Skip to content

agent-directed-workflows

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History

agent-directed-workflows

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
README.md
README.md
 
 
function_app.py
function_app.py
 
 
host.json
host.json
 
 
requirements.txt
requirements.txt
 
 
tools.py
tools.py
 
 

README.md

Agent-Directed Workflows — Durable Functions (Python)

This sample demonstrates how to build an agent-directed workflow (agent loop) using durable entities in Azure Durable Functions for Python backed by the Durable Task Scheduler.

It corresponds to the "Build your own using orchestrations and entities" approach described in the Durable Task for AI Agents comparison table.

Note: A Durable Task SDK version of this sample is also available. That version uses FastAPI with SSE streaming and gives you full control over the web host. This version uses the standard Azure Functions HTTP model (non-streaming responses).

What Is an Agent-Directed Workflow?

In a typical deterministic workflow, your code controls the execution path. In an agent-directed workflow, the LLM drives the control flow — it decides which tools to call, in what order, and when the task is complete. The execution path isn't known until runtime.

How This Sample Works

Each chat session is a durable entity that holds the full conversation history. When you send a message, the entity runs the agent loop (call LLM → execute tools → repeat) and streams response chunks to Redis pub/sub. The HTTP function subscribes to the Redis channel and collects the full response before returning it as JSON.

Project Structure

agent-directed-workflows/
├── function_app.py           # Entity, HTTP functions, and agent loop logic
├── tools.py                  # Tool definitions and execution logic
├── host.json                 # Functions host configuration (Durable Task Scheduler)
├── requirements.txt          # Python dependencies
└── README.md

Prerequisites

  1. Python 3.10+
  2. Azure Functions Core Tools v4+
  3. Docker (for the Durable Task Scheduler emulator and Redis)

Running the Sample

1. Start the Durable Task Scheduler emulator and Redis

# Durable Task Scheduler emulator
docker run --name dtsemulator -d -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest

# Redis
docker run --name redis -d -p 6379:6379 redis:latest

2. Install dependencies

pip install -r requirements.txt

3. Configure local settings

Create a local.settings.json file:

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
    "FUNCTIONS_WORKER_RUNTIME": "python",
    "DTS_CONNECTION_STRING": "Endpoint=http://localhost:8080;Authentication=None",
    "TASKHUB_NAME": "default",
    "REDIS_CONNECTION_STRING": "localhost:6379"
  }
}

4. (Optional) Configure Azure OpenAI

Add these to your local.settings.json Values to use a real LLM instead of the echo fallback:

{
  "AZURE_OPENAI_ENDPOINT": "https://<your-resource>.openai.azure.com",
  "AZURE_OPENAI_DEPLOYMENT": "<your-deployment-name>"
}

The sample uses DefaultAzureCredential — make sure you're signed in via az login.

5. Run the function app

func start

6. Chat with the agent

# Send a message (returns JSON with full response)
curl -X POST http://localhost:7071/api/chat/session1 \
  -H "Content-Type: application/json" \
  -d '{"message":"What is the weather in Seattle?"}'

# View conversation history
curl http://localhost:7071/api/chat/session1/history

# Reset the conversation
curl -X POST http://localhost:7071/api/chat/session1/reset

API Reference

Method Endpoint Description
POST /api/chat/{sessionId} Send a message and get the full response as JSON. Body: {"message": "..."}
GET /api/chat/{sessionId}/history Get the full conversation history for a session
POST /api/chat/{sessionId}/reset Clear a session's conversation history

Key Differences from the Durable Task SDK Version

Aspect Durable Task SDK Durable Functions
Web host FastAPI (full control) Azure Functions HTTP triggers
Streaming SSE via StreamingResponse Non-streaming (JSON response)
Entity registration worker.add_entity() @bp.entity_trigger decorator
Client DurableTaskSchedulerClient DurableOrchestrationClient
Configuration Environment variables host.json + local.settings.json

Learn More