🤖 AutoGen Overview
AutoGen is a framework for building multi-agent conversational systems where agents can collaborate, execute code, and solve complex tasks through orchestrated interactions. It enables the development of sophisticated AI applications through agent teamwork.
Multi-Agent
Multiple specialized agents working together
Executable
Agents can write and execute code
Flexible
Various conversation patterns and modes
Key Features
Multi-Agent Orchestration
Coordinate multiple AI agents with different roles and capabilities
Code Execution
Agents can write, execute, and debug code automatically
Human-in-the-Loop
Flexible human intervention modes for oversight and control
Conversation Patterns
Support for various patterns: sequential, group chat, nested
Tool Integration
Agents can use external tools and APIs
State Management
Maintain conversation context and agent states
Implementation Patterns
Two-Agent Conversation
Basic conversation between assistant and user proxy
import autogen
# Configure LLM
config_list = [{
"model": "gpt-4",
"api_key": "your-api-key"
}]
# Create assistant agent
assistant = autogen.AssistantAgent(
name="assistant",
llm_config={
"config_list": config_list,
"temperature": 0.7,
}
)
# Create user proxy agent
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "coding",
"use_docker": False,
}
)
# Start conversation
user_proxy.initiate_chat(
assistant,
message="Write a Python function to calculate fibonacci numbers"
)
# The assistant will write code, user_proxy will execute it automaticallyAgent Types & Roles
AssistantAgent
LLM-powered agent that can generate responses and code
- • Configured with system message and LLM settings
- • Can be specialized for specific roles
- • Generates text and code based on prompts
UserProxyAgent
Executes code and represents human interaction
- • Can execute code in various environments
- • Configurable human input modes
- • Handles termination conditions
GroupChatManager
Orchestrates multi-agent group conversations
- • Manages speaker selection and turn-taking
- • Enforces conversation rules and limits
- • Coordinates message passing between agents
Best Practices
✅ Recommendations
- • Define clear roles and system messages for agents
- • Set appropriate termination conditions
- • Use Docker for safe code execution
- • Implement proper error handling
- • Monitor token usage and costs
- • Test agent interactions thoroughly
- • Version control agent configurations
⚠️ Considerations
- • Be cautious with automatic code execution
- • Set max_consecutive_auto_reply limits
- • Validate agent outputs before actions
- • Consider human oversight for critical tasks
- • Monitor for conversation loops
- • Manage API rate limits across agents
- • Plan for edge cases and failures