Multi-Agent Frameworks

Why Multi-Agent? Single agents struggle with complex tasks. Multi-agent systems break problems down by having specialized agents collaborate, each with distinct roles and capabilities.

Microsoft AutoGen

Overview Framework for building multi-agent conversational systems with flexible agent orchestration.

Key Concepts

Example

from autogen import AssistantAgent, UserProxyAgent

# Create agents
assistant = AssistantAgent(
    name="assistant",
    llm_config={"model": "gpt-4o"}
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding"}
)

# Start conversation
user_proxy.initiate_chat(assistant, message="Analyze sales data")

CrewAI

Overview Framework for orchestrating role-playing AI agents working together as a "crew" on complex tasks.

Key Components

Example

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Analyst",
    goal="Find accurate information",
    backstory="Expert researcher with attention to detail"
)

writer = Agent(
    role="Content Writer",
    goal="Create engaging content",
    backstory="Experienced writer"
)

research_task = Task(description="Research AI trends", agent=researcher)
writing_task = Task(description="Write article based on research", agent=writer)

crew = Crew(agents=[researcher, writer], tasks=[research_task, writing_task])
result = crew.kickoff()

Comparison

Multi-Agent Patterns

• Hierarchical: Manager delegates to workers
• Sequential: Agents work in order
• Collaborative: Agents discuss and iterate
• Competitive: Agents propose, vote on solutions