LangGraph vs CrewAI vs AutoGen
Graphs vs role-based vs conversation-based orchestration — three philosophies of multi-step agents.
LangGraph vs CrewAI vs AutoGen is a free AI Agents lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Three Big Frameworks
For multi-step / multi-agent systems, three frameworks dominate:
- LangGraph — explicit graph of nodes and edges
- CrewAI — role-based teams of agents
- AutoGen — free-form conversation between agents
LangGraph Style
State machine — you define nodes, edges, and a typed state object. Best when control flow is explicit.
from langgraph.graph import StateGraph, END
g = StateGraph(State)
g.add_node('plan', plan_fn)
g.add_node('act', act_fn)
g.add_edge('plan', 'act')
g.add_conditional_edges('act', router, {'continue': 'plan', 'done': END})
app = g.compile()CrewAI Style
You define agents (with role, goal, backstory) and tasks. Assign tasks to agents; they execute as a "crew":
from crewai import Agent, Task, Crew
researcher = Agent(role='Researcher', goal='Find facts', backstory='...', tools=[search_tool])
writer = Agent(role='Writer', goal='Compose answers', backstory='...')
tasks = [
Task(description='Research X', agent=researcher),
Task(description='Write summary', agent=writer)
]
crew = Crew(agents=[researcher, writer], tasks=tasks)
result = crew.kickoff()AutoGen Style
Conversation-driven. Agents send messages to each other; a manager decides who speaks next:
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent('coder', llm_config={'model': 'gpt-4o-mini'})
user = UserProxyAgent('user', code_execution_config={'work_dir': './work'})
user.initiate_chat(assistant, message='Write a fibonacci function')When to Use LangGraph
- Need explicit state machines
- Loops, branching, human-in-the-loop
- Want checkpoints for durability
- Production reliability is a priority
When to Use CrewAI
- Role-based mental model fits your team
- Sequential or hierarchical task lists
- Want a high-level abstraction
- Less code for typical use cases
When to Use AutoGen
- Research-y, conversation-driven tasks
- Code generation with execution agents
- Multi-agent debate / critique
Maturity Comparison
| LangGraph | CrewAI | AutoGen | |
|---|---|---|---|
| Maintainer | LangChain | CrewAI Inc | Microsoft |
| Stability | High | Medium | Medium |
| Observability | LangSmith | Built-in basic | Custom |
Performance
Frameworks add overhead. For low-latency agents (chat UIs), minimal hand-rolled code often beats frameworks. For complex multi-step agents, the framework saves more in dev time than it costs in overhead.
LangGraph Has the Strongest Production Story
Checkpointers (Postgres, Sqlite), human-in-the-loop, retries, streaming, time travel — production primitives are first-class. Other frameworks rely more on user code.
CrewAI Has the Best DX for Simple Cases
Defining a few agents and tasks is genuinely shorter and more declarative than LangGraph for crews of 3-5 agents.
AutoGen Excels at Code Agents
Microsoft uses AutoGen internally for code-execution agents (similar to GitHub Copilot Workspace). The code-execution loop is best in class.
You Can Mix
It's not all-or-nothing. Use LangGraph for the outer state machine and AutoGen for an inner code-gen sub-agent, for example.
Don't Frame-Frame
Many teams over-invest in framework choice when their agent is simple enough to hand-roll in 100-200 lines. Start with the simplest thing; adopt a framework when you genuinely need it.
Framework Strengths
Which framework has the strongest production story?
Recap
LangGraph for explicit state machines and production. CrewAI for declarative role-based crews. AutoGen for conversation-driven agents. Pick by control-flow style.
Frequently asked questions
Is the “LangGraph vs CrewAI vs AutoGen” lesson free?
Yes — the full text of “LangGraph vs CrewAI vs AutoGen” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.
What will I learn in “LangGraph vs CrewAI vs AutoGen”?
Graphs vs role-based vs conversation-based orchestration — three philosophies of multi-step agents. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Agents?
No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “LangGraph vs CrewAI vs AutoGen” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Agents lesson?
Yes. Every AI Agents lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- LangGraph vs CrewAI vs AutoGen
- Letta (formerly MemGPT) for Long-Lived Agents
- OpenAI Assistants API and Threads
- Choosing the Right Framework Per Use Case