CrewAI vs AutoGen: which multi-agent framework should you use?

from crewai import Agent, Task, Crew
from crewai_tools import tool
import os

# Define tools
@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

# Create agents with roles
researcher = Agent(
    role="Research Analyst",
    goal="Find accurate information on given topics",
    backstory="Expert researcher with 10 years experience",
    tools=[search_web],
    llm_provider="openai",  # CrewAI uses provider abstraction
    model="gpt-4o-mini"
)

writer = Agent(
    role="Content Writer",
    goal="Write clear, engaging content",
    backstory="Award-winning writer",
    model="gpt-4o-mini"
)

# Define tasks
research_task = Task(
    description="Research artificial intelligence trends in 2026",
    agent=researcher,
    expected_output="Detailed research findings"
)

write_task = Task(
    description="Write an article based on research findings",
    agent=writer,
    expected_output="Polished article (500 words)"
)

# Execute in crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    verbose=True  # CrewAI handles orchestration; you define, it runs
)

result = crew.kickoff()

from autogen import ConversableAgent, GroupChat, GroupChatManager
import os

# Define LLM config
llm_config = {
    "config_list": [{"model": "gpt-4o-mini", "api_key": os.environ.get("OPENAI_API_KEY")}]
}

# Create agents with ConversableAgent
researcher = ConversableAgent(
    name="researcher",
    system_prompt="You are a research analyst. Find and summarize key information.",
    llm_config=llm_config,
    human_input_mode="NEVER"
)

writer = ConversableAgent(
    name="writer",
    system_prompt="You are a content writer. Write engaging articles based on research.",
    llm_config=llm_config,
    human_input_mode="NEVER"
)

# Define group chat with explicit turn management
group_chat = GroupChat(
    agents=[researcher, writer],
    messages=[],
    max_round=4,  # AutoGen requires explicit turn limits
    speaker_selection_method="round_robin"  # Manual turn ordering
)

manager = GroupChatManager(groupchat=group_chat, llm_config=llm_config)

# Initiate conversation
researcher.initiate_chat(
    manager,
    message="Research artificial intelligence trends in 2026, then write a 500-word article."
)