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Multi-Agent Collaboration: AutoGen & CrewAI

When tasks exceed the capability of a single prompt or agent loop, Multi-Agent Frameworks coordinate specialized teams of agents with complementary roles, skills, and tools.

Multi-Agent Advantage

Assigning specific personas (e.g. Senior Software Architect, Security Auditor, Code Writer) reduces prompt dilution and improves complex problem solving.

1. Microsoft AutoGen​

AutoGen is an open-source programming framework designed for building agentic AI applications that can act autonomously or work in collaboration with humans. It enables the creation of multi-agent systems where multiple agents, leveraging LLMs, tools, or human input, work together to solve complex tasks.

  • AutoGen Core: An event-driven framework for building scalable, distributed multi-agent systems.
  • AgentChat: A programming framework focused on conversational single and multi-agent applications.
  • AutoGen Studio: A web-based UI for prototyping and managing agents without writing code.
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

coder = AssistantAgent(name="Coder", llm_config=llm_config)
reviewer = AssistantAgent(name="Code_Reviewer", llm_config=llm_config)
user_proxy = UserProxyAgent(name="User", code_execution_config={"work_dir": "workspace"})

group_chat = GroupChat(agents=[user_proxy, coder, reviewer], messages=[], max_round=12)
manager = GroupChatManager(groupchat=group_chat, llm_config=llm_config)

user_proxy.initiate_chat(manager, message="Build a FastAPI microservice for matrix multiplication.")

2. CrewAI​

CrewAI is a leading open-source framework designed for orchestrating autonomous AI agents and building complex, production-ready multi-agent systems. It combines the collaborative intelligence of Crews with the precise control of Flows.

  • Flows (The Backbone): Provide the structure, state management, and event-driven logic for AI applications.
  • Crews (The Intelligence): Collaborative teams of autonomous agents working together to solve delegated tasks.
  • Agents: Individual workers within a crew, equipped with tools, memory, knowledge, and structured outputs.
  • Tasks & Processes: Orchestrate how tasks are distributed among agents (e.g., sequential or hierarchical).
from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="AI Research Analyst",
    goal="Discover cutting edge papers on local quantization",
    backstory="You are a veteran AI researcher tracking hardware efficiency trends."
)

task1 = Task(
    description="Analyze recent GGUF 4-bit quantization benchmarks for mobile.",
    agent=researcher,
    expected_output="A bulleted summary of memory vs accuracy tradeoffs."
)

crew = Crew(
    agents=[researcher],
    tasks=[task1],
    process=Process.sequential
)

result = crew.kickoff()