AG2 vs CrewAI

Overview

A comparison of two multi-agent frameworks: AG2 (formerly AutoGen) vs CrewAI. Both enable building multi-agent systems but with fundamentally different approaches.

At a Glance

Aspect	AG2	CrewAI
Paradigm	Conversation-centric	Role-based orchestration
Programming Model	Natural language conversations	Structured task delegation
Code Execution	✅ Built-in (Docker sandbox)	⚠️ Via tools
Human-in-the-Loop	✅ Excellent	⚠️ Limited
Learning Curve	Medium-High	Low
Microsoft Backing	✅ Yes	❌ No
Best For	Complex problem-solving	Structured workflows

Deep Dive

AG2 (AutoGen 2.0)

Core Philosophy: Complex problems are best solved through conversation between specialized agents.

Strengths:

• Natural Paradigm: Think in terms of conversations, not rigid workflows
• Built-in Code Execution: Safe sandboxed execution with Docker
• Human-in-the-Loop: Seamless human intervention at any point
• Group Chat Management: Orchestrates multi-agent discussions
• Flexible: Works for simple and extremely complex scenarios
• Microsoft Research: Strong backing and enterprise focus

Weaknesses:

• Learning Curve: Conversation paradigm takes time to master
• Documentation: Can be scattered across examples and docs
• Performance: Conversation overhead for simple tasks
• Debugging: Harder to trace multi-agent conversations

Best For:

• Complex problem-solving requiring iteration
• Code generation and execution workflows
• Scenarios needing human oversight
• Research and experimental projects

CrewAI

Core Philosophy: Define roles, assign tasks, and let specialized agents collaborate.

Strengths:

• Simple Mental Model: Roles → Tasks → Process → Result
• Low Learning Curve: Easy to get started
• Structured Output: Predictable, organized workflows
• Role Definition: Clear agent identities and responsibilities
• Process Management: Sequential, hierarchical, or custom processes
• Good Documentation: Clear examples and guides

Weaknesses:

• Less Flexible: More rigid than conversation-based approach
• No Built-in Code Execution: Requires custom tool implementation
• Limited Human Intervention: Harder to intervene mid-process
• Smaller Ecosystem: Fewer integrations than AG2

Best For:

• Structured, repeatable workflows
• Content creation pipelines
• Research and analysis tasks
• Teams new to multi-agent systems

Code Comparison

Simple Task

AG2:

import autogen

assistant = autogen.AssistantAgent(
    name="Assistant",
    llm_config={"config_list": [{"model": "gpt-4o"}]},
    system_message="You are a helpful assistant."
)

user_proxy = autogen.UserProxyAgent(
    name="UserProxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=10
)

user_proxy.initiate_chat(
    assistant,
    message="Write a Python function to calculate Fibonacci numbers."
)

CrewAI:

from crewai import Agent, Task, Crew

researcher = Agent(
    role='Researcher',
    goal='Gather and analyze information',
    backstory='Expert researcher with deep analytical skills',
    verbose=True
)

task = Task(
    description='Research Fibonacci sequence and its applications',
    expected_output='Comprehensive research report',
    agent=researcher
)

crew = Crew(
    agents=[researcher],
    tasks=[task],
    verbose=True
)

result = crew.kickoff()

Multi-Agent Collaboration

AG2 (Group Chat):

from autogen import GroupChat, GroupChatManager

writer = autogen.AssistantAgent(name="Writer", ...)
editor = autogen.AssistantAgent(name="Editor", ...)
fact_checker = autogen.AssistantAgent(name="FactChecker", ...)

group_chat = GroupChat(
    agents=[writer, editor, fact_checker],
    messages=[],
    max_round=12,
    speaker_selection_method="auto"
)

manager = GroupChatManager(groupchat=group_chat, llm_config={...})

user_proxy.initiate_chat(
    manager,
    message="Write an article about AI agents."
)

CrewAI (Process):

from crewai import Agent, Task, Crew, Process

writer = Agent(role='Writer', goal='Write engaging content', ...)
editor = Agent(role='Editor', goal='Refine and improve content', ...)
fact_checker = Agent(role='Fact Checker', goal='Verify all claims', ...)

write_task = Task(description='Write article...', agent=writer)
edit_task = Task(description='Edit article...', agent=editor)
verify_task = Task(description='Verify facts...', agent=fact_checker)

crew = Crew(
    agents=[writer, editor, fact_checker],
    tasks=[write_task, edit_task, verify_task],
    process=Process.sequential  # or hierarchical
)

result = crew.kickoff()

Code Execution

AG2 (Built-in):

code_executor = autogen.UserProxyAgent(
    name="CodeExecutor",
    code_execution_config={
        "work_dir": "coding",
        "use_docker": True,  # Safe sandbox
        "timeout": 60
    }
)

coder = autogen.AssistantAgent(
    name="Coder",
    system_message="Write clean, efficient Python code."
)

coder.initiate_chat(
    code_executor,
    message="Write and run a function to sort using quicksort."
)

CrewAI (Via Tool):

from crewai import Agent, Task, Crew
from crewai_tools import CodeInterpreterTool

coder = Agent(
    role='Coder',
    goal='Write and execute code',
    tools=[CodeInterpreterTool()],
    backstory='Expert Python developer'
)

task = Task(
    description='Write and run a quicksort implementation',
    expected_output='Sorted output and performance metrics',
    agent=coder
)

crew = Crew(agents=[coder], tasks=[task])
result = crew.kickoff()

When to Choose Which

Choose AG2 When:

• You need code execution capabilities
• You want human-in-the-loop support
• You're building complex, iterative problem-solving systems
• You prefer conversation-based programming
• You're in a Microsoft/.NET ecosystem

Choose CrewAI When:

• You want a simple, structured approach
• You're building content creation or research pipelines
• You need predictable, repeatable workflows
• You're new to multi-agent systems
• You value clear documentation and examples

Migration Path

• Starting fresh: Choose based on your workflow style preference
• From AG2 to CrewAI: Restructure conversations as role-based tasks
• From CrewAI to AG2: Convert tasks into agent conversations

Conclusion

AG2 excels at complex, iterative problem-solving with code execution and human oversight. CrewAI shines at structured, repeatable workflows with clear role definitions. Choose based on your project's complexity and your team's preferred programming paradigm.

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Last updated: May 2026