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Agent Framework Azure Ai Py

该SDK是微软Agent Framework的Azure专属实现,可在Azure AI Foundry构建持久化智能体。核心功能包括支持自定义函数、托管工具(代码解释、网页/文件搜索)、MCP集成,维护对话线程实现多轮上下文,输出流式响应或结构化数据。典型用于构建AI助手、研究工具等,通过Python代码调用,需配置Azure环境变量与认证凭证,输出智能体的文本或结构化结果。

thegovind
thegovind
热度 97.05
最近更新 2026-05-11 15:51:18
分类:开发依赖:代码执行 / 第三方 API / MCP 服务应用场景:代码生成 / API 集成 / DevOps

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add agent-framework-azure-ai-py
Metadata
name
agent-framework-azure-ai-py
description
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
package
agent-framework-azure-ai

Agent Framework Azure Hosted Agents

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

Architecture

User Query → AzureAIAgentsProvider → Azure AI Agent Service (Persistent)
                    ↓
              Agent.run() / Agent.run_stream()
                    ↓
              Tools: Functions | Hosted (Code/Search/Web) | MCP
                    ↓
              AgentThread (conversation persistence)

Installation

# Full framework (recommended)
pip install agent-framework --pre

# Or Azure-specific package only
pip install agent-framework-azure-ai --pre

Environment Variables

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"  # For web search

Authentication

from azure.identity.aio import AzureCliCredential, DefaultAzureCredential

# Development
credential = AzureCliCredential()

# Production
credential = DefaultAzureCredential()

Core Workflow

Basic Agent

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())

Agent with Function Tools

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],  # Pass functions directly
        )
        
        result = await agent.run("What's the weather in Seattle?")
        print(result.text)

Agent with Hosted Tools

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)

Streaming Responses

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()

Conversation Threads

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],
        )
        
        # Create thread for conversation persistence
        thread = agent.get_new_thread()
        
        # First turn
        result1 = await agent.run("What's the weather in Seattle?", thread=thread)
        print(f"Agent: {result1.text}")
        
        # Second turn - context is maintained
        result2 = await agent.run("What about Portland?", thread=thread)
        print(f"Agent: {result2.text}")
        
        # Save thread ID for later resumption
        print(f"Conversation ID: {thread.conversation_id}")

Structured Outputs

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 Methods

Method Description
create_agent() Create new agent on Azure AI service
get_agent(agent_id) Retrieve existing agent by ID
as_agent(sdk_agent) Wrap SDK Agent object (no HTTP call)

Hosted Tools Quick Reference

Tool Import Purpose
HostedCodeInterpreterTool from agent_framework import HostedCodeInterpreterTool Execute Python code
HostedFileSearchTool from agent_framework import HostedFileSearchTool Search vector stores
HostedWebSearchTool from agent_framework import HostedWebSearchTool Bing web search
HostedMCPTool from agent_framework import HostedMCPTool Service-managed MCP
MCPStreamableHTTPTool from agent_framework import MCPStreamableHTTPTool Client-managed MCP

Complete Example

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()
        
        # Non-streaming
        result = await agent.run(
            "Search for Python best practices and summarize",
            thread=thread,
        )
        print(f"Response: {result.text}")
        
        # Streaming
        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()
        
        # Structured output
        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())

Conventions

  • Always use async context managers: async with provider:
  • Pass functions directly to tools= parameter (auto-converted to AIFunction)
  • Use Annotated[type, Field(description=...)] for function parameters
  • Use get_new_thread() for multi-turn conversations
  • Prefer HostedMCPTool for service-managed MCP, MCPStreamableHTTPTool for client-managed

Reference Files