* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@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 AFAgentcomparison. - 02_assistant_agent_with_tool.py — Function tool integration in both SDKs.
- 03_assistant_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.
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'sAgentis multi-turn and continues tool execution automatically. - Thread management: AF agents are stateless by default. Use
agent.create_session()and pass it torun()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