AutoGen Framework
Master Microsoft AutoGen for building multi-agent conversational AI systems
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0 / 5 completedAdvanced AutoGen Features
Beyond basic agent conversations, AutoGen offers powerful features for code execution, human oversight, and performance optimization that enable production-ready multi-agent systems.
Interactive: Advanced Features Explorer
Code Execution
Agents can write and execute Python code in isolated environments with automatic result handling.
use_docker=True, work_dir="./workspace"🔧 More Advanced Capabilities
Nested Chat
Agents can spawn sub-conversations with other agents before responding. Useful for complex reasoning where agent needs internal discussion before external response.
register_nested_chats(trigger_condition, sub_agents)Sequential Chat
Chain multiple two-agent conversations where output flows through pipeline. Great for workflows like: research → analysis → writing → review.
initiate_chats([chat1_config, chat2_config, ...])Teaching Mode
Agents can learn from human feedback during conversations. Corrections and preferences are incorporated into agent behavior over time.
teachable_agent.learn_from_user_feedback()Multi-Modal Support
Process images, PDFs, and other media alongside text. Agents can analyze documents, extract information from images, and handle diverse data types.
message_content=["type": "image_url", "url": ...}]🎯 Production Considerations
Cost Control: Use caching aggressively (40-70% savings), set max_rounds to prevent runaway conversations, and use cheaper models for simple agents (GPT-3.5 for routing, GPT-4 for complex reasoning).
Error Handling: Implement robust try-catch around code execution, set appropriate timeouts, and have fallback strategies when agents fail to terminate properly.
Observability: Log all agent messages, track token usage per agent, monitor conversation lengths, and capture termination reasons for debugging and optimization.