Build Agents with smolagents on Azure AI

This example showcases how build agents with smolagents, leveraging Large Language Models (LLMs) from the Hugging Face Collection in Azure AI Foundry Hub deployed as an Azure ML Managed Online Endpoint, powered by Hugging Face’s Text Generation Inference (TGI).

TL;DR Smolagents is an open-source Python library designed to make it extremely easy to build and run agents using just a few lines of code. Text Generation Inference (TGI) is a solution developed by Hugging Face for deploying and serving LLMs and VLMs with high performance text generation. Azure AI Foundry provides a unified platform for enterprise AI operations, model builders, and application development. Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle.

This example will specifically deploy Qwen/Qwen2.5-Coder-32B-Instruct from the Hugging Face Hub (or see it on AzureML or on Azure AI Foundry) as an Azure ML Managed Online Endpoint on Azure AI Foundry Hub.

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen), bringing the following improvements upon CodeQwen1.5:

• Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
• A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
• Long-context Support up to 128K tokens.

For more information, make sure to check their model card on the Hugging Face Hub.

Pre-requisites

To run the following example, you will need to comply with the following pre-requisites, alternatively, you can also read more about those in the Azure Machine Learning Tutorial: Create resources you need to get started.

Azure Account

A Microsoft Azure account with an active subscription. If you don’t have a Microsoft Azure account, you can now create one for free, including 200 USD worth of credits to use within the next 30 days after the account creation.

Azure CLI

The Azure CLI (az) installed on the instance that you’re running this example on, see the installation steps, and follow the steps of the preferred method based on your instance. Then log in into your subscription as follows:

az login

More information at Sign in with Azure CLI - Login and Authentication.

Azure CLI extension for Azure ML

Besides the Azure CLI (az), you also need to install the Azure ML CLI extension (az ml) which will be used to create the Azure ML and Azure AI Foundry required resources.

First you will need to list the current extensions and remove any ml-related extension before installing the latest one i.e., v2.

az extension list
az extension remove --name azure-cli-ml
az extension remove --name ml

Then you can install the az ml v2 extension as follows:

az extension add --name ml

More information at Azure Machine Learning (ML) - Install and setup the CLI (v2).

Azure Resource Group

An Azure Resource Group under the one you will create the Azure AI Foundry Hub-based project (note it will create an Azure AI Foundry resource as an Azure L Workspace, but not the other way around, meaning that the Azure AI Foundry Hub will be listed as an Azure ML workspace, but leveraging the Azure AI Foundry capabilities for Gen AI), and the rest of the required resources. If you don’t have one, you can create it as follow:

az group create --name huggingface-azure-rg --location eastus

Then, you can ensure that the resource group was created successfully by e.g. listing all the available resource groups that you have access to on your subscription:

az group list --output table

More information at Manage Azure resource groups by using Azure CLI.

Azure AI Foundry Hub-based project

An Azure AI Foundry Hub under the subscription and resource group aforementioned. If you don’t have one, you can create it as follows:

az ml workspace create \
    --kind hub \
    --name huggingface-azure-hub \
    --resource-group huggingface-azure-rg \
    --location eastus

Then, you can ensure that the workspace was created successfully by e.g. listing all the available workspaces that you have access to on your subscription:

az ml workspace list --filtered-kinds hub --query "[].{Name:name, Kind:kind}" --resource-group huggingface-azure-rg --output table

Once the Azure AI Foundry Hub is created, you need to create an Azure AI Foundry Project linked to that Hub, to do so you first need to obtain the Azure AI Foundry Hub ID of the recently created Hub as follows (replace the resource names with yours):

az ml workspace show \
    --name huggingface-azure-hub \
    --resource-group huggingface-azure-rg \
    --query "id" \
    -o tsv

That command will provide the ID as follows /subscriptions/<SUBSCRIPTION_ID>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.MachineLearningServices/workspaces/huggingface-azure-hub, meaning that you can also format it manually yourself with the appropriate replacements. Then you need to run the following command to create the Azure AI Foundry Project for that Hub as:

az ml workspace create \
    --kind project \
    --hub-id $(az ml workspace show --name huggingface-azure-hub --resource-group huggingface-azure-rg --query "id" -o tsv) \
    --name huggingface-azure-project \
    --resource-group huggingface-azure-rg \
    --location eastus

Finally, you can verify that it was correctly created with the following command:

az ml workspace list --filtered-kinds project --query "[].{Name:name, Kind:kind}" --resource-group huggingface-azure-rg --output table

More information at How to create and manage an Azure AI Foundry Hub and at How to create a Hub using the Azure CLI.

Setup and installation

In this example, the Azure Machine Learning SDK for Python will be used to create the endpoint and the deployment, as well as to invoke the deployed API. Along with it, you will also need to install azure-identity to authenticate with your Azure credentials via Python.

%pip install azure-ai-ml azure-identity --upgrade --quiet

More information at Azure Machine Learning SDK for Python.

Then, for convenience setting the following environment variables is recommended as those will be used along the example for the Azure ML Client, so make sure to update and set those values accordingly as per your Microsoft Azure account and resources.

%env LOCATION eastus
%env SUBSCRIPTION_ID <YOUR_SUBSCRIPTION_ID>
%env RESOURCE_GROUP <YOUR_RESOURCE_GROUP>
%env AI_FOUNDRY_HUB_PROJECT <YOUR_AI_FOUNDRY_HUB_PROJECT>

Finally, you also need to define both the endpoint and deployment names, as those will be used throughout the example too:

import os
from uuid import uuid4

os.environ["ENDPOINT_NAME"] = f"qwen-coder-endpoint-{str(uuid4())[:8]}"
os.environ["DEPLOYMENT_NAME"] = f"qwen-coder-deployment-{str(uuid4())[:8]}"

!echo $ENDPOINT_NAME
!echo $DEPLOYMENT_NAME

Authenticate to Azure ML

Initially, you need to authenticate into the Azure AI Foundry Hub via Azure ML with the Azure ML Python SDK, which will be later used to deploy Qwen/Qwen2.5-Coder-32B-Instruct as an Azure ML Managed Online Endpoint in your Azure AI Foundry Hub.

import os
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

client = MLClient(
    credential=DefaultAzureCredential(),
    subscription_id=os.getenv("SUBSCRIPTION_ID"),
    resource_group_name=os.getenv("RESOURCE_GROUP"),
    workspace_name=os.getenv("AI_FOUNDRY_HUB_PROJECT"),
)

Create and Deploy Azure AI Endpoint

Before creating the Managed Online Endpoint, you need to build the model URI, which is formatted as it follows azureml://registries/<REGISTRY_NAME>/models/<MODEL_ID>/labels/latest (even if the URI contains azureml it’s the same as in Azure AI Foundry, since the model catalog is shared), that means that the REGISTRY_NAME should be set to “HuggingFace” as you intend to deploy a model from the Hugging Face Collection, and the MODEL_ID won’t be the Hugging Face Hub ID, but rather the ID with hyphen replacements for both backslash (/) and underscores (_) with hyphens (-), and then into lower case, as follows:

model_id = "Qwen/Qwen2.5-Coder-32B-Instruct"

model_uri = (
    f"azureml://registries/HuggingFace/models/{model_id.replace('/', '-').replace('_', '-').lower()}/labels/latest"
)
model_uri

Note that you will need to verify in advance that the URI is valid, and that the given Hugging Face Hub Model ID exists on Azure, since Hugging Face is publishing those models into their collection, meaning that some models may be available on the Hugging Face Hub but not yet on the Azure Model Catalog (you can request adding a model following the guide Request a model addition).

Alternatively, you can use the following snippet to verify if a model is available on the Azure Model Catalog programmatically:

import requests

response = requests.get(f"https://generate-azureml-urls.azurewebsites.net/api/generate?modelId={model_id}")
if response.status_code != 200:
    print(
        "[{response.status_code=}] {model_id=} not available on the Hugging Face Collection in Azure ML Model Catalog"
    )

Then you can create the Managed Online Endpoint specifying its name (note that the name must be unique per entire region, not only within a single subscription, resource group, workspace, etc., so it’s a nice practice to add some sort of unique name to it in case multi-region deployments are intended) via the ManagedOnlineEndpoint Python class.

Also note that by default the ManagedOnlineEndpoint will use the key authentication method, meaning that there will be a primary and secondary key that should be sent within the Authentication headers as a Bearer token; but also the aml_token authentication method can be used, read more about it at Authenticate clients for online endpoints.

The deployment, created via the ManagedOnlineDeployment Python class, will define the actual model deployment that will be exposed via the previously created endpoint. The ManagedOnlineDeployment will expect: the model i.e., the previously created URI azureml://registries/HuggingFace/models/qwen-qwen2.5-coder-32b-instruct/labels/latest, the endpoint_name, and the instance requirements being the instance_type and the instance_count.

Every model in the Hugging Face Collection is powered by an efficient inference backend, and each of those can run on a wide variety of instance types (as listed in Supported Hardware); in this case, a NVIDIA H100 GPU will be used i.e., Standard_NC40ads_H100_v5.

from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment

endpoint = ManagedOnlineEndpoint(name=os.getenv("ENDPOINT_NAME"))

deployment = ManagedOnlineDeployment(
    name=os.getenv("DEPLOYMENT_NAME"),
    endpoint_name=os.getenv("ENDPOINT_NAME"),
    model=model_uri,
    instance_type="Standard_NC40ads_H100_v5",
    instance_count=1,
)

client.begin_create_or_update(endpoint).wait()

client.online_deployments.begin_create_or_update(deployment).wait()

Once deployed, via either the Azure AI Foundry or the Azure ML Studio you’ll be able to inspect the endpoint details, the real-time logs, how to consume the endpoint, and even use the, still on preview, monitoring feature.

Find more information about it at Azure ML Managed Online Endpoints

Build agents with smolagents

Now that the Azure AI Endpoint is running, you can start sending requests to it. Since there are multiple approaches, but the following is just covering the OpenAI Python SDK approach, you should visit e.g. Deploy Large Language Models (LLMs) on Azure AI to see different alternatives.

So on, the steps to follow for building the agent are going to be:

1. Create the OpenAI client with smolagents, connected to the running Azure AI Endpoint via the smolagents.OpenAIServerModel (note that smolagents also exposes the smolagents.AzureOpenAIServerModel but that’s the client for using OpenAI via the Azure, not to connect to Azure AI).
2. Define the set of tools that the agent will have access to i.e., Python functions with the smolagents.tool decorator.
3. Create the smolagents.CodeAgent leveraging the code-LLM deployed on Azure AI, adding the set tools previously defined, so that the agent can use those when appropriate, using a local executor (not recommended if code to be executed is sensible or unidentified).

Create OpenAI Client

Since Text Generation Inference (TGI) also exposes OpenAI-compatible routes, you can also leverage the OpenAI Python SDK via smolagents to send requests to the deployed Azure ML Endpoint.

%pip install "smolagents[openai]" --upgrade --quiet

To use the OpenAI Python SDK with Azure ML Managed Online Endpoints, you need to first retrieve:

• api_url with the /v1 route (that contains the v1/chat/completions endpoint that the OpenAI Python SDK will send requests to)
• api_key which is the API Key in Azure AI or the primary key in Azure ML (unless a dedicated Azure ML Token is used instead)

api_key = client.online_endpoints.get_keys(os.getenv("ENDPOINT_NAME")).primary_key
api_url = client.online_endpoints.get(os.getenv("ENDPOINT_NAME")).scoring_uri.replace("/generate", "/v1")

api_url = f"https://{os.getenv('ENDPOINT_NAME')}.{os.getenv('LOCATION')}.inference.ml.azure.com/v1"

Then you can use the OpenAI Python SDK normally, making sure to include the extra header azureml-model-deployment header that contains the Azure AI / ML Deployment name.

The extra header will be provided via the default_headers argument of the OpenAI Python SDK when instantiating the client, to be provided in smolagents via the client_kwargs argument of smolagents.OpenAIServerModel, that will propagate those to the underlying OpenAI client.

from smolagents import OpenAIServerModel

model = OpenAIServerModel(
    model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
    api_base=api_url,
    api_key=api_key,
    client_kwargs={"default_headers": {"azureml-model-deployment": os.getenv("DEPLOYMENT_NAME")}},
)

Build Python Tools

smolagents will be used to build the tools that the agent will leverage, as well as to build the smolagents.CodeAgent itself. The following tools will be defined, using the smolagents.tool decorator, that will prepare the Python functions to be used as tools within the LLM Agent.

Note that the function signatures should come with proper typing so as to guide the LLM, as well as a clear function name and, most importantly, well-formatted docstrings indicating what the function does, what are the arguments, what it returns, and what errors can be raised; if applicable.

In this case, the tools that will be provided to the agent are the following:

• World Time API - get_time_in_timezone: fetches the current time on a given location using the World Time API.

• Wikipedia API - search_wikipedia: fetches a summary of a Wikipedia entry using the Wikipedia API.

from smolagents import tool

World Time API - get_time_in_timezone

@tool
def get_time_in_timezone(location: str) -> str:
    """
    Fetches the current time for a given location using the World Time API.
    Args:
        location: The location for which to fetch the current time, formatted as 'Region/City'.
    Returns:
        str: A string indicating the current time in the specified location, or an error message if the request fails.
    Raises:
        requests.exceptions.RequestException: If there is an issue with the HTTP request.
    """
    import requests

    url = f"http://worldtimeapi.org/api/timezone/{location}.json"

    try:
        response = requests.get(url)
        response.raise_for_status()

        data = response.json()
        current_time = data["datetime"]

        return f"The current time in {location} is {current_time}."

    except requests.exceptions.RequestException as e:
        return f"Error fetching time data: {str(e)}"

Wikipedia API - search_wikipedia

@tool
def search_wikipedia(query: str) -> str:
    """
    Fetches a summary of a Wikipedia page for a given query.
    Args:
        query: The search term to look up on Wikipedia.
    Returns:
        str: A summary of the Wikipedia page if successful, or an error message if the request fails.
    Raises:
        requests.exceptions.RequestException: If there is an issue with the HTTP request.
    """
    import requests

    url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{query}"

    try:
        response = requests.get(url)
        response.raise_for_status()

        data = response.json()
        title = data["title"]
        extract = data["extract"]

        return f"Summary for {title}: {extract}"

    except requests.exceptions.RequestException as e:
        return f"Error fetching Wikipedia data: {str(e)}"

Create Agent

Since in this case the deployed LLM on Azure AI is a coding-specific LLM, the agent will be created with smolagents.CodeAgent that adds the relevant prompt and parsing functionality, so as to interpret the LLM outputs as code. Alternatively, one could also use smolagents.ToolCallingAgent which is a tool calling agent, meaning that the given LLM should have tool calling capabilities.

Then, the smolagents.CodeAgent expects both the model and the set of tools that the model has access to, and then via the run method, you can leverage all the potential of the agent in an automatic way, without manual intervention; so that the agent will use the given tools if needed, to answer or comply with your initial request.

from smolagents import CodeAgent

agent = CodeAgent(
    tools=[
        get_time_in_timezone,
        search_wikipedia,
    ],
    model=model,
    stream_outputs=True,
)

agent.run(
    "Could you create a Python function that given the summary of 'What is a Lemur?'"
    " replaces all the occurrences of the letter E with the letter U (ignore the casing)"
)
# Summary for Lumur: Lumurs aru wut-nosud primatus of thu supurfamily Lumuroidua, dividud into 8 familius and consisting of 15 gunura and around 100 uxisting spucius. Thuy aru undumic to thu island of Madagascar. Most uxisting lumurs aru small, with a pointud snout, largu uyus, and a long tail. Thuy chiufly livu in truus and aru activu at night.

agent.run("What time is in Thailand right now? And what's the time difference with France?")
# The current time in Thailand is 5 hours ahead of the current time in France.

Release resources

Once you are done using the Azure AI Endpoint / Deployment, you can delete the resources as it follows, meaning that you will stop paying for the instance on which the model is running and all the attached costs will be stopped.

client.online_endpoints.begin_delete(name=os.getenv("ENDPOINT_NAME")).result()

Conclusion

Throughout this example you learnt how to deploy an Azure ML Managed Online Endpoint on an Azure AI Foundry Hub-based project running an open model from the Hugging Face Collection in the Azure AI Foundry Hub / Azure ML Model Catalog, leverage it to build agents with smolagents, and finally, how to stop and release the resources.

If you have any doubt, issue or question about this example, feel free to open an issue and we’ll do our best to help!