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

| Tool | Class | Use Case |

|------|-------|----------|

| Code Interpreter | CodeInterpreterTool | Execute Python, generate files |

| File Search | FileSearchTool | RAG over uploaded documents |

| Bing Grounding | BingGroundingTool | Web search |

| Azure AI Search | AzureAISearchTool | Search your indexes |

| Function Calling | FunctionTool | Call your Python functions |

| OpenAPI | OpenApiTool | Call REST APIs |

| MCP | McpTool | Model Context Protocol servers |

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