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AI Agents & Agentic Workflows

AutoGen Guide 2026 — Microsoft's Most Advanced Multi-Agent AI Framework

AutoGen (by Microsoft Research) models agents as conversational participants. Agents talk to each other via a message-passing interface, making it natural to build systems where agents debate, critique, and collaborate through dialogue.

Install

Terminal window
pip install pyautogen

Two-Agent Conversation

import autogen


config_list = [{"model": "claude-opus-4-6", "api_key": "YOUR_KEY", "api_type": "anthropic"}]


# Assistant that can write and execute code
assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list},
)


# Human proxy that can execute code
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=10,
    code_execution_config={"work_dir": "coding"},
)


# Initiate a task
user_proxy.initiate_chat(
    assistant,
    message="Write a Python script that fetches the top 5 AI papers from arXiv today.",
)

GroupChat (Multiple Agents)

groupchat = autogen.GroupChat(
    agents=[user_proxy, researcher, coder, critic],
    messages=[],
    max_round=15,
)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config={"config_list": config_list})


user_proxy.initiate_chat(manager, message="Build and test a web scraper for HackerNews")

When to Use AutoGen

  • Tasks that benefit from agent debate and iteration (code review, problem solving)
  • When you want automatic code execution in the loop
  • Research workflows where the “conversation” format adds clarity