AutoGen is Microsoft's framework for building multi-agent AI applications. Agents can converse, use tools, execute code, and collaborate — enabling complex workflows that single agents can't handle.
Why AutoGen Powers Complex AI Workflows
A software team needed AI to write code, test it, debug failures, and iterate — all automatically. AutoGen's multi-agent conversation handles this loop: a coder writes, an executor runs, a critic reviews, and they iterate until the code works.
Key Features:
- Multi-Agent Conversations — Agents talk to each other
- Code Execution — Agents can write and run code safely
- Tool Use — Integrate any Python function as a tool
- Human-in-the-Loop — Optional human approval at any step
- Customizable — Define agent behaviors and conversation flows
Quick Start
pip install autogen-agentchat
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant", llm_config={"model": "gpt-4"})
user_proxy = UserProxyAgent("user", human_input_mode="NEVER", code_execution_config={"work_dir": "coding"})
user_proxy.initiate_chat(assistant, message="Create a Python script that fetches Bitcoin price")
Group Chat
from autogen import GroupChat, GroupChatManager
coder = AssistantAgent("coder", system_message="You write Python code.")
reviewer = AssistantAgent("reviewer", system_message="You review code for bugs.")
tester = AssistantAgent("tester", system_message="You write tests.")
groupchat = GroupChat(agents=[coder, reviewer, tester], messages=[])
manager = GroupChatManager(groupchat=groupchat)
user_proxy.initiate_chat(manager, message="Build a URL shortener")
Why Choose AutoGen
- Code execution — agents that actually run code
- Multi-agent — complex workflows with agent collaboration
- Microsoft-backed — active development and support
Check out AutoGen docs to get started.
Building AI agents? Check out my Apify actors or email spinov001@gmail.com for data extraction.
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