agent-framework/python/samples/getting_started/workflow

Workflow Getting Started Samples

Installation

To install the base agent_framework.workflow package, please run:

pip install agent-framework-workflow

You can install the workflow package with visualization dependency:

pip install agent-framework-workflow[viz]

To export visualization images you also need to install GraphViz.

Samples Overview

Foundational Concepts - Start Here

Begin with the foundational folder in order. These three samples introduce the core ideas of executors, edges, agents in workflows, and streaming.

Sample	File	Concepts
Executors and Edges	foundational/step1_executors_and_edges.py	Minimal workflow with basic executors and edges
Agents in a Workflow	foundational/step2_agents_in_a_workflow.py	Introduces AgentExecutor; calling agents inside a workflow
Streaming	foundational/step3_streaming.py	Extends workflows with event streaming

Once comfortable with these, explore the rest of the samples.

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Samples Overview (by directory)

agents

Sample	File	Concepts
Azure Chat Agents Streaming	agents/azure_chat_agents_streaming.py	Directly adds Azure agents as edges and handling streaming events
Custom Agent Executors	agents/custom_agent_executors.py	Create executors to handle agent run methods
Foundry Chat Agents Streaming	agents/foundry_chat_agents_streaming.py	Directly adds Foundry agents as edges and handling streaming events
Workflow as Agent	ai_agent/workflow_as_agent.py	Wrap a workflow so it can behave like an agent
Workflow as Agent + HITL	ai_agent/workflow_as_agent_human_in_the_loop.py	Extend workflow-as-agent with human-in-the-loop capability

checkpoint

Sample	File	Concepts
Checkpoint & Resume	checkpoint/checkpoint_with_resume.py	Create checkpoints, inspect them, and resume execution

conditional_edges

Sample	File	Concepts
Edge Condition	conditional_edges/edge_condition.py	Conditional routing based on agent classification
Switch-Case Edge Group	conditional_edges/switch_case_edge_group.py	Switch-case branching using classifier outputs
Multi-Selection Edge Group	conditional_edges/multi_selection_edge_group.py	Select one or many targets dynamically (subset fan-out)

fan_out_fan_in

Sample	File	Concepts
Concurrent (Fan-out/Fan-in)	fan_out_fan_in/fan_out_fan_in_edges.py	Dispatch to multiple executors and aggregate results
Map-Reduce with Visualization	fan_out_fan_in/map_reduce_and_visualization.py	Fan-out/fan-in pattern with GraphViz/diagram export

human_in_the_loop

Sample	File	Concepts
Human-In-The-Loop (Guessing Game)	human_in_the_loop/guessing_game_with_human_input.py	Interactive request/response prompts with a human

loop

Sample	File	Concepts
Simple Loop	loop/simple_loop.py	Feedback loop where an agent judges ABOVE/BELOW/MATCHED

orchestration

Sample	File	Concepts
Magentic Workflow (Multi-Agent)	orchestration/magentic.py	Orchestrate multiple agents with Magentic manager and streaming
Magentic + Human Plan Review	orchestration/magentic_human_plan_update.py	Human reviews/updates the plan before execution

sequential

Sample	File	Concepts
Sequential Executors	sequential/sequential_executors.py	Sequential workflow with explicit executor setup
Sequential (Streaming)	sequential/sequential_streaming.py	Stream events from a simple sequential run

shared_states

Sample	File	Concepts
Shared States	shared_states/shared_states_with_agents.py	Store in shared state once and later reuse across agents

sub_workflow

Sample	File	Concepts
Sub-Workflow (Basics)	sub_workflow/sub_workflow_basics.py	Wrap a workflow as an executor and orchestrate sub-workflows
Sub-Workflow: Request Interception	sub_workflow/sub_workflow_request_interception.py	Intercept/forward requests with decorators and request handling
Sub-Workflow: Parallel Requests	sub_workflow/sub_workflow_parallel_requests.py	Multi-type interception and external forwarding patterns

tracing

Sample	File	Concepts
Tracing (Basics)	tracing/tracing_basics.py	Use basic tracing for workflow telemetry

visualization

Sample	File	Concepts
Concurrent with Visualization	visualization/concurrent_with_visualization.py	Fan-out/fan-in workflow with diagram export

Notes

• Agent‑based samples use provider SDKs (Azure/OpenAI, etc.). Ensure credentials are configured, or adapt agents accordingly.

Environment Variables

• AzureChatClient: Set Azure OpenAI environment variables as documented here.
  These variables are required for samples that construct AzureChatClient

• OpenAI (used in orchestration samples):

  • OpenAIChatClient env vars
  • OpenAIResponsesClient env vars