LangGraph vs CrewAI vs AutoGen — I Tested All Three

April 2026 · AI News

The first thing you get stuck on when building an AI agent is choosing a framework. LangGraph, CrewAI, AutoGen — the structures are completely different. I compared them directly to figure out what to use and when.

In late 2025, things changed significantly. Microsoft put AutoGen in maintenance mode and merged it with Semantic Kernel into Microsoft Agent Framework. The community forked it as AG2 and kept going.

All three frameworks target the same "AI agent" goal, but their approaches differ. The choice depends on the situation. The bottom line — LangGraph for complex workflows, CrewAI for fast experiments, AG2 for existing AutoGen codebases.

Why Framework Choice Is Hard

The AI agent ecosystem exploded between 2024 and 2025. The options multiplied, and each framework claims a different advantage. One says it's "easier," another says it's "more flexible."

The problem is that none of them are wrong. Each was built to solve a different problem. LangGraph targets complex control flow. CrewAI targets fast team agent assembly. AutoGen targeted inter-agent debate and validation.

Then AutoGen disappeared, making the comparison even murkier. "Should I use that, move to AG2, or start fresh with something else?" — that question still comes up often in 2026. Here's a breakdown, one by one.

LangGraph — Drawing the Flow as a Graph

LangGraph represents the agent execution flow as a DAG (Directed Acyclic Graph). A DAG is like an electrical circuit diagram — you design the flow directly with nodes and edges. Each node is a unit of work: an LLM call, a tool execution, or a conditional decision.

It supports conditional branching, loops, checkpoints, and state persistence. Complex control flows like "if A returns X, go to B; if Y, go to C" show up clearly in code. On this front, it's clearly stronger than the other two frameworks.

The downside is the upfront design cost. You have to design the graph structure yourself. For simple tasks, that's overkill. When you need a quick prototype, CrewAI is the better call.

LangGraph's Core: State and Checkpoints

The most important concept in LangGraph is State. It's the data store shared across the entire agent execution. Like a shopping cart — no matter which page you navigate to, the items you added are still there.

Checkpoint saves the mid-execution state to disk. If an error occurs or the process stops, it restarts from the last checkpoint. For long-running agents or workflows that need human intervention in the middle, this feature becomes essential.

Human-in-the-loop is also supported. You can implement a flow where the agent pauses before a critical decision and waits for human approval. That fits situations where you want automation, but not full autonomy.

CrewAI — Running Agents Like a Team

CrewAI works by assigning a Role and a Goal to each agent. Define roles like Researcher, Writer, and Reviewer, distribute the tasks, and you have an agent team. The agents collaborate like a real team meeting.

The biggest advantage is the fast start. Thirty lines of Python and a multi-agent pipeline is running. Just define roles and goals in natural language. The agent team works without graph design or state management.

That said, fine-grained control flow is more limited than LangGraph. Complex branching like "if condition A, skip agent B and go to C" is tricky to express in CrewAI. That limitation surfaces when moving from prototype to production.

AutoGen Is Done — Look to AG2

AutoGen started as a conversational multi-agent framework. Agents talk and debate with each other to write and validate code. "Agent A writes the code, Agent B reviews it, C runs it and validates the result" — that was the structure.

In late 2025, Microsoft ended AutoGen development. It merged with Semantic Kernel into a new framework called Microsoft Agent Framework. The direction shifted toward a deeply Microsoft-integrated enterprise solution.

Existing users forked it under the name AG2 and kept going. AG2 maintains API compatibility with AutoGen and continues development independently. Existing AutoGen code runs on AG2 with minimal changes.

All Three Frameworks at a Glance

Structure and strengths are completely different. Check the table below to get a quick read on each framework's position before choosing.

Pricing and Licensing

All three frameworks are open source. There's no licensing cost for basic use. Deploy on your own infrastructure and you only pay for LLM API calls.

AG2 is a community-maintained project, so long-term support is uncertain. If you're planning long-term production use, factor that in. For quick experiments and research, it's fine.

Situation-Based Selection Guide

Need complex workflows, conditional branching, and state persistence — that's LangGraph. Want to quickly assemble an agent team and experiment — CrewAI fits. Already have an AutoGen codebase — migrate to AG2.

Combination Patterns Used in Practice

Mixing all three is also an option. The most common pattern is using CrewAI to quickly set up the team structure, then swapping only the complex core nodes with LangGraph. The code structure for each framework looks like this.