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 int...
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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 int...
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
# Full framework (recommended)
pip install agent-framework --pre
# Or Azure-specific package only
pip install agent-framework-azure-ai --pre
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
asyncdefmain():
asyncwith (
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
defget_weather(
location: Annotated[str, Field(description="City name to get weather for")],
) -> str:
"""Get the current weather for a location."""returnf"Weather in {location}: 72°F, sunny"defget_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")
asyncdefmain():
asyncwith (
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
asyncdefmain():
asyncwith (
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
asyncdefmain():
asyncwith (
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)
asyncfor 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
asyncdefmain():
asyncwith (
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 resumptionprint(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
classWeatherResponse(BaseModel):
model_config = ConfigDict(extra="forbid")
location: str
temperature: float
unit: str
conditions: strasyncdefmain():
asyncwith (
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
defget_weather(
location: Annotated[str, Field(description="City name")],
) -> str:
"""Get weather for a location."""returnf"Weather in {location}: 72°F, sunny"classAnalysisResult(BaseModel):
summary: str
key_findings: list[str]
confidence: floatasyncdefmain():
asyncwith (
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}")
# Streamingprint("\nStreaming: ", end="")
asyncfor 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
