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Python: Improve the workflow getting started samples (#570)
* Wip: samples * wip - samples * Updates to workflow getting started samples * Checkpointing enhancements * Cleanup * PR feedback * Updates * Sample updates * Updates * Revamp samples, improve doc strings and code comments * Cleanup unused comment * Formatting cleanup * wip * Further work on samples. Allow agent to be specified as edge. * Cleanup * Typing cleanup * Sample updates --------- Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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## Installation
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To install the base `agent_framework.workflow` package, please run:
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```bash
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pip install agent-framework-workflow
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```
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You can install the workflow package with visualization dependency:
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```bash
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@@ -9,3 +15,104 @@ pip install agent-framework-workflow[viz]
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```
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To export visualization images you also need to [install GraphViz](https://graphviz.org/download/).
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## Samples Overview
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## Foundational Concepts - Start Here
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Begin with the `foundational` folder in order. These three samples introduce the core ideas of executors, edges, agents in workflows, and streaming.
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| Sample | File | Concepts |
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|--------|------|----------|
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| Executors and Edges | [foundational/step1_executors_and_edges.py](./foundational/step1_executors_and_edges.py) | Minimal workflow with basic executors and edges |
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| Agents in a Workflow | [foundational/step2_agents_in_a_workflow.py](./foundational/step2_agents_in_a_workflow.py) | Introduces `AgentExecutor`; calling agents inside a workflow |
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| Streaming | [foundational/step3_streaming.py](./foundational/step3_streaming.py) | Extends workflows with event streaming |
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Once comfortable with these, explore the rest of the samples.
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---
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## Samples Overview (by directory)
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### agents
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| Sample | File | Concepts |
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| Azure Chat Agents Streaming | [agents/azure_chat_agents_streaming.py](./agents/azure_chat_agents_streaming.py) | Directly adds Azure agents as edges and handling streaming events |
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| Custom Agent Executors | [agents/custom_agent_executors.py](./agents/custom_agent_executors.py) | Create executors to handle agent run methods |
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| Foundry Chat Agents Streaming | [agents/foundry_chat_agents_streaming.py](./agents/foundry_chat_agents_streaming.py) | Directly adds Foundry agents as edges and handling streaming events |
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| Workflow as Agent | [ai_agent/workflow_as_agent.py](./agents/workflow_as_agent.py) | Wrap a workflow so it can behave like an agent |
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| Workflow as Agent + HITL | [ai_agent/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 |
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### checkpoint
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| Sample | File | Concepts |
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| Checkpoint & Resume | [checkpoint/checkpoint_with_resume.py](./checkpoint/checkpoint_with_resume.py) | Create checkpoints, inspect them, and resume execution |
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### conditional_edges
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| Sample | File | Concepts |
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|---|---|---|
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| Edge Condition | [conditional_edges/edge_condition.py](./conditional_edges/edge_condition.py) | Conditional routing based on agent classification |
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| Switch-Case Edge Group | [conditional_edges/switch_case_edge_group.py](./conditional_edges/switch_case_edge_group.py) | Switch-case branching using classifier outputs |
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| Multi-Selection Edge Group | [conditional_edges/multi_selection_edge_group.py](./conditional_edges/multi_selection_edge_group.py) | Select one or many targets dynamically (subset fan-out) |
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### fan_out_fan_in
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| Sample | File | Concepts |
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| Concurrent (Fan-out/Fan-in) | [fan_out_fan_in/fan_out_fan_in_edges.py](./fan_out_fan_in/fan_out_fan_in_edges.py) | Dispatch to multiple executors and aggregate results |
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| Map-Reduce with Visualization | [fan_out_fan_in/map_reduce_and_visualization.py](./fan_out_fan_in/map_reduce_and_visualization.py) | Fan-out/fan-in pattern with GraphViz/diagram export |
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### human_in_the_loop
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| Sample | File | Concepts |
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| 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 |
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### loop
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| Sample | File | Concepts |
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| Simple Loop | [loop/simple_loop.py](./loop/simple_loop.py) | Feedback loop where an agent judges ABOVE/BELOW/MATCHED |
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### orchestration
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| Sample | File | Concepts |
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| Magentic Workflow (Multi-Agent) | [orchestration/magentic.py](./orchestration/magentic.py) | Orchestrate multiple agents with Magentic manager and streaming |
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| Magentic + Human Plan Review | [orchestration/magentic_human_plan_update.py](./orchestration/magentic_human_plan_update.py) | Human reviews/updates the plan before execution |
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### sequential
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| Sample | File | Concepts |
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| Sequential Executors | [sequential/sequential_executors.py](./sequential/sequential_executors.py) | Sequential workflow with explicit executor setup |
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| Sequential (Streaming) | [sequential/sequential_streaming.py](./sequential/sequential_streaming.py) | Stream events from a simple sequential run |
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### shared_states
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| Sample | File | Concepts |
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| Shared States | [shared_states/shared_states_with_agents.py](./shared_states/shared_states_with_agents.py) | Store in shared state once and later reuse across agents |
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### sub_workflow
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| Sample | File | Concepts |
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| Sub-Workflow (Basics) | [sub_workflow/sub_workflow_basics.py](./sub_workflow/sub_workflow.py) | Wrap a workflow as an executor and orchestrate sub-workflows |
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| Sub-Workflow: Request Interception | [sub_workflow/sub_workflow_request_interception.py](./sub_workflow/sub_workflow_request_interception.py) | Intercept/forward requests with decorators and request handling |
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| Sub-Workflow: Parallel Requests | [sub_workflow/sub_workflow_parallel_requests.py](./sub_workflow/sub_workflow_parallel_requests.py) | Multi-type interception and external forwarding patterns |
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### tracing
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| Sample | File | Concepts |
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| Tracing (Basics) | [tracing/tracing_basics.py](./tracing/tracing_basics.py) | Use basic tracing for workflow telemetry |
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### visualization
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| Sample | File | Concepts |
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| Concurrent with Visualization | [visualization/concurrent_with_visualization.py](./visualization/concurrent_with_visualization.py) | Fan-out/fan-in workflow with diagram export |
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Notes
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- Agent‑based samples use provider SDKs (Azure/OpenAI, etc.). Ensure credentials are configured, or adapt agents accordingly.
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### Environment Variables
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- **AzureChatClient**: 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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These variables are required for samples that construct `AzureChatClient`
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- **OpenAI** (used in 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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