Build Conversational AI Agents with AutoGen

AutoGen is Microsoft's framework for building multi-agent AI systems where agents communicate through conversations. You define agents with specific roles and capabilities, then they chat to solve problems. One agent might propose solutions while another verifies—like pair programming with AI. The conversation-driven approach makes complex logic traceable and debuggable.

AutoGen emphasizes conversation and code execution—agents literally chat and run code. CrewAI focuses on task-based collaboration with role specialization. AutoGen excels at coding tasks and research; CrewAI suits content pipelines and business workflows. AutoGen has Microsoft backing; CrewAI has a simpler learning curve. Choose based on your primary use case.

AutoGen Studio is a visual interface for building AutoGen workflows without coding. Drag-and-drop agents, configure conversations, and test interactively. Great for prototyping and non-developers. Export workflows for production use. Available as a local web app via pip install autogenstudio.

AutoGen's code executor runs in configured environments: local (danger!), Docker (recommended), or Azure Container Instance. Always use Docker for production—it sandboxes execution, limits resources, and prevents malicious code from affecting your system. Configure timeout, memory limits, and allowed packages.

Yes, AutoGen has first-class Azure OpenAI support. Configure api_type='azure' with your endpoint and key. Supports all Azure OpenAI features including content filters and managed identity auth. Mix Azure and OpenAI models in the same workflow. Use Azure for enterprise compliance requirements.

AssistantAgent: LLM-powered agent that thinks and responds. UserProxyAgent: represents human (or automated) input, can execute code. GroupChatManager: coordinates multi-agent conversations. You can create custom agents by subclassing. Most workflows use assistant + user proxy pairs that iterate on tasks.

UserProxyAgent supports human_input_mode: 'ALWAYS' (require approval), 'TERMINATE' (only at end), or 'NEVER' (fully autonomous). Add termination conditions to stop when task completes. For safety-critical applications, require human approval before code execution or external actions.

Register functions with @agent.register_function decorator. Define the function signature, docstring (which the LLM reads), and implementation. Functions can call APIs, query databases, or any Python code. AutoGen uses the docstring to understand when and how to call the tool.

GroupChat enables multi-agent conversations beyond two agents. Define a list of agents and a manager agent coordinates who speaks when. Use for: diverse expertise (analyst + coder + reviewer), debate scenarios (multiple perspectives), or complex workflows requiring coordination. Speaker selection can be automatic, round-robin, or manual.

Enable caching to avoid repeat API calls. Use cheaper models (GPT-3.5) for simple tasks, expensive models (GPT-4) for reasoning. Set max_consecutive_auto_reply to limit loops. Summarize long conversations periodically. Use termination conditions to stop early when task completes. Monitor token usage per agent.

Yes, via retrieve augmented agents. AutoGen includes RetrieveUserProxyAgent that queries vector databases before responding. Integrate with Chroma, FAISS, or any embedding store. Alternatively, create custom functions that query Pinecone/Weaviate and register as tools for any agent.

Containerize with Docker (which also sandboxes code execution). Use FastAPI or Flask to expose endpoints. Implement proper async handling for concurrent workflows. Add Redis or database for conversation persistence. Monitor LLM costs and add rate limiting. Consider Azure Container Apps for managed deployment with Azure OpenAI integration.