LangChain vs CrewAI vs AutoGen: What I'd Pick for an Enterprise Agent in 2026

Every few weeks someone asks me which agent framework to use. The honest answer starts with a question: what does your agent need to survive in production? Features and GitHub stars matter less than state handling, observability, permissions, and testing.

This post compares LangChain (with LangGraph), CrewAI, and AutoGen on those four criteria. The code sketches are minimal and written from memory of each project's documentation. These libraries change quickly, so check the current docs and release notes before you copy anything.

The four criteria

1. State. Can you persist where a task is, resume it after a crash, and inspect it?
2. Observability. Can you trace every model call, tool call, and handoff for one task?
3. Permissions. Can you limit which tools an agent can use and require approval for risky actions?
4. Testing. Can you run a saved set of cases after every change and compare results?

A framework doesn't have to do all four itself. It has to make them possible without fighting it.

LangChain and LangGraph

LangGraph models an agent as a graph of steps that share a typed state. That fits criterion one well, because state is explicit and you can add persistence.

from typing import TypedDict
from langgraph.graph import StateGraph, END

class State(TypedDict):
    question: str
    answer: str

def answer(state: State) -> dict:
    return {"answer": call_your_model(state["question"])}

graph = StateGraph(State)
graph.add_node("answer", answer)
graph.set_entry_point("answer")
graph.add_edge("answer", END)

app = graph.compile()
print(app.invoke({"question": "What is our refund window?"}))

What I like: explicit state, conditional edges, and a clear place to insert an approval step. What to watch: the ecosystem is large and moves fast, so pin versions and keep your own thin wrapper around it.

CrewAI

CrewAI describes agents by role and goal, then groups them into a crew that works through tasks. It's quick to start, and the role language is easy for non-engineers to read.

from crewai import Agent, Task, Crew

analyst = Agent(
    role="Support analyst",
    goal="Classify incoming tickets and draft a reply",
    backstory="You work on a customer support team.",
)

task = Task(
    description="Classify this ticket and draft a short reply: {ticket}",
    expected_output="A category and a draft reply",
    agent=analyst,
)

crew = Crew(agents=[analyst], tasks=[task])
result = crew.kickoff(inputs={"ticket": "My order arrived damaged."})
print(result)

What I like: fast prototyping and readable structure. What to watch: you'll need to add your own persistence, approval steps, and tracing for production, and role-based prompts can hide how decisions get made.

AutoGen

AutoGen focuses on conversations between agents. Recent versions split the project into layers, and the high-level chat API is async.

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    model = OpenAIChatCompletionClient(model="your-model-name")
    agent = AssistantAgent("helper", model_client=model)
    result = await agent.run(task="Summarize this ticket in one line.")
    print(result.messages[-1].content)

asyncio.run(main())

What I like: a clean model for multi-agent conversation and good building blocks. What to watch: check the project's current status and recommended successor before you commit, because the Microsoft agent tooling has been changing. Pick a framework whose maintainers' roadmap you've actually read.

Side by side

Criterion	LangGraph	CrewAI	AutoGen
State	Explicit, good fit	Add your own	Add your own
Observability	Tracing integrations	Add your own	Add your own
Permissions	Build approval nodes	Build around tools	Build around tools
Testing	Bring your own eval set	Bring your own eval set	Bring your own eval set

The last row is the important one. None of these gives you a test suite. You still need a saved set of cases and a script that runs them after every change.

What I'd pick

For a workflow with clear steps, approvals, and an audit trail, I'd start with LangGraph because explicit state makes the other criteria easier. For a quick prototype that business teams will read, CrewAI gets you there fastest. For research on multi-agent conversation, AutoGen is worth studying.

And if the real need is permissions, approvals, tracing, and model switching across several teams, you may not want to assemble those from a framework at all. At that point, compare a platform before you build the plumbing yourself.

A checklist before you commit

• Can I persist and resume a task after a restart?
• Can I see one trace per task with every call?
• Can I require approval for specific tools?
• Can I rerun fifty saved cases after each change?
• Who maintains this in a year, and what is its roadmap?

If you want the longer comparison, I wrote up LangChain vs CrewAI vs AutoGen compared. I work on enterprise agent deployments at AI Hive. Which of the four criteria has caused you the most trouble so far?