azure-ai-agents 1.2.0b6
Microsoft Corporation Azure AI Agents Client Library for Python
License: MIT License
Requires Python: >=3.9
Keywords: azure, azure sdk

<!-- PIPY LONG DESCRIPTION BEGIN -->
# Azure AI Agents client library for Python

Use the AI Agents client library to:

* **Develop Agents using the Azure AI Agents Service**, leveraging an extensive ecosystem of models, tools, and capabilities from OpenAI, Microsoft, and other LLM providers. The Azure AI Agents Service enables the building of Agents for a wide range of generative AI use cases.
* **Note:** While this package can be used independently, we recommend using the Azure AI Projects client library (azure-ai-projects) for an enhanced experience. 
The Projects library provides simplified access to advanced functionality, such as creating and managing agents, enumerating AI models, working with datasets and 
managing search indexes, evaluating generative AI performance, and enabling OpenTelemetry tracing.

[Product documentation](https://aka.ms/azsdk/azure-ai-agents/product-doc)
| [Samples][samples]
| [API reference documentation](https://aka.ms/azsdk/azure-ai-agents/python/reference)
| [Package (PyPI)](https://aka.ms/azsdk/azure-ai-agents/python/package)
| [SDK source code](https://aka.ms/azsdk/azure-ai-agents/python/code)
| [AI Starter Template](https://aka.ms/azsdk/azure-ai-agents/python/ai-starter-template)

## Reporting issues

To report an issue with the client library, or request additional features, please open a GitHub issue [here](https://github.com/Azure/azure-sdk-for-python/issues). Mention the package name "azure-ai-agents" in the title or content.

## Table of contents

- [Getting started](#getting-started)
  - [Prerequisite](#prerequisite)
  - [Install the package](#install-the-package)
- [Key concepts](#key-concepts)
  - [Create and authenticate the client](#create-and-authenticate-the-client)
- [Examples](#examples)
  - [Create an Agent](#create-agent) with:
    - [File Search](#create-agent-with-file-search)
    - [Enterprise File Search](#create-agent-with-enterprise-file-search)
    - [Code interpreter](#create-agent-with-code-interpreter)
    - [Bing grounding](#create-agent-with-bing-grounding)
    - [Azure AI Search](#create-agent-with-azure-ai-search)
    - [Function call](#create-agent-with-function-call)
    - [Azure Function Call](#create-agent-with-azure-function-call)
    - [OpenAPI](#create-agent-with-openapi)
    - [Browser Automation](#create-agent-with-browser-automation)
    - [Fabric data](#create-an-agent-with-fabric)
    - [Connected agents](#create-an-agent-using-another-agents)
    - [Deep Research](#create-agent-with-deep-research)
    - [MCP](#create-agent-with-mcp)
  - [Create thread](#create-thread) with
    - [Tool resource](#create-thread-with-tool-resource)
  - [Create message](#create-message) with:
    - [File search attachment](#create-message-with-file-search-attachment)
    - [Code interpreter attachment](#create-message-with-code-interpreter-attachment)
    - [Create Message with Image Inputs](#create-message-with-image-inputs)
  - [Execute Run, Run_and_Process, or Stream](#execute-run-run_and_process-or-stream)
  - [Retrieve message](#retrieve-message)
  - [Retrieve file](#retrieve-file)
  - [Tear down by deleting resource](#teardown)
  - [Tracing](#tracing)
    - [Installation](#installation)
    - [How to enable tracing](#how-to-enable-tracing)
    - [Enabling content recording](#enabling-content-recording)
    - [How to trace your own functions](#how-to-trace-your-own-functions)
    - [Adding custom attributes to spans](#adding-custom-attributes-to-spans)
- [Troubleshooting](#troubleshooting)
  - [Logging](#logging)
  - [Reporting issues](#reporting-issues)
- [Next steps](#next-steps)
- [Contributing](#contributing)
<!-- PIPY LONG DESCRIPTION END -->
## Getting started

### Prerequisite

- Python 3.9 or later.
- An [Azure subscription][azure_sub].
- A [project in Azure AI Foundry](https://learn.microsoft.com/azure/ai-studio/how-to/create-projects).
- The project endpoint string. It can be found in your Azure AI Foundry project overview page, under "Project details". Below we will assume the environment variable `PROJECT_ENDPOINT_STRING` was defined to hold this value.
- Entra ID is needed to authenticate the client. Your application needs an object that implements the [TokenCredential](https://learn.microsoft.com/python/api/azure-core/azure.core.credentials.tokencredential) interface. Code samples here use [DefaultAzureCredential](https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential). To get that working, you will need:
  * An appropriate role assignment. see [Role-based access control in Azure AI Foundry portal](https://learn.microsoft.com/azure/ai-foundry/concepts/rbac-ai-foundry). Role assigned can be done via the "Access Control (IAM)" tab of your Azure AI Project resource in the Azure portal.
  * [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed.
  * You are logged into your Azure account by running `az login`.
  * Note that if you have multiple Azure subscriptions, the subscription that contains your Azure AI Project resource must be your default subscription. Run `az account list --output table` to list all your subscription and see which one is the default. Run `az account set --subscription "Your Subscription ID or Name"` to change your default subscription.

### Install the package

```bash
pip install azure-ai-agents
```

## Key concepts

### Create and authenticate the client

To use this SDK, start by creating an `AIProjectClient`. For more information on `azure-ai-projects`, refer to its [README](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-projects/README.md).

Here is an example of creating a synchronous `AIProjectClient`:

```python
import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

project_client = AIProjectClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)
```

To construct an asynchronous client, install the `aiohttp` package:

```bash
pip install aiohttp
```

Then use the code below with `AIProjectClient` and `DefaultAzureCredential` in `aio` packages:

```python
import asyncio
import os
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import DefaultAzureCredential

async def main() -> None:
    project_client = AIProjectClient(
       endpoint=os.environ["PROJECT_ENDPOINT"],
       credential=DefaultAzureCredential(),
    )

if __name__ == "__main__":
    asyncio.run(main())
```

Once you have an `AIProjectClient`, you can obtain an `AgentsClient` like this:

**Synchronous Client:**
```python
with project_client:
    agents_client = project_client.agents
```

**Asynchronous Client:**
```python
async with project_client:
    agents_client = project_client.agents
```

Alternatively, you can instantiate an AgentsClient directly as a standalone approach without using `azure-ai-projects`. However, this is not recommended, as it has limitations and lacks the integrated capabilities provided by using an `AIProjectClient`.   Here is is the example:

**Synchronous Client:**
```python
import os
from azure.ai.agents import AgentsClient
from azure.identity import DefaultAzureCredential

agents_client = AgentsClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential()
)

with agents_client:
    # your code to consume the client
    pass

```

**Asynchronous Client:**
```python
import asyncio
import os
from azure.ai.agents.aio import AgentsClient
from azure.identity.aio import DefaultAzureCredential

async def main() -> None:
    agents_client = AgentsClient(
        endpoint=os.environ["PROJECT_ENDPOINT"],
        credential=DefaultAzureCredential()
    )
    async with agents_client:
        # your code to consume the client
        pass

if __name__ == "__main__":
    asyncio.run(main())
```

## Examples

### Create Agent

Before creating an Agent, you need to set up Azure resources to deploy your model. [Create a New Agent Quickstart](https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=programming-language-python-azure) details selecting and deploying your Agent Setup.

Here is an example of how to create an Agent:
<!-- SNIPPET:sample_agents_basics.create_agent -->

```python
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
)
```

<!-- END SNIPPET -->

To allow Agents to access your resources or custom functions, you need tools. You can pass tools to `create_agent` by either `toolset` or combination of `tools` and `tool_resources`.

Here is an example of `toolset`:
<!-- SNIPPET:sample_agents_run_with_toolset.create_agent_toolset -->

```python
functions = FunctionTool(user_functions)
code_interpreter = CodeInterpreterTool()

toolset = ToolSet()
toolset.add(functions)
toolset.add(code_interpreter)

# To enable tool calls executed automatically
agents_client.enable_auto_function_calls(toolset)

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    toolset=toolset,
)
```

<!-- END SNIPPET -->

Also notices that if you use asynchronous client, you use `AsyncToolSet` instead.  Additional information related to `AsyncFunctionTool` be discussed in the later sections.

Here is an example to use `tools` and `tool_resources`:
<!-- SNIPPET:sample_agents_vector_store_batch_file_search.create_agent_with_tools_and_tool_resources -->

```python
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])

# Notices that FileSearchTool as tool and tool_resources must be added or the agent unable to search the file
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=file_search_tool.definitions,
    tool_resources=file_search_tool.resources,
)
```

<!-- END SNIPPET -->

In the following sections, we show you sample code in either `toolset` or combination of `tools` and `tool_resources`.

### Create Agent with File Search

To perform file search by an Agent, we first need to upload a file, create a vector store, and associate the file to the vector store. Here is an example:

<!-- SNIPPET:sample_agents_file_search.upload_file_create_vector_store_and_agent_with_file_search_tool -->

```python
file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
print(f"Uploaded file, file ID: {file.id}")

vector_store = agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
print(f"Created vector store, vector store ID: {vector_store.id}")

# Create file search tool with resources followed by creating agent
file_search = FileSearchTool(vector_store_ids=[vector_store.id])

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="Hello, you are helpful agent and can search information from uploaded files",
    tools=file_search.definitions,
    tool_resources=file_search.resources,
)
```

<!-- END SNIPPET -->

### Create Agent with Enterprise File Search

We can upload file to Azure as it is shown in the example, or use the existing Azure blob storage. In the code below we demonstrate how this can be achieved. First we upload file to azure and create `VectorStoreDataSource`, which then is used to create vector store. This vector store is then given to the `FileSearchTool` constructor.

<!-- SNIPPET:sample_agents_enterprise_file_search.upload_file_and_create_agent_with_file_search -->

```python
# If provided, we will upload the local file to Azure and will use it for vector store creation.
# Otherwise, we'll use a previously created dataset reference
if "AZURE_BLOB_URI" in os.environ:
    asset_uri = os.environ["AZURE_BLOB_URI"]
else:
    dataset_name = os.environ["AZURE_DATASET_NAME"]
    dataset_version = os.environ["AZURE_DATASET_VERSION"]
    dataset = project_client.datasets.get(name=dataset_name, version=dataset_version)
    asset_uri = dataset.id

# Create a vector store and wait for it to be processed
ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
vector_store = agents_client.vector_stores.create_and_poll(data_sources=[ds], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")
vector_store_files = {}
for fle in agents_client.vector_store_files.list(vector_store.id):
    uploaded_file = agents_client.files.get(fle.id)
    vector_store_files[fle.id] = uploaded_file.filename

# Create a file search tool
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])

# Notices that FileSearchTool as tool and tool_resources must be added or the agent unable to search the file
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=file_search_tool.definitions,
    tool_resources=file_search_tool.resources,
)
```

<!-- END SNIPPET -->

We also can attach files to the existing vector store. In the code snippet below, we first create an empty vector store and add file to it.

<!-- SNIPPET:sample_agents_vector_store_batch_enterprise_file_search.attach_files_to_store -->

```python
# Create a vector store with no file and wait for it to be processed
vector_store = agents_client.vector_stores.create_and_poll(data_sources=[], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")

ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
# Add the file to the vector store or you can supply data sources in the vector store creation
vector_store_file_batch = agents_client.vector_store_file_batches.create_and_poll(
    vector_store_id=vector_store.id, data_sources=[ds]
)
print(f"Created vector store file batch, vector store file batch ID: {vector_store_file_batch.id}")

# Create a file search tool
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
```

<!-- END SNIPPET -->

### Create Agent with Code Interpreter

Here is an example to upload a file and use it for code interpreter by an Agent:

<!-- SNIPPET:sample_agents_code_interpreter.upload_file_and_create_agent_with_code_interpreter -->

```python
file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
print(f"Uploaded file, file ID: {file.id}")

code_interpreter = CodeInterpreterTool(file_ids=[file.id])

# Create agent with code interpreter tool and tools_resources
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=code_interpreter.definitions,
    tool_resources=code_interpreter.resources,
)
```

<!-- END SNIPPET -->

### Create Agent with Bing Grounding

To enable your Agent to perform search through Bing search API, you use `BingGroundingTool` along with a connection.

Here is an example:

<!-- SNIPPET:sample_agents_bing_grounding.create_agent_with_bing_grounding_tool -->

```python
conn_id = project_client.connections.get(os.environ["BING_CONNECTION_NAME"]).id

# Initialize agent bing tool and add the connection id
bing = BingGroundingTool(connection_id=conn_id)

# Create agent with the bing tool and process agent run
with project_client:
    agents_client = project_client.agents
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=bing.definitions,
    )
```

<!-- END SNIPPET -->

### Create Agent with Deep Research

To enable your Agent to do detailed research of a topic, use the `DeepResearchTool` along with a connection to a Bing Grounding resource.
This scenarios requires you to specify two model deployments. One is the generic chat model that does arbitration, and is
specified as usual when you call the `create_agent` method. The other is the Deep Research model, which is specified
when you define the `DeepResearchTool`.

Here is an example:

<!-- SNIPPET:sample_agents_deep_research.create_agent_with_deep_research_tool -->

```python
bing_connection = project_client.connections.get(name=os.environ["BING_RESOURCE_NAME"])

# Initialize a Deep Research tool with Bing Connection ID and Deep Research model deployment name
deep_research_tool = DeepResearchTool(
    bing_grounding_connection_id=bing_connection.id,
    deep_research_model=os.environ["DEEP_RESEARCH_MODEL_DEPLOYMENT_NAME"],
)

# Create Agent with the Deep Research tool and process Agent run
with project_client:

    with project_client.agents as agents_client:

        # Create a new agent that has the Deep Research tool attached.
        # NOTE: To add Deep Research to an existing agent, fetch it with `get_agent(agent_id)` and then,
        # update the agent with the Deep Research tool.
        agent = agents_client.create_agent(
            model=os.environ["MODEL_DEPLOYMENT_NAME"],
            name="my-agent",
            instructions="You are a helpful Agent that assists in researching scientific topics.",
            tools=deep_research_tool.definitions,
        )
```

<!-- END SNIPPET -->

> **Limitation**: The Deep Research tool is currently recommended **only** in non-streaming scenarios.
> Using it with streaming can work, but it may occasionally time-out and is therefore not yet recommended.

### Create Agent with MCP

To enable your Agent to connect to a MCP server, use the `McpTool` along with a server URI to a MCP server and a label for that server.
Note that approval to send data to that server is required by default (but can be set to not required for each run).

Here is an example:

<!-- SNIPPET:sample_agents_mcp.create_agent_with_mcp_tool -->

```python
# Initialize agent MCP tool
mcp_tool = McpTool(
    server_label=mcp_server_label,
    server_url=mcp_server_url,
    allowed_tools=[],  # Optional: specify allowed tools
)

# You can also add or remove allowed tools dynamically
search_api_code = "search_azure_rest_api_code"
mcp_tool.allow_tool(search_api_code)
print(f"Allowed tools: {mcp_tool.allowed_tools}")

# Create agent with MCP tool and process agent run
with project_client:
    agents_client = project_client.agents

    # Create a new agent.
    # NOTE: To reuse existing agent, fetch it with get_agent(agent_id)
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-mcp-agent",
        instructions="You are a helpful agent that can use MCP tools to assist users. Use the available MCP tools to answer questions and perform tasks.",
        tools=mcp_tool.definitions,
    )
```

<!-- END SNIPPET -->

The tool approval flow looks like this:

<!-- SNIPPET:sample_agents_mcp.handle_tool_approvals -->

```python
# Create and process agent run in thread with MCP tools
mcp_tool.update_headers("SuperSecret", "123456")
# mcp_tool.set_approval_mode("never")  # Uncomment to disable approval requirement
run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources)
print(f"Created run, ID: {run.id}")

while run.status in ["queued", "in_progress", "requires_action"]:
    time.sleep(1)
    run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)

    if run.status == "requires_action" and isinstance(run.required_action, SubmitToolApprovalAction):
        tool_calls = run.required_action.submit_tool_approval.tool_calls
        if not tool_calls:
            print("No tool calls provided - cancelling run")
            agents_client.runs.cancel(thread_id=thread.id, run_id=run.id)
            break

        tool_approvals = []
        for tool_call in tool_calls:
            if isinstance(tool_call, RequiredMcpToolCall):
                try:
                    print(f"Approving tool call: {tool_call}")
                    tool_approvals.append(
                        ToolApproval(
                            tool_call_id=tool_call.id,
                            approve=True,
                            headers=mcp_tool.headers,
                        )
                    )
                except Exception as e:
                    print(f"Error approving tool_call {tool_call.id}: {e}")

        print(f"tool_approvals: {tool_approvals}")
        if tool_approvals:
            agents_client.runs.submit_tool_outputs(
                thread_id=thread.id, run_id=run.id, tool_approvals=tool_approvals
            )

    print(f"Current run status: {run.status}")
```

<!-- END SNIPPET -->

For scenarios requiring custom approval logic or additional control over MCP tool calls, you can use a `RunHandler` with the `create_and_process` method. This approach allows you to implement custom logic for approving or denying MCP tool calls.

Here's an example that demonstrates manual MCP tool call approval using a `RunHandler`:

<!-- SNIPPET:sample_agents_mcp_in_create_and_process.run_handler -->

```python
class MyRunHandler(RunHandler):
    def submit_mcp_tool_approval(
        self, *, run: ThreadRun, tool_call: RequiredMcpToolCall, **kwargs: Any
    ) -> ToolApproval:
        return ToolApproval(
            tool_call_id=tool_call.id,
            approve=True,
            headers=mcp_tool.headers,
        )
```

<!-- END SNIPPET -->

To use the RunHandler with `create_and_process` for MCP tools:

<!-- SNIPPET:sample_agents_mcp_in_create_and_process.create_and_process -->

```python
run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id, run_handler=MyRunHandler())
```

<!-- END SNIPPET -->

For a complete example, see [`sample_agents_mcp_in_create_and_process.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_mcp_in_create_and_process.py).

### Create Agent with Azure AI Search

Azure AI Search is an enterprise search system for high-performance applications. It integrates with Azure OpenAI Service and Azure Machine Learning, offering advanced search technologies like vector search and full-text search. Ideal for knowledge base insights, information discovery, and automation. Creating an Agent with Azure AI Search requires an existing Azure AI Search Index. For more information and setup guides, see [Azure AI Search Tool Guide](https://learn.microsoft.com/azure/ai-services/agents/how-to/tools/azure-ai-search?tabs=azurecli%2Cpython&pivots=overview-azure-ai-search).

Here is an example to integrate Azure AI Search:

<!-- SNIPPET:sample_agents_azure_ai_search.create_agent_with_azure_ai_search_tool -->

```python
conn_id = project_client.connections.get_default(ConnectionType.AZURE_AI_SEARCH).id
print(conn_id)

# Initialize agent AI search tool and add the search index connection id
ai_search = AzureAISearchTool(
    index_connection_id=conn_id,
    index_name="sample_index",
    query_type=AzureAISearchQueryType.SIMPLE,
    top_k=3,
    filter="",
)

agent = project_client.agents.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    tools=ai_search.definitions,
    tool_resources=ai_search.resources,
)
```

<!-- END SNIPPET -->

If the agent has found the relevant information in the index, the reference
and annotation will be provided in the message response. In the example above, we replace
the reference placeholder by the actual reference and url. Please note, that to
get sensible result, the index needs to have "embedding", "token", "category" and "title" fields.

<!-- SNIPPET:sample_agents_azure_ai_search.populate_references_agent_with_azure_ai_search_tool -->

```python
# Fetch and log all messages
messages = project_client.agents.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
for message in messages:
    if message.role == MessageRole.AGENT and message.url_citation_annotations:
        placeholder_annotations = {
            annotation.text: f" [see {annotation.url_citation.title}] ({annotation.url_citation.url})"
            for annotation in message.url_citation_annotations
        }
        for message_text in message.text_messages:
            message_str = message_text.text.value
            for k, v in placeholder_annotations.items():
                message_str = message_str.replace(k, v)
            print(f"{message.role}: {message_str}")
    else:
        for message_text in message.text_messages:
            print(f"{message.role}: {message_text.text.value}")
```

<!-- END SNIPPET -->

### Create Agent with Function Call

You can enhance your Agents by defining callback functions as function tools. These can be provided to `create_agent` via either the `toolset` parameter or the combination of `tools` and `tool_resources`. Here are the distinctions:

For more details about requirements and specification of functions, refer to [Function Tool Specifications](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/FunctionTool.md)

Here is an example to use [user functions](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/utils/user_functions.py) in `toolset`:
<!-- SNIPPET:sample_agents_stream_eventhandler_with_toolset.create_agent_with_function_tool -->

```python
functions = FunctionTool(user_functions)
toolset = ToolSet()
toolset.add(functions)
agents_client.enable_auto_function_calls(toolset)

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    toolset=toolset,
)
```

<!-- END SNIPPET -->

For asynchronous functions, you must import `AgentsClient` from `azure.ai.agents.aio` and use `AsyncFunctionTool`.   Here is an example using [asynchronous user functions](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_async/sample_agents_functions_async.py):

```python
from azure.ai.agents.aio import AgentsClient
```

<!-- SNIPPET:sample_agents_run_with_toolset_async.create_agent_with_async_function_tool -->

```python
functions = AsyncFunctionTool(user_async_functions)

toolset = AsyncToolSet()
toolset.add(functions)
agents_client.enable_auto_function_calls(toolset)

agent = await agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    toolset=toolset,
)
```

<!-- END SNIPPET -->

When `enable_auto_function_calls` is called, the SDK will automatically invoke functions during both `create_and_process` and streaming workflows. This simplifies agent logic by handling function execution internally.  Furthermore, although function tools and definitions are preserved in Agent service, their function implements are not.  Therefore, if your code queries earlier created agents through `update_agents` or `get_agents` function, you MUST also provide the function implementations through `enable_auto_function_calls` to complete auto function callings.

- For examples of automatic function calls in action, refer to [`sample_agents_auto_function_call.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_auto_function_call.py) or [`sample_agents_auto_function_call_async.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_async/sample_agents_auto_function_call_async.py).
- If you prefer to manage function execution manually, refer to [`sample_agents_stream_eventhandler_with_functions.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_streaming/sample_agents_stream_eventhandler_with_functions.py) or
[`sample_agents_functions.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_functions.py).

### Create Agent With Azure Function Call

The AI agent leverages Azure Functions triggered asynchronously via Azure Storage Queues. To enable the agent to perform Azure Function calls, you must set up the corresponding `AzureFunctionTool`, specifying input and output queues as well as parameter definitions.

Example Python snippet illustrating how you create an agent utilizing the Azure Function Tool:

```python
azure_function_tool = AzureFunctionTool(
    name="foo",
    description="Get answers from the foo bot.",
    parameters={
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "The question to ask."},
            "outputqueueuri": {"type": "string", "description": "The full output queue uri."},
        },
    },
    input_queue=AzureFunctionStorageQueue(
        queue_name="azure-function-foo-input",
        storage_service_endpoint=storage_service_endpoint,
    ),
    output_queue=AzureFunctionStorageQueue(
        queue_name="azure-function-tool-output",
        storage_service_endpoint=storage_service_endpoint,
    ),
)

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="azure-function-agent-foo",
    instructions=f"You are a helpful support agent. Use the provided function any time the prompt contains the string 'What would foo say?'. When you invoke the function, ALWAYS specify the output queue uri parameter as '{storage_service_endpoint}/azure-function-tool-output'. Always responds with \"Foo says\" and then the response from the tool.",
    tools=azure_function_tool.definitions,
)
print(f"Created agent, agent ID: {agent.id}")
```

---

**Limitations**

Currently, the Azure Function integration for the AI Agent has the following limitations:

- Supported trigger for Azure Function is currently limited to **Queue triggers** only.
  HTTP or other trigger types and streaming responses are not supported at this time.

---

**Create and Deploy Azure Function**

Before you can use the agent with AzureFunctionTool, you need to create and deploy Azure Function.

Below is an example Python Azure Function responding to queue-triggered messages and placing responses on the output queue:

```python
import azure.functions as func
import logging
import json

app = func.FunctionApp()


@app.function_name(name="Foo")
@app.queue_trigger(
    arg_name="arguments",
    queue_name="azure-function-foo-input",
    connection="AzureWebJobsStorage")
@app.queue_output(
    arg_name="outputQueue",
    queue_name="azure-function-tool-output",
    connection="AzureWebJobsStorage")
def foo(arguments: func.QueueMessage, outputQueue: func.Out[str]) -> None:
    """
    The function, answering question.

    :param arguments: The arguments, containing json serialized request.
    :param outputQueue: The output queue to write messages to.
    """
    
    parsed_args = json.loads(arguments.get_body().decode('utf-8'))
    try:
        response = {
            "Value": "Bar",
            "CorrelationId": parsed_args['CorrelationId']
        }
        outputQueue.set(json.dumps(response))
        logging.info(f'The function returns the following message: {json.dumps(response)}')
    except Exception as e:
        logging.error(f"Error processing message: {e}")
        raise
```

> **Important:** Both input and output payloads must contain the `CorrelationId`, which must match in request and response.

---

**Azure Function Project Creation and Deployment**

To deploy your function to Azure properly, follow Microsoft's official documentation step by step:

[Azure Functions Python Developer Guide](https://learn.microsoft.com/azure/azure-functions/create-first-function-cli-python?tabs=windows%2Cbash%2Cazure-cli%2Cbrowser)
**Note:** The Azure Function may be only used in standard agent setup. Please follow the [instruction](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/microsoft/infrastructure-setup/41-standard-agent-setup) to deploy an agent, capable of calling Azure Functions.

**Summary of required steps:**

- Use the Azure CLI or Azure Portal to create an Azure Function App.
- Create input and output queues in Azure Storage.
- Deploy your Function code.

---

**Verification and Testing Azure Function**

To ensure that your Azure Function deployment functions correctly:

1. Place the following style message manually into the input queue (`input`):

{
  "CorrelationId": "42"
}

Check the output queue (`output`) and validate the structured message response:

{
  "Value": "Bar",
  "CorrelationId": "42"
}

---

**Required Role Assignments (IAM Configuration)**

Ensure your Azure AI Project identity has the following storage account permissions:
- `Storage Account Contributor`
- `Storage Blob Data Contributor`
- `Storage File Data Privileged Contributor`
- `Storage Queue Data Contributor`
- `Storage Table Data Contributor`

---

**Additional Important Configuration Notes**

- The Azure Function configured above uses the `AzureWebJobsStorage` connection string for queue connectivity. You may alternatively use managed identity-based connections as described in the official Azure Functions Managed Identity documentation.
- Storage queues you specify (`input` & `output`) should already exist in the storage account before the Function deployment or invocation, created manually via Azure portal or CLI.
- When using Azure storage account connection strings, make sure the account has enabled storage account key access (`Storage Account > Settings > Configuration`).

---

With the above steps complete, your Azure Function integration with your AI Agent is ready for use.


### Create Agent With Logic Apps

Logic Apps allow HTTP requests to trigger actions. For more information, refer to the guide [Logic App Workflows for Function Calling](https://learn.microsoft.com/azure/ai-services/openai/how-to/assistants-logic-apps).

Your Logic App must be in the same resource group as your Azure AI Project, shown in the Azure Portal. Agents SDK accesses Logic Apps through Workflow URLs, which are fetched and called as requests in functions.

Below is an example of how to create an Azure Logic App utility tool and register a function with it.

<!-- SNIPPET:sample_agents_logic_apps.register_logic_app -->

```python
# Extract subscription and resource group from the project scope
subscription_id = os.environ["SUBSCRIPTION_ID"]
resource_group = os.environ["resource_group_name"]

# Logic App details
logic_app_name = "<LOGIC_APP_NAME>"
trigger_name = "<TRIGGER_NAME>"

# Create and initialize AzureLogicAppTool utility
logic_app_tool = AzureLogicAppTool(subscription_id, resource_group)
logic_app_tool.register_logic_app(logic_app_name, trigger_name)
print(f"Registered logic app '{logic_app_name}' with trigger '{trigger_name}'.")

# Create the specialized "send_email_via_logic_app" function for your agent tools
send_email_func = create_send_email_function(logic_app_tool, logic_app_name)

# Prepare the function tools for the agent
functions_to_use: Set = {
    fetch_current_datetime,
    send_email_func,  # This references the AzureLogicAppTool instance via closure
}
```

<!-- END SNIPPET -->

After this the functions can be incorporated normally into code using `FunctionTool`.


### Create Agent With OpenAPI

OpenAPI specifications describe REST operations against a specific endpoint. Agents SDK can read an OpenAPI spec, create a function from it, and call that function against the REST endpoint without additional client-side execution.

Here is an example creating an OpenAPI tool (using anonymous authentication):

<!-- SNIPPET:sample_agents_openapi.create_agent_with_openapi -->

```python

with open(weather_asset_file_path, "r") as f:
    openapi_weather = jsonref.loads(f.read())

with open(countries_asset_file_path, "r") as f:
    openapi_countries = jsonref.loads(f.read())

# Create Auth object for the OpenApiTool (note that connection or managed identity auth setup requires additional setup in Azure)
auth = OpenApiAnonymousAuthDetails()

# Initialize agent OpenApi tool using the read in OpenAPI spec
openapi_tool = OpenApiTool(
    name="get_weather", spec=openapi_weather, description="Retrieve weather information for a location", auth=auth
)
openapi_tool.add_definition(
    name="get_countries", spec=openapi_countries, description="Retrieve a list of countries", auth=auth
)

# Create agent with OpenApi tool and process agent run
with project_client:
    agents_client = project_client.agents

    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=openapi_tool.definitions,
    )
```

<!-- END SNIPPET -->

### Create Agent with Browser Automation

To enable your Agent to perform automated Browser navigation tasks, you will need the `BrowserAutomationTool`, along with a connection to
a [Microsoft Playwright Workspace](https://azure.microsoft.com/products/playwright-testing) resource.

Here is an example:

<!-- SNIPPET:sample_agents_browser_automation.create_agent_with_browser_automation -->

```python
connection_id = project_client.connections.get(os.environ["AZURE_PLAYWRIGHT_CONNECTION_NAME"]).id

# Initialize Browser Automation tool and add the connection id
browser_automation = BrowserAutomationTool(connection_id=connection_id)

with project_client:

    agents_client = project_client.agents

    # Create a new Agent that has the Browser Automation tool attached.
    # Note: To add Browser Automation tool to an existing Agent with an `agent_id`, do the following:
    # agent = agents_client.update_agent(agent_id, tools=browser_automation.definitions)
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="""
            You are an Agent helping with browser automation tasks. 
            You can answer questions, provide information, and assist with various tasks 
            related to web browsing using the Browser Automation tool available to you.
            """,
        tools=browser_automation.definitions,
    )
```

<!-- END SNIPPET -->

### Create an Agent with Fabric

To enable your Agent to answer queries using Fabric data, use `FabricTool` along with a connection to the Fabric resource.

Here is an example:

<!-- SNIPPET:sample_agents_fabric.create_agent_with_fabric_tool -->

```python
conn_id = project_client.connections.get(os.environ["FABRIC_CONNECTION_NAME"]).id

print(conn_id)

# Initialize an Agent Fabric tool and add the connection id
fabric = FabricTool(connection_id=conn_id)

# Create an Agent with the Fabric tool and process an Agent run
with project_client:
    agents_client = project_client.agents

    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=fabric.definitions,
    )
```

<!-- END SNIPPET -->

### Create an Agent using another agents

We can use `ConnectedAgentTool` to call specialized agents. In the example below we will create two agents, one is returning the Microsoft stock price and another returns weather. Note that the `ConnectedAgentTool` does not support local functions, we will use Azure function to return weather. The code of that function is given below; please see [Azure Function Call](#create-agent-with-azure-function-call) section for the instructions on how to deploy Azure Function.

```python
import azure.functions as func
import datetime
import json
import logging

app = func.FunctionApp()

@app.function_name(name="GetWeather")
@app.queue_trigger(
    arg_name="arguments",
    queue_name="weather-input",
    connection="AzureWebJobsStorage")
@app.queue_output(
    arg_name="outputQueue",
    queue_name="weather-output",
    connection="AzureWebJobsStorage")
def foo(arguments: func.Q