agent-framework-azure-ai-py — Python SDK для создания AI-агентов на Azure

--- name: agent-framework-azure-ai-py description: "Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK." risk: unknown source: community date_added: "2026-02-27" --- # 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 ```bash # Full framework (recommended) pip install agent-framework --pre # Or Azure-specific package only pip install agent-framework-azure-ai --pre ``` ## Environment Variables ```bash 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 ```python from azure.identity.aio import AzureCliCredential, DefaultAzureCredential # Development credential = AzureCliCredential() # Production credential = DefaultAzureCredential() ``` ## Core Workflow ### Basic Agent ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 - 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 ## When to Use This skill is applicable to execute the workflow or actions described in the overview.