autogen-setup

Solid

Microsoft AutoGen multi-agent configuration for conversational AI systems

AI & Automation 814 stars 53 forks Updated today MIT

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Skill Content

# AutoGen Setup Skill ## Capabilities - Configure AutoGen agents (AssistantAgent, UserProxyAgent) - Set up agent conversations and group chats - Implement code execution capabilities - Design human-in-the-loop patterns - Configure nested agent architectures - Implement custom reply functions ## Target Processes - multi-agent-system - autonomous-task-planning ## Implementation Details ### Agent Types 1. **AssistantAgent**: LLM-powered assistant 2. **UserProxyAgent**: Human proxy with code execution 3. **GroupChatManager**: Multi-agent orchestration 4. **ConversableAgent**: Base class for custom agents ### Configuration Options - LLM configuration (models, temperatures) - Code execution settings - Human input mode - Max consecutive auto-replies - Function calling configuration ### Patterns - Two-agent conversations - Group chats with selection - Nested conversations - Teachable agents ### Best Practices - Proper termination conditions - Safe code execution sandboxing - Clear agent system messages - Monitor conversation flow ### Dependencies - pyautogen

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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