Agent Framework Azure AI Py

Agent Framework Azure 托管智能体

使用 Microsoft Agent Framework Python SDK 在 Azure AI Foundry 上构建持久化智能体。

架构

用户查询 → AzureAIAgentsProvider → Azure AI Agent Service (持久化)
                    ↓
              Agent.run() / Agent.run_stream()
                    ↓
              工具: 函数 | 托管 (代码/搜索/网页) | MCP
                    ↓
              AgentThread (对话持久化)

安装

# 完整框架（推荐）
pip install agent-framework --pre

# 或仅安装 Azure 专用包
pip install agent-framework-azure-ai --pre

环境变量

export AZURE_AI_PROJECT_ENDPOINT="https://<project>.services.ai.azure.com/api/projects/<project-id>"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
export BING_CONNECTION_ID="your-bing-connection-id"  # 用于网页搜索

身份认证

from azure.identity.aio import AzureCliCredential, DefaultAzureCredential

# 开发环境
credential = AzureCliCredential()

# 生产环境
credential = DefaultAzureCredential()

核心工作流

基础智能体

import asyncio
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential

async def main():
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="MyAgent",
            instructions="You are a helpful assistant.",
        )
        
        result = await agent.run("Hello!")
        print(result.text)

asyncio.run(main())

带函数工具的智能体

from typing import Annotated
from pydantic import Field
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential

def get_weather(
    location: Annotated[str, Field(description="City name to get weather for")],
) -> str:
    """Get the current weather for a location."""
    return f"Weather in {location}: 72°F, sunny"

def get_current_time() -> str:
    """Get the current UTC time."""
    from datetime import datetime, timezone
    return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")

async def main():
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="WeatherAgent",
            instructions="You help with weather and time queries.",
            tools=[get_weather, get_current_time],  # 直接传递函数
        )
        
        result = await agent.run("What's the weather in Seattle?")
        print(result.text)

带托管工具的智能体

from agent_framework import (
    HostedCodeInterpreterTool,
    HostedFileSearchTool,
    HostedWebSearchTool,
)
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential

async def main():
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="MultiToolAgent",
            instructions="You can execute code, search files, and search the web.",
            tools=[
                HostedCodeInterpreterTool(),
                HostedWebSearchTool(name="Bing"),
            ],
        )
        
        result = await agent.run("Calculate the factorial of 20 in Python")
        print(result.text)

流式响应

async def main():
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="StreamingAgent",
            instructions="You are a helpful assistant.",
        )
        
        print("Agent: ", end="", flush=True)
        async for chunk in agent.run_stream("Tell me a short story"):
            if chunk.text:
                print(chunk.text, end="", flush=True)
        print()

对话线程

from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential

async def main():
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="ChatAgent",
            instructions="You are a helpful assistant.",
            tools=[get_weather],
        )
        
        # 创建线程用于对话持久化
        thread = agent.get_new_thread()
        
        # 第一轮
        result1 = await agent.run("What's the weather in Seattle?", thread=thread)
        print(f"Agent: {result1.text}")
        
        # 第二轮 - 上下文保持
        result2 = await agent.run("What about Portland?", thread=thread)
        print(f"Agent: {result2.text}")
        
        # 保存线程 ID 以便后续恢复
        print(f"Conversation ID: {thread.conversation_id}")

结构化输出

from pydantic import BaseModel, ConfigDict
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential

class WeatherResponse(BaseModel):
    model_config = ConfigDict(extra="forbid")
    
    location: str
    temperature: float
    unit: str
    conditions: str

async def main():
    async with (
        AzureCliCredential() as credential,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="StructuredAgent",
            instructions="Provide weather information in structured format.",
            response_format=WeatherResponse,
        )
        
        result = await agent.run("Weather in Seattle?")
        weather = WeatherResponse.model_validate_json(result.text)
        print(f"{weather.location}: {weather.temperature}°{weather.unit}")

Provider 方法

托管工具快速参考

完整示例

import asyncio
from typing import Annotated
from pydantic import BaseModel, Field
from agent_framework import (
    HostedCodeInterpreterTool,
    HostedWebSearchTool,
    MCPStreamableHTTPTool,
)
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential


def get_weather(
    location: Annotated[str, Field(description="City name")],
) -> str:
    """Get weather for a location."""
    return f"Weather in {location}: 72°F, sunny"


class AnalysisResult(BaseModel):
    summary: str
    key_findings: list[str]
    confidence: float


async def main():
    async with (
        AzureCliCredential() as credential,
        MCPStreamableHTTPTool(
            name="Docs MCP",
            url="https://learn.microsoft.com/api/mcp",
        ) as mcp_tool,
        AzureAIAgentsProvider(credential=credential) as provider,
    ):
        agent = await provider.create_agent(
            name="ResearchAssistant",
            instructions="You are a research assistant with multiple capabilities.",
            tools=[
                get_weather,
                HostedCodeInterpreterTool(),
                HostedWebSearchTool(name="Bing"),
                mcp_tool,
            ],
        )
        
        thread = agent.get_new_thread()
        
        # 非流式
        result = await agent.run(
            "Search for Python best practices and summarize",
            thread=thread,
        )
        print(f"Response: {result.text}")
        
        # 流式
        print("\nStreaming: ", end="")
        async for chunk in agent.run_stream("Continue with examples", thread=thread):
            if chunk.text:
                print(chunk.text, end="", flush=True)
        print()
        
        # 结构化输出
        result = await agent.run(
            "Analyze findings",
            thread=thread,
            response_format=AnalysisResult,
        )
        analysis = AnalysisResult.model_validate_json(result.text)
        print(f"\nConfidence: {analysis.confidence}")


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

约定

• 始终使用异步上下文管理器：async with provider:
• 将函数直接传递给 tools= 参数（自动转换为 AIFunction）
• 使用 Annotated[type, Field(description=...)] 定义函数参数
• 使用 get_new_thread() 进行多轮对话
• 服务管理的 MCP 优先使用 HostedMCPTool，客户端管理的使用 MCPStreamableHTTPTool

参考文件

• references/tools.md: 详细的托管工具模式
• references/mcp.md: MCP 集成（托管 + 本地）
• references/threads.md: 线程和对话管理
• references/advanced.md: OpenAPI、引用、结构化输出

适用场景

本技能适用于执行概述中描述的工作流或操作。

限制

• 仅当任务明确匹配上述范围时使用本技能。
• 输出内容不能替代特定环境的验证、测试或专家评审。
• 如果缺少必需的输入、权限、安全边界或成功标准，请停止并请求澄清。