القائمة
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
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npx skills add https://github.com/BEKO2210/Firstbrain --skill agent-framework-azure-ai-pyيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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عرض SKILL.md
| name | agent-framework-azure-ai-py |
| description | Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | ai-ml |
| category | llm-agents |
| risk | unknown |
| source | community |
| tags | ["skill","ai-ml","llm-agents","agent","framework","azure"] |
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 = agent.run()
(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 |
HostedMCPTool | from agent_framework import HostedMCPTool | Service-managed MCP |
MCPStreamableHTTPTool | from agent_framework import MCPStreamableHTTPTool | Client-managed 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= 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.