Features
- Multi-agent conversations with customizable patterns
- Code execution sandbox for generated code
- Human-in-the-loop integration
- Support for multiple LLM providers
Pros
- Backed by Microsoft Research with strong foundations
- Flexible conversation patterns between agents
- Built-in safe code execution environment
Cons
- Python-only framework
- API has undergone significant changes
- Multi-agent debugging can be challenging
Overview
AutoGen is a multi-agent conversation framework developed by Microsoft Research. It enables building applications where multiple AI agents engage in conversations to solve tasks, with support for human participation in the conversation flow.
AutoGen’s core abstraction is the conversational agent. Agents can be configured with different LLM backends, system prompts, and capabilities (like code execution). They communicate through structured conversations, and you define the interaction patterns: who talks to whom, when humans should be consulted, and when the conversation should terminate.
A key feature is the built-in code execution sandbox. When an agent generates code, AutoGen can automatically execute it in a safe environment, capture the output, and feed it back into the conversation, enabling iterative code development and debugging.
When to Use
Choose AutoGen for research-oriented multi-agent applications where flexible conversation patterns and code execution are needed. It is well-suited for data analysis workflows, code generation pipelines, and exploratory AI research.
Getting Started
pip install autogen-agentchatfrom autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant", llm_config={"model": "gpt-4o"})
user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding"})
user_proxy.initiate_chat(assistant, message="Plot a chart of AAPL stock prices")