Tao Chen
a971d24f1e
* WIP: with_output_from * Add with_output_from to other modules; next: workflow as agent * WIP: remove agent run events * orchestrations * WIP: update samples; next start at guessing_game_With_human_input.py * Update all samples * WIP: consolidate workflow as agent streaming vs non-streaming * Consolidate workflow as agent streaming vs non-streaming * Move request info event processing to a share method * Final pass on the samples * Fix mypy * Fix mypy * Comments --------- Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
AutoGen → Microsoft Agent Framework Migration Samples
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.
What's Included
Single-Agent Parity
- 01_basic_assistant_agent.py — Minimal AutoGen
AssistantAgentand AFChatAgentcomparison. - 02_assistant_agent_with_tool.py — Function tool integration in both SDKs.
- 03_assistant_agent_thread_and_stream.py — Thread management and streaming responses.
- 04_agent_as_tool.py — Using agents as tools (hierarchical agent pattern) and streaming with tools.
Multi-Agent Orchestration
- 01_round_robin_group_chat.py — AutoGen
RoundRobinGroupChat→ AFGroupChatBuilder/SequentialBuilder. - 02_selector_group_chat.py — AutoGen
SelectorGroupChat→ AFGroupChatBuilder. - 03_swarm.py — AutoGen Swarm pattern → AF
HandoffBuilder. - 04_magentic_one.py — AutoGen
MagenticOneGroupChat→ AFMagenticBuilder.
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.
Prerequisites
- 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).
Running Single-Agent Samples
From the repository root:
python samples/autogen-migration/single_agent/01_basic_assistant_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.
Running Orchestration Samples
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
Tips for Migration
- Default behavior differences: AutoGen's
AssistantAgentis single-turn by default (max_tool_iterations=1), while AF'sChatAgentis multi-turn and continues tool execution automatically. - Thread management: AF agents are stateless by default. Use
agent.get_new_thread()and pass it torun()/run_stream()to maintain conversation state, similar to AutoGen's conversation context. - Tools: AutoGen uses
FunctionToolwrappers; AF uses@tooldecorators with automatic schema inference. - Orchestration patterns:
RoundRobinGroupChat→SequentialBuilderorWorkflowBuilderSelectorGroupChat→GroupChatBuilderwith LLM-based speaker selectionSwarm→HandoffBuilderfor agent handoff coordinationMagenticOneGroupChat→MagenticBuilderfor orchestrated multi-agent workflows