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Python: (samples): adopt AzureOpenAIResponsesClient, reorganize orchestration examples, and fix workflow/orchestration bugs (#3873)
* adopt AzureOpenAIResponsesClient, reorganize orchestration examples, and fix workflow/orchestration bugs * Updates * add comment
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@@ -26,24 +26,58 @@ from agent_framework.orchestrations import (
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)
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```
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## Samples Overview
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## Samples Overview (by directory)
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| Sample | File | Concepts |
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| ------------------------------------------------- | ------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------- |
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| Concurrent Orchestration (Default Aggregator) | [concurrent_agents.py](./concurrent_agents.py) | Fan-out to multiple agents; fan-in with default aggregator returning combined Messages |
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| Concurrent Orchestration (Custom Aggregator) | [concurrent_custom_aggregator.py](./concurrent_custom_aggregator.py) | Override aggregator via callback; summarize results with an LLM |
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| Concurrent Orchestration (Custom Agent Executors) | [concurrent_custom_agent_executors.py](./concurrent_custom_agent_executors.py) | Child executors own Agents; concurrent fan-out/fan-in via ConcurrentBuilder |
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| Group Chat with Agent Manager | [group_chat_agent_manager.py](./group_chat_agent_manager.py) | Agent-based manager using `with_orchestrator(agent=)` to select next speaker |
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| Group Chat Philosophical Debate | [group_chat_philosophical_debate.py](./group_chat_philosophical_debate.py) | Agent manager moderates long-form, multi-round debate across diverse participants |
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| Group Chat with Simple Function Selector | [group_chat_simple_selector.py](./group_chat_simple_selector.py) | Group chat with a simple function selector for next speaker |
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| Handoff (Simple) | [handoff_simple.py](./handoff_simple.py) | Single-tier routing: triage agent routes to specialists, control returns to user after each specialist response |
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| Handoff (Autonomous) | [handoff_autonomous.py](./handoff_autonomous.py) | Autonomous mode: specialists iterate independently until invoking a handoff tool using `.with_autonomous_mode()` |
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| Handoff with Code Interpreter | [handoff_with_code_interpreter_file.py](./handoff_with_code_interpreter_file.py) | Retrieve file IDs from code interpreter output in handoff workflow |
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| Magentic Workflow (Multi-Agent) | [magentic.py](./magentic.py) | Orchestrate multiple agents with Magentic manager and streaming |
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| Magentic + Human Plan Review | [magentic_human_plan_review.py](./magentic_human_plan_review.py) | Human reviews/updates the plan before execution |
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| Magentic + Checkpoint Resume | [magentic_checkpoint.py](./magentic_checkpoint.py) | Resume Magentic orchestration from saved checkpoints |
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| Sequential Orchestration (Agents) | [sequential_agents.py](./sequential_agents.py) | Chain agents sequentially with shared conversation context |
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| Sequential Orchestration (Custom Executor) | [sequential_custom_executors.py](./sequential_custom_executors.py) | Mix agents with a summarizer that appends a compact summary |
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### concurrent
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| Sample | File | Concepts |
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| ------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
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| Concurrent Orchestration (Default Aggregator) | [concurrent/concurrent_agents.py](./concurrent/concurrent_agents.py) | Fan-out to multiple agents; fan-in with default aggregator returning combined Messages |
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| Concurrent Orchestration (Custom Aggregator) | [concurrent/concurrent_custom_aggregator.py](./concurrent/concurrent_custom_aggregator.py) | Override aggregator via callback; summarize results with an LLM |
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| Concurrent Orchestration (Custom Agent Executors) | [concurrent/concurrent_custom_agent_executors.py](./concurrent/concurrent_custom_agent_executors.py) | Child executors own Agents; concurrent fan-out/fan-in via ConcurrentBuilder |
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| Concurrent Orchestration as Agent | [concurrent/concurrent_workflow_as_agent.py](./concurrent/concurrent_workflow_as_agent.py) | Build a ConcurrentBuilder workflow and expose it as an agent via `workflow.as_agent(...)` |
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| Tool Approval with ConcurrentBuilder | [concurrent/concurrent_builder_tool_approval.py](./concurrent/concurrent_builder_tool_approval.py) | Require human approval for sensitive tools across concurrent participants |
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| ConcurrentBuilder Request Info | [concurrent/concurrent_request_info.py](./concurrent/concurrent_request_info.py) | Review concurrent agent outputs before aggregation using `.with_request_info()` |
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### sequential
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| Sample | File | Concepts |
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| ------------------------------------------ | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
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| Sequential Orchestration (Agents) | [sequential/sequential_agents.py](./sequential/sequential_agents.py) | Chain agents sequentially with shared conversation context |
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| Sequential Orchestration (Custom Executor) | [sequential/sequential_custom_executors.py](./sequential/sequential_custom_executors.py) | Mix agents with a summarizer that appends a compact summary |
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| Sequential Orchestration as Agent | [sequential/sequential_workflow_as_agent.py](./sequential/sequential_workflow_as_agent.py) | Build a SequentialBuilder workflow and expose it as an agent via `workflow.as_agent(...)` |
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| Tool Approval with SequentialBuilder | [sequential/sequential_builder_tool_approval.py](./sequential/sequential_builder_tool_approval.py) | Require human approval for sensitive tools in SequentialBuilder workflows |
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| SequentialBuilder Request Info | [sequential/sequential_request_info.py](./sequential/sequential_request_info.py) | Request info for agent responses mid-orchestration using `.with_request_info()` |
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### group-chat
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| Sample | File | Concepts |
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| ------------------------------------ | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------- |
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| Group Chat with Agent Manager | [group-chat/group_chat_agent_manager.py](./group-chat/group_chat_agent_manager.py) | Agent-based manager using `with_orchestrator(agent=)` to select next speaker |
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| Group Chat Philosophical Debate | [group-chat/group_chat_philosophical_debate.py](./group-chat/group_chat_philosophical_debate.py) | Agent manager moderates long-form, multi-round debate across diverse participants |
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| Group Chat with Simple Selector | [group-chat/group_chat_simple_selector.py](./group-chat/group_chat_simple_selector.py) | Group chat with a simple function selector for next speaker |
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| Group Chat Orchestration as Agent | [group-chat/group_chat_workflow_as_agent.py](./group-chat/group_chat_workflow_as_agent.py) | Build a GroupChatBuilder workflow and wrap it as an agent for composition |
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| Tool Approval with GroupChatBuilder | [group-chat/group_chat_builder_tool_approval.py](./group-chat/group_chat_builder_tool_approval.py) | Require human approval for sensitive tools in group chat orchestration |
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| GroupChatBuilder Request Info | [group-chat/group_chat_request_info.py](./group-chat/group_chat_request_info.py) | Steer group discussions with periodic guidance using `.with_request_info()` |
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### handoff
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| Sample | File | Concepts |
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| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
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| Handoff (Simple) | [handoff/handoff_simple.py](./handoff/handoff_simple.py) | Single-tier routing: triage agent routes to specialists, control returns to user after each specialist response |
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| Handoff (Autonomous) | [handoff/handoff_autonomous.py](./handoff/handoff_autonomous.py) | Autonomous mode: specialists iterate independently until invoking a handoff tool using `.with_autonomous_mode()` |
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| Handoff with Code Interpreter | [handoff/handoff_with_code_interpreter_file.py](./handoff/handoff_with_code_interpreter_file.py) | Retrieve file IDs from code interpreter output in handoff workflow |
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| Handoff with Tool Approval + Checkpoint | [handoff/handoff_with_tool_approval_checkpoint_resume.py](./handoff/handoff_with_tool_approval_checkpoint_resume.py) | Capture tool-approval decisions in checkpoints and resume from persisted state |
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| Handoff Orchestration as Agent | [handoff/handoff_workflow_as_agent.py](./handoff/handoff_workflow_as_agent.py) | Build a HandoffBuilder workflow and expose it as an agent, including HITL request/response flow |
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### magentic
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| Sample | File | Concepts |
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| ---------------------------- | ------------------------------------------------------------------------------------------ | --------------------------------------------------------------------- |
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| Magentic Workflow | [magentic/magentic.py](./magentic/magentic.py) | Orchestrate multiple agents with a Magentic manager and streaming |
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| Magentic + Human Plan Review | [magentic/magentic_human_plan_review.py](./magentic/magentic_human_plan_review.py) | Human reviews or updates the plan before execution |
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| Magentic + Checkpoint Resume | [magentic/magentic_checkpoint.py](./magentic/magentic_checkpoint.py) | Resume Magentic orchestration from saved checkpoints |
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| Magentic Orchestration as Agent | [magentic/magentic_workflow_as_agent.py](./magentic/magentic_workflow_as_agent.py) | Build a MagenticBuilder workflow and reuse it as an agent |
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## Tips
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@@ -60,8 +94,9 @@ These may appear in event streams (executor_invoked/executor_completed). They're
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## Environment Variables
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- **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).
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Orchestration samples that use `AzureOpenAIResponsesClient` expect:
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- **OpenAI** (used in some orchestration samples):
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- [OpenAIChatClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_chat_client/README.md)
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- [OpenAIResponsesClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_responses_client/README.md)
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- `AZURE_AI_PROJECT_ENDPOINT` (Azure AI Foundry Agent Service (V2) project endpoint)
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- `AZURE_AI_MODEL_DEPLOYMENT_NAME` (model deployment name)
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These values are passed directly into the client constructor via `os.getenv()` in sample code.
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