agent-framework-azure-ai-py
0
总安装量
5
周安装量
#55973
全站排名
安装命令
npx skills add https://github.com/microsoft/skills --skill agent-framework-azure-ai-py
Agent 安装分布
opencode
4
gemini-cli
4
github-copilot
4
codex
4
claude-code
3
kimi-cli
3
Skill 文档
Agent Framework Azure Hosted Agents
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
Architecture
User Query â AzureAIAgentsProvider â Azure AI Agent Service (Persistent)
â
Agent.run() / Agent.run_stream()
â
Tools: Functions | Hosted (Code/Search/Web) | MCP
â
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
HostedMCPToolfor service-managed MCP,MCPStreamableHTTPToolfor client-managed
Reference Files
- references/tools.md: Detailed hosted tool patterns
- references/mcp.md: MCP integration (hosted + local)
- references/threads.md: Thread and conversation management
- references/advanced.md: OpenAPI, citations, structured outputs