Tutorial: Build an agentic web app in Azure App Service with LangGraph or Foundry Agent Service (Python)

This tutorial demonstrates how to add agentic capability to an existing data-driven FastAPI CRUD application. It does this using two different approaches: LangGraph and Foundry Agent Service.

If your web application already has useful features, like shopping, hotel booking, or data management, it's relatively straightforward to add agent functionality to your web application by wrapping those functionalities in a plugin (for LangGraph) or as an OpenAPI endpoint (for Foundry Agent Service). In this tutorial, you start with a simple to-do list app. By the end, you'll be able to create, update, and manage tasks with an agent in an App Service app.

LangGraph

Foundry Agent Service

Both LangGraph and Foundry Agent Service enable you to build agentic web applications with AI-driven capabilities. LangGraph is similar to Microsoft Agent Framework and is an SDK. The following table shows some of the considerations and trade-offs:

Consideration	LangGraph or Microsoft Agent Framework	Foundry Agent Service
Performance	Fast (runs locally)	Slower (managed, remote service)
Development	Full code, maximum control	Low code, rapid integration
Testing	Manual/unit tests in code	Built-in playground for quick testing
Scalability	App-managed	Azure-managed, autoscaled
Security guardrails	Custom implementation required	Built-in content safety and moderation
Identity	Custom implementation required	Built-in agent ID and authentication
Enterprise	Custom integration required	Built-in Microsoft 365/Teams deployment and Microsoft 365 integrated tool calls.

In this tutorial, you learn how to:

• Convert existing app functionality into a plugin for LangGraph.
• Add the plugin to a LangGraph agent and use it in a web app.
• Convert existing app functionality into an OpenAPI endpoint for Foundry Agent Service.
• Call a Foundry agent in a web app.
• Assign the required permissions for managed identity connectivity.

Prerequisites

• An Azure account with an active subscription - Create an account for free.
• GitHub account to use GitHub Codespaces - Learn more about GitHub Codespaces.

Open the sample with Codespaces

The easiest way to get started is by using GitHub Codespaces, which provides a complete development environment with all required tools preinstalled.

1. Navigate to the GitHub repository at https://github.com/Azure-Samples/app-service-agentic-langgraph-foundry-python.

2. Select the Code button, select the Codespaces tab, and select Create codespace on main.

3. Wait a few moments for your Codespace to initialize. When ready, you'll see a fully configured development environment in your browser.

4. Run the application locally:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   uvicorn src.app:app --host 0.0.0.0 --port 3000
   

5. When you see Your application running on port 3000 is available, select Open in Browser and add a few tasks.

   The agents aren't fully configured so they don't work yet. You'll configure them later.

Review the agent code

Both approaches use the same implementation pattern, where the agent is initialized on application start, and responds to user messages by POST requests.

LangGraph

The LangGraphTaskAgent is initialized in the constructor in src/agents/langgraph_task_agent.py. The initialization code does the following:

• Configures the AzureChatOpenAI client using environment variables.
• Creates the prebuilt ReAct agent with memory and a set of CRUD tools for task management (see LangGraph quickstart).

# Initialize Azure OpenAI client
credential = DefaultAzureCredential()
azure_ad_token_provider = get_bearer_token_provider(
    credential, "https://cognitiveservices.azure.com/.default"
)

self.llm = AzureChatOpenAI(
    azure_endpoint=endpoint,
    azure_deployment=deployment_name,
    azure_ad_token_provider=azure_ad_token_provider,
    api_version="2024-10-21"
)

# Define tools
tools = [
    self._create_task_tool(),
    self._get_tasks_tool(),
    self._get_task_tool(),
    self._update_task_tool(),
    self._delete_task_tool()
]

# Create the agent
self.agent = create_react_agent(self.llm, tools, checkpointer=self.memory)
print("LangGraph Task Agent initialized successfully")

When processing user messages, the agent is invoked using ainvoke() with the user's message and a thread ID for conversation continuity:

result = await self.agent.ainvoke(
    {"messages": [("user", message)]},
    config=config
)

Foundry Agent Service

The FoundryTaskAgent is initialized in the constructor of src/agents/foundry_task_agent.py. The initialization code does the following:

• Creates an AIProjectClient using Azure credentials.
• Gets an OpenAI client from the project client.
• Retrieves the agent from Foundry by name.
• Creates a new conversation for the session.

# Create the project client using Azure credentials
self.project_client = AIProjectClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential()
)

# Get the OpenAI client for conversation operations
self.openai_client = self.project_client.get_openai_client()

# Find the existing agent by name
self.agent = self.project_client.agents.get(agent_name)

if not self.agent:
    print(f"Agent with name '{agent_name}' not found in project.")
    return
            
# Create a conversation for this session
conversation = self.openai_client.conversations.create()
self.conversation_id = conversation.id
print("Foundry agent initialized successfully")

This initialization code doesn't define any functionality for the agent, because you would typically build the agent in the Foundry portal. As part of the example scenario, it also follows the OpenAPI pattern shown in Add an App Service app as a tool in Foundry Agent Service (Python), and makes its CRUD functionality available as an OpenAPI endpoint. This lets you add it to the agent later as a callable tool.

The OpenAPI code is defined in src/routes/api.py. For example, the "GET /tasks" route defines a custom operation_id parameter, as required by the OpenAPI spec tool in Microsoft Foundry, and description helps the agent determine how to call the API:

@router.get(
    "/tasks",
    response_model=List[TaskItem],
    operation_id="getAllTasks",
    description="Retrieve all tasks in the task list."
)
async def get_all_tasks():
    """Get all tasks"""
    try:
        tasks = await task_service.get_all_tasks()
        return tasks
    except Exception as e:
        print(f"Error getting tasks: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Failed to get tasks: {str(e)}")

When processing user messages, the agent is invoked by adding the user's message to the conversation and calling responses.create() with the agent reference:

# Add user message to the conversation
self.openai_client.conversations.items.create(
    conversation_id=self.conversation_id,
    items=[{"type": "message", "role": "user", "content": message}],
)

# Create response using the agent
response = self.openai_client.responses.create(
    conversation=self.conversation_id,
    extra_body={"agent": {"name": self.agent.name, "type": "agent_reference"}},
    input="",
)

Deploy the sample application

The sample repository contains an Azure Developer CLI (AZD) template, which creates an App Service app with managed identity and deploys your sample application.

1. In the terminal, log into Azure using Azure Developer CLI:

   azd auth login
   

   Follow the instructions to complete the authentication process.

2. Deploy the Azure App Service app with the AZD template:

   azd up
   

3. When prompted, give the following answers:

   Question	Answer
   Enter a new environment name:	Type a unique name.
   Select an Azure Subscription to use:	Select the subscription.
   Pick a resource group to use:	Select Create a new resource group.
   Select a location to create the resource group in:	Select Sweden Central.
   Enter a name for the new resource group:	Type Enter.

4. In the AZD output, find the URL of your app and navigate to it in the browser. The URL looks like this in the AZD output:

    Deploying services (azd deploy)
   
      (✓) Done: Deploying service web
      - Endpoint: <URL>
    

5. Open the autogenerated OpenAPI schema at the https://....azurewebsites.net/openapi.json path. You need this schema later.

   You now have an App Service app with a system-assigned managed identity.

Create and configure the Microsoft Foundry resource

LangGraph

1. In the Foundry portal, make sure the top New Foundry radio button is set to active and create a project.

2. Deploy a model of your choice (see Microsoft Foundry Quickstart: Create resources).

3. From top of the model playground, copy the model name.

4. The easiest way to get the Azure OpenAI endpoint is still from the classic portal. Select the New Foundry radio button, then Azure OpenAI, and then copy the URL in Azure OpenAI endpoint for later.

   

Foundry Agent Service

1. In the Foundry portal, make sure the top New Foundry radio button is set to active and create a project.

2. In the home page, copy the Project endpoint for later.

3. Select Start building > Create agent and follow the prompt.

4. In the new agent's playground, create an OpenAPI tool to call your web app by selecting Tools > Add > Custom > OpenAPI tool > Create. In the Setup pane, add an action with the OpenAPI spec tool. Use the OpenAPI schema that you get from the deployed web app and anonymous authentication. For detailed steps, see How to use the OpenAPI spec tool.

   Your application code is already configured to include the server's url and operationId, which are needed by the agent. For more information, see Connect to OpenAPI Specification.

5. Be sure to select Save.

6. Select Try in playground and test your Foundry agent with prompts like "Show me all the tasks" and "Please add a task."

   If you get a valid response, the agent is making tool calls to the OpenAPI endpoint on your deployed web app.

Assign required permissions

LangGraph

1. From the top menu of the new Foundry portal, select Operate, then select Admin. In the row for your Foundry project, you should see two links. The one in the Name column is the Foundry project resource, and the one in the Parent resource column is the Foundry resource.

   

2. Select the Foundry resource in the Parent resource and then select Manage this resource in the Azure portal. From the Azure portal, you can assign role-based access for the resource to the deployed web app.

3. Add the following role for the App Service app's managed identity:

   Target resource	Required role	Needed for
   Foundry	Cognitive Services OpenAI User	The chat completion service in Microsoft Agent Framework.

   For instructions, see Assign Azure roles using the Azure portal.

Foundry Agent Service

1. From the top menu of the new Foundry portal, select Operate, then select Admin. In the row for your Foundry project, you should see two links. The one in the Name column is the Foundry project resource, and the one in the Parent resource column is the Foundry resource.

   

2. Select the Foundry project resource in the Name column and then select Manage this resource in the Azure portal. From the Azure portal, you can assign role-based access for the resource to the deployed web app.

3. Add the following role for the App Service app's managed identity:

   Target resource	Required role	Needed for
   Foundry Project	Azure AI User	Reading and calling the Foundry agent.

   For instructions, see Assign Azure roles using the Azure portal.

Configure connection variables in your sample application

1. Open .env. Using the values you copied earlier from the Foundry portal, configure the following variables:

   LangGraph

   Variable	Description
   AZURE_OPENAI_ENDPOINT	Azure OpenAI endpoint (copied from the classic Foundry portal).
   AZURE_OPENAI_DEPLOYMENT_NAME	Model name in the deployment (copied from the model playground in the new Foundry portal).

   Note

   To keep the tutorial simple, you'll use these variables in .env instead of overwriting them with app settings in App Service.

   Foundry Agent Service

   Variable	Description
   AZURE_AI_FOUNDRY_PROJECT_ENDPOINT	Microsoft Foundry project endpoint from the new Foundry portal.
   AZURE_AI_FOUNDRY_AGENT_NAME	Agent name (from the agent playground in the Foundry portal).

   Note

   To keep the tutorial simple, you'll use these variables in .env instead of overwriting them with app settings in App Service.

2. Sign in to Azure with the Azure CLI:

   az login
   

   This allows the Azure Identity client library in the sample code to receive an authentication token for the logged in user. Remember that you added the required role for this user earlier.

3. Run the application locally:

   npm run build
   npm start
   

4. When you see Your application running on port 3000 is available, select Open in Browser.

5. Select the LangGraph Agent link and the Foundry Agent link to try out the chat interface. If you get a response, your application is connecting successfully to the Microsoft Foundry resource.

6. Back in the GitHub codespace, deploy your app changes.

   azd up
   

7. Navigate to the deployed application again and test the chat agents.

LangGraph

Foundry Agent Service

Clean up resources

When you're done with the application, you can delete the App Service resources to avoid incurring further costs:

azd down --purge

Since the AZD template doesn't include the Microsoft Foundry resources, you need to delete them manually if you want.

More resources

• Integrate AI into your Azure App Service applications
• What is Foundry Agent Service?
• LangGraph - Quickstart
• Azure AI Projects client library for Python