AutoGen to CrewAI Migration Guide

AutoGen and CrewAI approach multi-agent orchestration from opposite directions. AutoGen centers on conversation patterns between agents. CrewAI centers on role-based task execution. Switching between them isn't a drop-in replacement; it's a redesign of how your agents communicate and divide work.

This guide covers the architectural differences, concept mapping, migration steps, and code examples for teams moving from AutoGen to CrewAI.

Why Teams Switch

The most common reasons teams move from AutoGen to CrewAI in 2026:

1. Simpler setup for structured workflows. CrewAI requires less boilerplate to define agents with clear roles, goals, and task assignments. AutoGen's conversational approach needs more configuration for structured, predictable workflows.

1. Role-based design matches team structures. CrewAI's Agent → Task → Crew hierarchy maps naturally to business processes where each team member has a defined role. AutoGen's conversation-based model is more flexible but harder to map to organizational structures.

1. Faster prototyping. CrewAI's higher-level abstractions (Agent, Task, Crew, Process) let teams build working multi-agent systems in fewer lines of code. AutoGen offers more control but requires more code.

1. Better documentation and community growth. CrewAI's documentation and community have expanded significantly in 2025-2026, while AutoGen went through a major architectural transition from v0.2 to AG2/v0.4.

Concept Mapping: AutoGen → CrewAI

| AutoGen Concept | CrewAI Equivalent | Key Difference |
|---|---|---|
| AssistantAgent | Agent (with role, goal, backstory) | CrewAI agents have explicit role definitions and goals |
| UserProxyAgent | Not needed | CrewAI handles human-in-the-loop differently (via human_input flag) |
| GroupChat | Crew (with agents and tasks) | Crew orchestrates agents through tasks, not open conversation |
| GroupChatManager | Process (sequential or hierarchical) | CrewAI uses process types instead of chat managers |
| register_function | @tool decorator | Both support custom tool registration |
| initiate_chat() | crew.kickoff() | Different entry points for starting multi-agent workflows |
| Conversation patterns | Task dependencies | CrewAI uses explicit task ordering instead of conversation flow |

Migration Steps

Step 1: Map Your Agents

AutoGen:

python
from autogen import AssistantAgent, UserProxyAgent
researcher = AssistantAgent(
    name="researcher",
    system_message="You research topics thoroughly.",
    llm_c{"model": "gpt-4"}
)
writer = AssistantAgent(
    name="writer",
    system_message="You write clear, engaging content.",
    llm_c{"model": "gpt-4"}
)
user_proxy = UserProxyAgent(
    name="user_proxy",
    humaninputmode="NEVER",
    codeexecutionc{"work_dir": "output"}
)

CrewAI:

python
from crewai import Agent
researcher = Agent(
    role="Senior Research Analyst",
    goal="Find accurate, comprehensive information on the given topic",
    backstory="You are an experienced researcher with expertise in finding and synthesizing information from multiple sources.",
    verbose=True,
    llm="gpt-4"
)
writer = Agent(
    role="Content Writer",
    goal="Write clear, engaging content based on research findings",
    backstory="You are a skilled writer who transforms research into readable, well-structured content.",
    verbose=True,
    llm="gpt-4"
)

Key changes:

• No UserProxyAgent needed. CrewAI doesn't use proxy agents for human interaction.


• Add role, goal, and backstory to each agent. These guide the agent's behavior more explicitly than AutoGen's system_message.


• system_message maps roughly to backstory but CrewAI separates role, goal, and context.

Step 2: Convert Tasks

AutoGen uses conversation initiation:

python
userproxy.initiatechat(
    researcher,
    message="Research the latest developments in quantum computing."
)

CrewAI uses explicit Task objects:

python
from crewai import Task
research_task = Task(
    description="Research the latest developments in quantum computing. Focus on practical applications, key players, and recent breakthroughs.",
    expected_output="A detailed research report with sources, key findings, and analysis.",
    agent=researcher
)
writing_task = Task(
    description="Write an engaging article based on the research findings about quantum computing.",
    expected_output="A well-structured 1,500-word article suitable for a tech blog.",
    agent=writer
)

Key changes:

• Each task has an explicit expected_output that guides the agent.


• Tasks are assigned to specific agents.


• Task ordering is handled by the Crew's process type, not conversation flow.

Step 3: Replace GroupChat with Crew

AutoGen:

python
from autogen import GroupChat, GroupChatManager
groupchat = GroupChat(
    agents=[user_proxy, researcher, writer],
    messages=[],
    max_round=10
)
manager = GroupChatManager(groupchat=groupchat)
userproxy.initiatechat(manager, message="Research and write about quantum computing.")

CrewAI:

python
from crewai import Crew, Process
crew = Crew(
    agents=[researcher, writer],
    tasks=[researchtask, writingtask],
    process=Process.sequential,
    verbose=True
)
result = crew.kickoff()

Key changes:

• No chat manager. The Crew handles orchestration.


• Process.sequential executes tasks in order. Process.hierarchical adds a manager agent that delegates.


• Task output from one agent automatically passes to the next.

Step 4: Migrate Custom Tools

AutoGen:

python
@userproxy.registerfor_execution()
@researcher.registerforllm(description="Search the web")
def web_search(query: str) -> str:
    return search_results

CrewAI:

python
from crewai.tools import tool
@tool
def web_search(query: str) -> str:
    """Search the web for information on the given query."""
    return search_results
researcher = Agent(
    role="Senior Research Analyst",
    goal="Find accurate information",
    backstory="Experienced researcher.",
    tools=[web_search]
)

Key changes:

• CrewAI uses a @tool decorator instead of registration functions.


• Tools are assigned directly to agents via the tools parameter.


• The docstring serves as the tool description for the LLM.

Architecture Differences That Matter

Conversation vs. Task execution. AutoGen agents converse freely, with the GroupChat managing turns. CrewAI agents execute assigned tasks sequentially or hierarchically. If your AutoGen workflow relies on agents debating or negotiating, you'll need to restructure for CrewAI's task-based model. State management. AutoGen maintains conversation history as the primary state. CrewAI passes task outputs between agents. If your AutoGen agents reference earlier conversation turns for context, you'll need to include that context in CrewAI task descriptions or use shared memory. Human-in-the-loop. AutoGen's UserProxyAgent with humaninputmode handles human interaction. CrewAI uses a human_input=True flag on tasks that require human approval before proceeding. Code execution. AutoGen has built-in code execution via UserProxyAgent. CrewAI uses the CodeInterpreterTool from crewai_tools for similar functionality.

When NOT to Switch

Stay on AutoGen if:

• Your workflow requires free-form agent conversation and debate


• You need fine-grained control over which agent speaks next


• Your agents negotiate or iterate on solutions through discussion


• You're heavily invested in AutoGen's code execution infrastructure

Switch to CrewAI if:

• Your workflow has clear role assignments and task sequences


• You want faster setup with less boilerplate code


• Your team thinks in terms of roles and responsibilities rather than conversations


• You need a more active community and documentation ecosystem in 2026