name: azure-ai-agents-python
description: Build AI agents using the Azure AI Agents Python SDK (azure-ai-agents). Use when creating agents hosted on Azure AI Foundry with tools (File Search, Code Interpreter, Bing Grounding, Azure AI Search, Function Calling, OpenAPI, MCP), managing threads and messages, implementing streaming responses, or working with vector stores. This is the low-level SDK - for higher-level abstractions, use the agent-framework skill instead.

Azure AI Agents Python SDK

Build agents hosted on Azure AI Foundry using the azure-ai-agents SDK.

Installation

pip install azure-ai-agents azure-identity
# Or with azure-ai-projects for additional features
pip install azure-ai-projects azure-identity

Environment Variables

PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
MODEL_DEPLOYMENT_NAME="gpt-4o-mini"

Authentication

from azure.identity import DefaultAzureCredential
from azure.ai.agents import AgentsClient

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

Core Workflow

The basic agent lifecycle: create agent → create thread → create message → create run → get response

Minimal Example

import os
from azure.identity import DefaultAzureCredential
from azure.ai.agents import AgentsClient

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

# 1. Create agent
agent = client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful assistant.",
)

# 2. Create thread
thread = client.threads.create()

# 3. Add message
client.messages.create(
    thread_id=thread.id,
    role="user",
    content="Hello!",
)

# 4. Create and process run
run = client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)

# 5. Get response
if run.status == "completed":
    messages = client.messages.list(thread_id=thread.id)
    for msg in messages:
        if msg.role == "assistant":
            print(msg.content[0].text.value)

# Cleanup
client.delete_agent(agent.id)

Tools Overview

See references/tools.md for detailed patterns.

Adding Tools

from azure.ai.agents import CodeInterpreterTool, FileSearchTool

agent = client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="tool-agent",
    instructions="You can execute code and search files.",
    tools=[CodeInterpreterTool()],
    tool_resources={"code_interpreter": {"file_ids": [file.id]}},
)

Function Calling

from azure.ai.agents import FunctionTool, ToolSet

def get_weather(location: str) -> str:
    """Get weather for a location."""
    return f"Weather in {location}: 72F, sunny"

functions = FunctionTool(functions=[get_weather])
toolset = ToolSet()
toolset.add(functions)

agent = client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="function-agent",
    instructions="Help with weather queries.",
    toolset=toolset,
)

# Process run - toolset auto-executes functions
run = client.runs.create_and_process(
    thread_id=thread.id,
    agent_id=agent.id,
    toolset=toolset,  # Pass toolset for auto-execution
)

Streaming

from azure.ai.agents import AgentEventHandler

class MyHandler(AgentEventHandler):
    def on_message_delta(self, delta):
        if delta.text:
            print(delta.text.value, end="", flush=True)

    def on_error(self, data):
        print(f"Error: {data}")

with client.runs.stream(
    thread_id=thread.id,
    agent_id=agent.id,
    event_handler=MyHandler(),
) as stream:
    stream.until_done()

See references/streaming.md for advanced patterns.

File Operations

Upload File

file = client.files.upload_and_poll(
    file_path="data.csv",
    purpose="assistants",
)

Create Vector Store

vector_store = client.vector_stores.create_and_poll(
    file_ids=[file.id],
    name="my-store",
)

agent = client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    tools=[FileSearchTool()],
    tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
)

Async Client

from azure.ai.agents.aio import AgentsClient

async with AgentsClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
) as client:
    agent = await client.create_agent(...)
    # ... async operations

See references/async-patterns.md for async patterns.

Response Format

JSON Mode

agent = client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    response_format={"type": "json_object"},
)

JSON Schema

agent = client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "weather_response",
            "schema": {
                "type": "object",
                "properties": {
                    "temperature": {"type": "number"},
                    "conditions": {"type": "string"},
                },
                "required": ["temperature", "conditions"],
            },
        },
    },
)

Thread Management

Continue Conversation

# Save thread_id for later
thread_id = thread.id

# Resume later
client.messages.create(
    thread_id=thread_id,
    role="user",
    content="Follow-up question",
)
run = client.runs.create_and_process(thread_id=thread_id, agent_id=agent.id)

List Messages

messages = client.messages.list(thread_id=thread.id, order="asc")
for msg in messages:
    role = msg.role
    content = msg.content[0].text.value
    print(f"{role}: {content}")

Best Practices

1. Use context managers for async client
2. Clean up agents when done: client.delete_agent(agent.id)
3. Use create_and_process for simple cases, streaming for real-time UX
4. Pass toolset to run for automatic function execution
5. Poll operations use *_and_poll methods for long operations

Reference Files

• references/tools.md: All tool types with detailed examples
• references/streaming.md: Event handlers and streaming patterns
• references/async-patterns.md: Async client usage