agent-framework-azure-ai-py by sickn33/antigravity-awesome-skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill agent-framework-azure-ai-py使用 Microsoft Agent Framework Python SDK 在 Azure AI Foundry 上构建持久化智能体。
User Query → AzureAIAgentsProvider → Azure AI Agent Service (Persistent)
↓
Agent.run() / Agent.run_stream()
↓
Tools: Functions | Hosted (Code/Search/Web) | MCP
↓
AgentThread (conversation persistence)
# 完整框架(推荐)
pip install agent-framework --pre
# 或仅安装 Azure 特定包
pip install agent-framework-azure-ai --pre
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" # 用于网络搜索
from azure.identity.aio import AzureCliCredential, DefaultAzureCredential
# 开发环境
credential = AzureCliCredential()
# 生产环境
credential = DefaultAzureCredential()
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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())
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)
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)
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()
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}")
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}")
| 方法 | 描述 |
|---|---|
create_agent() | 在 Azure AI 服务上创建新智能体 |
get_agent(agent_id) | 通过 ID 检索现有智能体 |
as_agent(sdk_agent) | 包装 SDK Agent 对象(无需 HTTP 调用) |
| 工具 | 导入语句 | 用途 |
|---|---|---|
HostedCodeInterpreterTool | from agent_framework import HostedCodeInterpreterTool | 执行 Python 代码 |
HostedFileSearchTool | from agent_framework import HostedFileSearchTool | 搜索向量存储 |
HostedWebSearchTool | from agent_framework import HostedWebSearchTool | Bing 网络搜索 |
HostedMCPTool | from agent_framework import HostedMCPTool | 服务托管的 MCP |
MCPStreamableHTTPTool | from agent_framework import MCPStreamableHTTPTool | 客户端托管的 MCP |
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())
async with provider:tools= 参数(自动转换为 AIFunction)Annotated[type, Field(description=...)] 作为函数参数get_new_thread() 进行多轮对话HostedMCPTool,客户端托管的优先使用 MCPStreamableHTTPTool此技能适用于执行概述中描述的工作流或操作。
每周安装量
75
代码仓库
GitHub 星标数
27.4K
首次出现
2026年2月13日
安全审计
安装于
codex74
opencode74
github-copilot73
amp73
kimi-cli73
gemini-cli73
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
User Query → AzureAIAgentsProvider → Azure AI Agent Service (Persistent)
↓
Agent.run() / Agent.run_stream()
↓
Tools: Functions | Hosted (Code/Search/Web) | MCP
↓
AgentThread (conversation persistence)
# Full framework (recommended)
pip install agent-framework --pre
# Or Azure-specific package only
pip install agent-framework-azure-ai --pre
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
from azure.identity.aio import AzureCliCredential, DefaultAzureCredential
# Development
credential = AzureCliCredential()
# Production
credential = DefaultAzureCredential()
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())
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)
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)
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()
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}")
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}")
| 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) |
| 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 |
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())
async with provider:tools= parameter (auto-converted to AIFunction)Annotated[type, Field(description=...)] for function parametersget_new_thread() for multi-turn conversationsHostedMCPTool for service-managed MCP, MCPStreamableHTTPTool for client-managedThis skill is applicable to execute the workflow or actions described in the overview.
Weekly Installs
75
Repository
GitHub Stars
27.4K
First Seen
Feb 13, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
codex74
opencode74
github-copilot73
amp73
kimi-cli73
gemini-cli73
React 组合模式指南:Vercel 组件架构最佳实践,提升代码可维护性
120,000 周安装
HostedMCPTool | from agent_framework import HostedMCPTool | Service-managed MCP |
MCPStreamableHTTPTool | from agent_framework import MCPStreamableHTTPTool | Client-managed MCP |