agent-framework/python/samples/getting_started/workflows/declarative

Declarative Workflows

Declarative workflows allow you to define multi-agent orchestration patterns in YAML, including:

• Variable manipulation and state management
• Control flow (loops, conditionals, branching)
• Agent invocations
• Human-in-the-loop patterns

See the main workflows README for the list of available samples.

Prerequisites

pip install agent-framework-declarative

Running Samples

Each sample directory contains:

• workflow.yaml - The declarative workflow definition
• main.py - Python code to load and execute the workflow
• README.md - Sample-specific documentation

To run a sample:

cd <sample_directory>
python main.py

Workflow Structure

A basic workflow YAML file looks like:

name: my-workflow
description: A simple workflow example

actions:
  - kind: SetValue
    path: turn.greeting
    value: Hello, World!
    
  - kind: SendActivity
    activity:
      text: =turn.greeting

Action Types

Variable Actions

• SetValue - Set a variable in state
• SetVariable - Set a variable (.NET style naming)
• AppendValue - Append to a list
• ResetVariable - Clear a variable

Control Flow

• If - Conditional branching
• Switch - Multi-way branching
• Foreach - Iterate over collections
• RepeatUntil - Loop until condition
• GotoAction - Jump to labeled action

Output

• SendActivity - Send text/attachments to user
• EmitEvent - Emit custom events

Agent Invocation

• InvokeAzureAgent - Call an Azure AI agent
• InvokePromptAgent - Call a local prompt agent

Human-in-Loop

• Question - Request user input
• WaitForInput - Pause for external input