LangGraph vs AutoGen — My first Python AI agent, what should I start with?

Do you want to build your own AI agent with Python but don't know which tool to start with? The two leading contenders in agent development, LangGraph and AutoGen, are fundamentally different in their basic approach to solving problems. I'll break down the key differences between these two frameworks with a simple analogy so you can quickly find the perfect partner for your first project!

LangGraph: Controlling flows like drawing a flowchart

LangGraph is a tool that helps you design your entire automation process like a 'flowchart' or a 'mind map.' Developed by LangChain, it has been steadily improved and solidified its position as a representative graph-based state machine framework since the stable v1.2 version was released in May 2026.

The structure is more intuitive than you might think. You build the flow using 'nodes' that handle specific actions and 'edges' that are the arrows connecting those steps. Within this flow, you clearly exchange a shared data container called 'State.' It's like passing a box containing data from one worker to the next on a factory conveyor belt.

The biggest appeal of LangGraph is its robust 'safety mechanism.' Even if the network is temporarily disconnected during operation or an unexpected error occurs, the checkpointing feature—which records progress in real-time in databases like SQLite—allows you to resume tasks seamlessly from where they stopped.

The code below is a simple example showing how to build the skeleton of LangGraph in Python.

# LangGraph v1.2 기준 기초 흐름 설정 예시
from langgraph.graph import StateGraph, START, END
from typing import TypedDict

# 1. 노드 간에 주고받을 공용 상태 데이터 정의
class AgentState(TypedDict):
    task: str
    result: str

# 2. 순서도(그래프) 객체 생성 및 노드 연결
workflow = StateGraph(AgentState)
workflow.add_node("agent", run_ai_function)  # run_ai_function은 실제 AI 작업 함수
workflow.add_edge(START, "agent")
workflow.add_edge("agent", END)

# 3. 실행할 수 있도록 최종 컴파일
app = workflow.compile()

Whether you are a beginner or an expert, LangGraph is an excellent compass when building unidirectional task automation that strictly follows defined guidelines and rules, or when you need a safe system where results must be predictable.

AutoGen: A group chat room where assistants discuss freely

If LangGraph, which we saw earlier, is a meticulously crafted workflow, AutoGen is more like a group chat room where several experts gather to talk. You place AI assistants—taking on roles like developer, marketer, and planner—into a virtual group chat, and they autonomously ask questions, discuss, and solve complex tasks.

There has been an interesting change in this technology ecosystem recently. AutoGen was completely revamped for v0.4, adopting a cleaner conversation structure, and in April 2026, Microsoft officially released the Microsoft Agent Framework (MAF) 1.0, which refined this further. It's essentially a new standard that adds a stable enterprise-grade backbone to the existing flexible, conversational structure of AutoGen.

However, there is a barrier to entry that might catch Python beginners off guard: asynchronous programming. Because agents need to send and receive messages freely in real-time, you must use async/await keywords when writing code. Unlike intuitive code where the program runs sequentially, in an asynchronous flow with multiple overlapping conversations, it can be somewhat tricky to find out where and why something stopped.

In what situations should you choose which?

Depending on the nature of your project, the choice becomes quite clear. If you want to design unidirectional flows like task automation, report generation, or data cleaning where defined sequences and rules are important, LangGraph is a good choice. Since the developer maintains clear control over the entire flow, it is much easier to catch bugs where an agent might go off the rails.

On the other hand, if you want an autonomous system where multiple AI assistants freely share opinions, try running code themselves, and provide feedback on the results, then AutoGen and the Microsoft Agent Framework are a much better fit. They allow you to easily build a flexible, conversation-centered collaboration environment, just like setting up a group chat room.

However, there is a practical barrier that Python beginners should keep in mind: the difference in coding style. While LangGraph allows you to design intuitive flows using synchronous Python code that we're accustomed to, the AutoGen family deeply utilizes asynchronous (async/await) syntax. For beginners unfamiliar with Python's asynchronous programming, the process of interpreting and controlling the code structure can feel somewhat complex.

I'll summarize the key points to help you decide on the right tool for your goals more easily.

Taking the most important first step

It is easy to get tired if you try to build a massive multi-agent system from the start. Try using LangGraph's simple state management features to create a flow that automatically classifies your to-do items, or use AutoGen to create a small discussion room where two virtual assistants chat. The experience of manually completing even the smallest bit of automation yourself will become the most solid foundation for developing more complex AI agents in the future.