CrewAI vs AutoGen vs Pydantic AI: A Hands-On Agent Framework Shootout

I rebuilt the same agent task in three frameworks last week. The brief was deliberately mundane: take a URL, summarize the page, and tag it with three keywords pulled from a fixed taxonomy. Nothing fancy, nothing benchmark-friendly. Just enough work to expose how each framework actually feels when you have to ship something.

I came in with priors and left with different ones. Here is the honest write-up.

The setup

Same model, same tool, same input, same expected output. I gave each framework one Python tool function that fetches a URL and returns its text, then asked the agent to call the tool, summarize, and tag. The taxonomy was a small enum of categories like research, news, opinion, tutorial.

I have built a few agents before. If you are starting from zero, the How to Build with AI Agents post is a good primer first.

CrewAI: roles you can almost see

CrewAI was the fastest to get to first output. The mental model snapped into place quickly. You define an agent with a role, a goal, and a backstory. You define a task with a description and an expected output. You stand them up in a Crew. The framework executes the task by giving the agent a chance to call tools, then returns the result.

from crewai import Agent, Task, Crew, tools

@tools.tool("fetch_url")
def fetch_url(url: str) -> str:
    """Fetch the text content of a URL."""
    import requests
    return requests.get(url, timeout=10).text

summarizer = Agent(
    role="Web Page Summarizer",
    goal="Read URLs and produce a tight summary plus three taxonomy tags",
    backstory="You are concise. You never hallucinate.",
    tools=[fetch_url]
)

task = Task(
    description="Fetch {url}, summarize, and tag with three of: research, news, opinion, tutorial.",
    agent=summarizer,
    expected_output="JSON with keys 'summary' and 'tags'."
)

result = Crew(agents=[summarizer], tasks=[task]).kickoff(inputs={"url": "https://example.com"})

What I liked: the role-and-goal pattern is great writing prompt engineering scaffolding. It nudges you to think about what the agent should be, not just what it should do.

What bit me: the structured output story is text-based. I asked for JSON, I got JSON shaped text, then parsed it myself. There are validators, but I had to wire them up. For a single-task pipeline, that is fine. For a long workflow with intermediate types, it gets tedious.

Best for: pipelines that decompose into human-shaped roles, fast prototyping, content and research workflows.

AutoGen: conversation as the engine

AutoGen flips the abstraction. Instead of a task assigned to an agent, you have agents talking. There is an assistant agent, a user proxy that can execute tools, and the conversation runs until a termination condition fires.

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")

assistant = AssistantAgent(
    name="summarizer",
    model_client=model_client,
    tools=[fetch_url_tool],
    system_message="Fetch the URL, summarize, return JSON with summary and tags."
)

team = RoundRobinGroupChat([assistant])
result = await team.run(task="Summarize https://example.com")

What I liked: when I pushed the task harder by adding a critic agent that checked the summary for length and tag validity, AutoGen made that natural. Agents debating each other, revising outputs, and stopping when satisfied is a clean fit for the conversation primitive.

What bit me: for a single-agent task this felt like overkill. I had to learn enough of the framework to build a multi-agent setup just to run a one-agent task. The recent split into Core, AgentChat, and Extensions APIs also means you spend longer reading docs to figure out the right entry point. AutoGen Studio is a real win if you want to prototype visually.

Best for: multi-agent systems where the natural shape is debate, critique, or revision loops.

Pydantic AI: types all the way down

Pydantic AI was the slowest to type out and the easiest to maintain. The agent returns a typed Pydantic model. Tools take typed inputs through a RunContext. Logfire wires up tracing without configuration. The whole thing feels like writing a normal Python service with an LLM as one component, rather than fighting an agent framework.

from pydantic_ai import Agent, RunContext
from pydantic import BaseModel
import httpx

class Output(BaseModel):
    summary: str
    tags: list[str]

agent = Agent(
    "openai:gpt-4o-mini",
    output_type=Output,
    system_prompt="Summarize and tag URLs. Tags must be from: research, news, opinion, tutorial."
)

@agent.tool
async def fetch_url(ctx: RunContext, url: str) -> str:
    async with httpx.AsyncClient() as client:
        return (await client.get(url, timeout=10)).text

result = agent.run_sync("Summarize https://example.com")
print(result.output.summary, result.output.tags)

What I liked: I never wrote a JSON parser. I never wrote a validator. The output came back as an Output object with the right fields, or the framework retried. Logfire showed me each LLM call, the cost, and the timing. This is the framework I would pick for production work where the agent is part of a larger typed system.

What bit me: the multi-agent story is younger than CrewAI's or AutoGen's. There is graph support, agent-to-agent communication, and durable execution, but the patterns are not as well documented. If you need a six-agent crew today, the other two are more mature.

Best for: production agents that produce structured data for a typed downstream system.

Where each one wins

After the experiment my matrix looked like this:

If you want	Pick
To prototype a role-based pipeline today	CrewAI
Multi-agent debate, critique, or coordination	AutoGen
Type-safe single agent for production	Pydantic AI
Visual agent design	AutoGen Studio
Tracing, cost tracking, replay	Pydantic AI plus Logfire

If you are picking one and you do not yet know which shape your problem has, start with Pydantic AI. The type discipline forces you to think clearly about your inputs and outputs, which is the part most agent projects skip and pay for later. Migrating to a multi-agent framework once you understand the problem is cheaper than ripping out a poorly-shaped abstraction.

What I keep doing across all three

Some habits I stole from one framework and applied to the others:

1. Define your output schema first, even if the framework does not enforce it. CrewAI lets you skip this. Do not skip it.
2. Add a critic, even just an inline validator, before you ship. Wrong tags, hallucinated summaries, and off-taxonomy outputs are the most common failures.
3. Trace every LLM call in development. Pydantic AI gives this for free with Logfire. The other two need wiring, but every minute spent wiring traces saves an hour debugging.

External references: the CrewAI repository, the AutoGen repository, and the Pydantic AI repository all have live examples worth reading before you commit.

Tools mentioned in this post

• CrewAI: Role-based agent orchestration with crews and tasks.
• AutoGen: Microsoft's multi-agent conversation framework.
• Pydantic AI: Type-safe agent framework with structured outputs and Logfire.