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Python: (samples): adopt AzureOpenAIResponsesClient, reorganize orchestration examples, and fix workflow/orchestration bugs (#3873)

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* adopt AzureOpenAIResponsesClient, reorganize orchestration examples, and fix workflow/orchestration bugs

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Evan Mattson
2026-02-12 19:46:58 +09:00
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parent 8457533c69
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python/samples/getting_started/workflows/README.md
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@@ -38,14 +38,10 @@ Once comfortable with these, explore the rest of the samples below.
| Azure AI Agents (Streaming) | [agents/azure_ai_agents_streaming.py](./agents/azure_ai_agents_streaming.py) | Add Azure AI agents as edges and handle streaming events |
| Azure AI Agents (Shared Thread) | [agents/azure_ai_agents_with_shared_thread.py](./agents/azure_ai_agents_with_shared_thread.py) | Share a common message thread between multiple Azure AI agents in a workflow |
| Custom Agent Executors | [agents/custom_agent_executors.py](./agents/custom_agent_executors.py) | Create executors to handle agent run methods |
| Sequential Workflow as Agent | [agents/sequential_workflow_as_agent.py](./agents/sequential_workflow_as_agent.py) | Build a sequential workflow orchestrating agents, then expose it as a reusable agent |
| Concurrent Workflow as Agent | [agents/concurrent_workflow_as_agent.py](./agents/concurrent_workflow_as_agent.py) | Build a concurrent fan-out/fan-in workflow, then expose it as a reusable agent |
| Magentic Workflow as Agent | [agents/magentic_workflow_as_agent.py](./agents/magentic_workflow_as_agent.py) | Configure Magentic orchestration with callbacks, then expose the workflow as an agent |
| Workflow as Agent (Reflection Pattern) | [agents/workflow_as_agent_reflection_pattern.py](./agents/workflow_as_agent_reflection_pattern.py) | Wrap a workflow so it can behave like an agent (reflection pattern) |
| Workflow as Agent + HITL | [agents/workflow_as_agent_human_in_the_loop.py](./agents/workflow_as_agent_human_in_the_loop.py) | Extend workflow-as-agent with human-in-the-loop capability |
| Workflow as Agent with Thread | [agents/workflow_as_agent_with_thread.py](./agents/workflow_as_agent_with_thread.py) | Use AgentThread to maintain conversation history across workflow-as-agent invocations |
| Workflow as Agent kwargs | [agents/workflow_as_agent_kwargs.py](./agents/workflow_as_agent_kwargs.py) | Pass custom context (data, user tokens) via kwargs through workflow.as_agent() to @ai_function tools |
| Handoff Workflow as Agent | [agents/handoff_workflow_as_agent.py](./agents/handoff_workflow_as_agent.py) | Use a HandoffBuilder workflow as an agent with HITL via FunctionCallContent/FunctionResultContent |
### checkpoint
@@ -54,7 +50,7 @@ Once comfortable with these, explore the rest of the samples below.
| Checkpoint & Resume | [checkpoint/checkpoint_with_resume.py](./checkpoint/checkpoint_with_resume.py) | Create checkpoints, inspect them, and resume execution |
| Checkpoint & HITL Resume | [checkpoint/checkpoint_with_human_in_the_loop.py](./checkpoint/checkpoint_with_human_in_the_loop.py) | Combine checkpointing with human approvals and resume pending HITL requests |
| Checkpointed Sub-Workflow | [checkpoint/sub_workflow_checkpoint.py](./checkpoint/sub_workflow_checkpoint.py) | Save and resume a sub-workflow that pauses for human approval |
| Handoff + Tool Approval Resume | [checkpoint/handoff_with_tool_approval_checkpoint_resume.py](./checkpoint/handoff_with_tool_approval_checkpoint_resume.py) | Handoff workflow that captures tool-call approvals in checkpoints and resumes with human decisions |
| Handoff + Tool Approval Resume | Moved to orchestration samples | Handoff workflow that captures tool-call approvals in checkpoints and resumes with human decisions |
| Workflow as Agent Checkpoint | [checkpoint/workflow_as_agent_checkpoint.py](./checkpoint/workflow_as_agent_checkpoint.py) | Enable checkpointing when using workflow.as_agent() with checkpoint_storage parameter |
### composition
@@ -85,19 +81,13 @@ Once comfortable with these, explore the rest of the samples below.
| Human-In-The-Loop (Guessing Game) | [human-in-the-loop/guessing_game_with_human_input.py](./human-in-the-loop/guessing_game_with_human_input.py) | Interactive request/response prompts with a human via `ctx.request_info()` |
| Agents with Approval Requests in Workflows | [human-in-the-loop/agents_with_approval_requests.py](./human-in-the-loop/agents_with_approval_requests.py) | Agents that create approval requests during workflow execution and wait for human approval to proceed |
| Agents with Declaration-Only Tools | [human-in-the-loop/agents_with_declaration_only_tools.py](./human-in-the-loop/agents_with_declaration_only_tools.py) | Workflow pauses when agent calls a client-side tool (`func=None`), caller supplies the result |
| SequentialBuilder Request Info | [human-in-the-loop/sequential_request_info.py](./human-in-the-loop/sequential_request_info.py) | Request info for agent responses mid-workflow using `.with_request_info()` on SequentialBuilder |
| ConcurrentBuilder Request Info | [human-in-the-loop/concurrent_request_info.py](./human-in-the-loop/concurrent_request_info.py) | Review concurrent agent outputs before aggregation using `.with_request_info()` on ConcurrentBuilder |
| GroupChatBuilder Request Info | [human-in-the-loop/group_chat_request_info.py](./human-in-the-loop/group_chat_request_info.py) | Steer group discussions with periodic guidance using `.with_request_info()` on GroupChatBuilder |
Builder-oriented request-info samples are maintained in the orchestration sample set
(sequential, concurrent, and group-chat builder variants).
### tool-approval
Tool approval samples demonstrate using `@tool(approval_mode="always_require")` to gate sensitive tool executions with human approval. These work with the high-level builder APIs.
| Sample | File | Concepts |
| ------------------------------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| SequentialBuilder Tool Approval | [tool-approval/sequential_builder_tool_approval.py](./tool-approval/sequential_builder_tool_approval.py) | Sequential workflow with tool approval gates for sensitive operations |
| ConcurrentBuilder Tool Approval | [tool-approval/concurrent_builder_tool_approval.py](./tool-approval/concurrent_builder_tool_approval.py) | Concurrent workflow with tool approvals across parallel agents |
| GroupChatBuilder Tool Approval | [tool-approval/group_chat_builder_tool_approval.py](./tool-approval/group_chat_builder_tool_approval.py) | Group chat workflow with tool approval for multi-agent collaboration |
Builder-based tool approval samples are maintained in the orchestration sample set.
### observability
@@ -109,7 +99,8 @@ For additional observability samples in Agent Framework, see the [observability
### orchestration
Orchestration samples (Sequential, Concurrent, Handoff, GroupChat, Magentic) have moved to the dedicated [orchestrations samples directory](../orchestrations/README.md).
Orchestration-focused samples (Sequential, Concurrent, Handoff, GroupChat, Magentic), including builder-based
`workflow.as_agent(...)` variants, are documented in the [orchestrations](../orchestrations/README.md) directory.
### parallelism
@@ -169,9 +160,9 @@ Sequential orchestration uses a few small adapter nodes for plumbing:
### Environment Variables
- **AzureOpenAIChatClient**: Set Azure OpenAI environment variables as documented [here](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/chat_client/README.md#environment-variables).
These variables are required for samples that construct `AzureOpenAIChatClient`
Workflow samples that use `AzureOpenAIResponsesClient` expect:
- **OpenAI** (used in orchestration samples):
- [OpenAIChatClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_chat_client/README.md)
- [OpenAIResponsesClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_responses_client/README.md)
- `AZURE_AI_PROJECT_ENDPOINT` (Azure AI Foundry Agent Service (V2) project endpoint)
- `AZURE_AI_MODEL_DEPLOYMENT_NAME` (model deployment name)
These values are passed directly into the client constructor via `os.getenv()` in sample code.