Agent Framework Azure Hosted Agents#

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

Architecture#

text

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#

MethodDescriptioncreate_agent()Create new agent on Azure AI serviceget_agent(agent_id)Retrieve existing agent by IDas_agent(sdk_agent)Wrap SDK Agent object (no HTTP call)

Hosted Tools Quick Reference#

ToolImportPurposeHostedCodeInterpreterTool``from agent_framework import HostedCodeInterpreterToolExecute Python codeHostedFileSearchTool``from agent_framework import HostedFileSearchToolSearch vector storesHostedWebSearchTool``from agent_framework import HostedWebSearchToolBing web searchHostedMCPTool``from agent_framework import HostedMCPToolService-managed MCPMCPStreamableHTTPTool``from agent_framework import MCPStreamableHTTPToolClient-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