, while LangGraph follows an explicit, pre-defined workflow graph. Different mental models for the same broad problem.
What is smolagents best for?
Situations where you want to read and modify the agent loop itself — it’s Hugging Face’s deliberately minimal framework, small enough to understand in one sitting, with agents that write and execute Python code directly to use tools.