This gallery helps AutoGen developers move to the Microsoft Agent Framework (AF) with minimal guesswork. Each script pairs AutoGen code with its AF equivalent so you can compare primitives, tooling, and orchestration patterns side by side while you migrate production workloads.

• 01_basic_agent.py — Minimal AutoGen AssistantAgent and AF Agent comparison.
• 02_agent_with_tool.py — Function tool integration in both SDKs.
• 03_agent_thread_and_stream.py — Session management and streaming responses.
• 04_agent_as_tool.py — Using agents as tools (hierarchical agent pattern) and streaming with tools.

• 01_round_robin_group_chat.py — AutoGen RoundRobinGroupChat → AF GroupChatBuilder/SequentialBuilder.
• 02_selector_group_chat.py — AutoGen SelectorGroupChat → AF GroupChatBuilder.
• 03_swarm.py — AutoGen Swarm pattern → AF HandoffBuilder.
• 04_magentic_one.py — AutoGen MagenticOneGroupChat → AF MagenticBuilder.

Each script is fully async and the main() routine runs both implementations back to back so you can observe their outputs in a single execution.

• Python 3.10 or later.
• Access to the necessary model endpoints (Azure OpenAI, OpenAI, etc.).
• Installed SDKs: Install AutoGen and the Microsoft Agent Framework with:

  pip install "autogen-agentchat autogen-ext[openai] agent-framework"

• Service credentials exposed through environment variables (e.g., OPENAI_API_KEY).

From the repository root:

python samples/autogen-migration/single_agent/01_basic_agent.py

Every script accepts no CLI arguments and will first call the AutoGen implementation, followed by the AF version. Adjust the prompt or credentials inside the file as necessary before running.

Advanced comparisons are in autogen-migration/orchestrations (RoundRobin, Selector, Swarm, Magentic). You can run them directly:

python samples/autogen-migration/orchestrations/01_round_robin_group_chat.py
python samples/autogen-migration/orchestrations/04_magentic_one.py

• Default behavior differences: AutoGen's AssistantAgent is single-turn by default (max_tool_iterations=1), while AF's Agent is multi-turn and continues tool execution automatically.
• Thread management: AF agents are stateless by default. Use agent.create_session() and pass it to run() to maintain conversation state, similar to AutoGen's conversation context.
• Tools: AutoGen uses FunctionTool wrappers; AF uses @tool decorators with automatic schema inference.
• Orchestration patterns:
  • RoundRobinGroupChat → SequentialBuilder or WorkflowBuilder
  • SelectorGroupChat → GroupChatBuilder with LLM-based speaker selection
  • Swarm → HandoffBuilder for agent handoff coordination
  • MagenticOneGroupChat → MagenticBuilder for orchestrated multi-agent workflows