chore: import upstream snapshot with attribution

2026-07-13 13:39:25 +08:00
commit db620d33df
5151 changed files with 925932 additions and 0 deletions
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+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.env
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+FOUNDRY_PROJECT_ENDPOINT="..."
+AZURE_AI_MODEL_DEPLOYMENT_NAME="..."
+ENABLE_SENSITIVE_DATA=true
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+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY . user_agent/
+WORKDIR /app/user_agent
+
+RUN if [ -f requirements.txt ]; then \
+        pip install -r requirements.txt; \
+    else \
+        echo "No requirements.txt found"; \
+    fi
+
+EXPOSE 8088
+
+CMD ["python", "main.py"]
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+# What this sample demonstrates
+
+An instrumented [Agent Framework](https://github.com/microsoft/agent-framework) agent hosted using the **Responses protocol**.
+
+## How It Works
+
+### Model Integration
+
+The agent uses `FoundryChatClient` from the Agent Framework to create a Responses client from the project endpoint and model deployment. The agent supports both streaming (SSE events) and non-streaming (JSON) response modes.
+
+See [main.py](main.py) for the full implementation.
+
+### Agent Hosting
+
+The agent is hosted using the [Agent Framework](https://github.com/microsoft/agent-framework) with the `ResponsesHostServer`, which provisions a REST API endpoint compatible with the OpenAI Responses protocol.
+
+### Instrumentation
+
+Agent Framework is [**natively instrumented**](https://learn.microsoft.com/en-us/agent-framework/agents/observability?pivots=programming-language-python) to capture diagnostics and telemetry for agent execution. Instrumentation is enabled by default. To also capture sensitive event payloads (prompts, tool arguments, etc.) set `ENABLE_SENSITIVE_DATA=true`. This sample demonstrates how to manage these settings via environment variables in `agent.manifest.yaml` and `agent.yaml`.
+
+Foundry Hosted Agent has built-in observability thus you don't need to set up exporters manually to capture telemetry from your code. The traces, metrics, and logs generated by the agent are automatically collected and made available through Foundry's observability stack via Azure Monitor/Application Insights. The `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable is injected when the agent is deployed to Foundry, however it is still required to be set in your environment if you want to run the agent host locally and have telemetry sent to Application Insights from your local environment.
+
+## Running the Agent Host
+
+Follow the instructions in the [Running the Agent Host Locally](../../README.md#running-the-agent-host-locally) section of the README in the parent directory to run the agent host.
+
+## Interacting with the agent
+
+> Because the observability exporters are managed by Foundry, this sample must be run using `azd ai agent run`. Run this sample using `python main.py` will not send telemetry to Application Insights.
+
+After the agent host is running locally, you can interact with the agent using the `azd ai agent invoke --local` command. For example:
+
+```bash
+azd ai agent invoke --local "What is the current weather?"
+```
+
+A couple of spans will be created for this request from Agent Framework's instrumentation, representing the generation of the response by the agent:
+
+- `invoke_agent`: This span represents the invocation of the agent itself, capturing the start and end of the agent's processing for this request.
+- `chat`: This span represents the call to the underlying model.
+- `execute_tool`: This span represents the execution of any tools invoked by the agent as part of generating the response.
+
+> For more information on the spans, refer to the [OpenTelemetry GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/)
+
+## Deploying the Agent to Foundry
+
+To host the agent on Foundry, follow the instructions in the [Deploying the Agent to Foundry](../../README.md#deploying-the-agent-to-foundry) section of the README in the parent directory.
+
+### Viewing Telemetry in Foundry
+
+Once the agent is deployed to Foundry, the telemetry generated by the agent (traces, metrics, and logs) will be automatically collected and sent to Azure Monitor/Application Insights. You can view this telemetry by navigating to the Application Insights resource associated with your Foundry project or directly from the Foundry UI.
+
+In the Foundry UI, next to the **Playground** tab is the **Traces** tab, where you can find the conversations and their corresponding trace IDs. Clicking on a trace ID will allow you to drill into the detailed trace information for that particular conversation.
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+name: agent-framework-agent-observability-responses
+description: >
+  A basic Agent Framework agent hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Responses Protocol
+    - Streaming
+template:
+  name: agent-framework-agent-observability-responses
+  kind: hosted
+  protocols:
+    - protocol: responses
+      version: 2.0.0
+  environment_variables:
+    - name: AZURE_AI_MODEL_DEPLOYMENT_NAME
+      value: "{{AZURE_AI_MODEL_DEPLOYMENT_NAME}}"
+    - name: ENABLE_SENSITIVE_DATA
+      value: true
+resources:
+  - kind: model
+    id: gpt-4.1-mini
+    name: AZURE_AI_MODEL_DEPLOYMENT_NAME
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+# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
+kind: hosted
+name: agent-framework-agent-observability-responses
+protocols:
+  - protocol: responses
+    version: 2.0.0
+resources:
+  cpu: "0.25"
+  memory: "0.5Gi"
+environment_variables:
+  - name: AZURE_AI_MODEL_DEPLOYMENT_NAME
+    value: ${AZURE_AI_MODEL_DEPLOYMENT_NAME}
+  - name: ENABLE_SENSITIVE_DATA
+    value: true
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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from random import randint
+from typing import Annotated
+
+from agent_framework import Agent, tool
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import ResponsesHostServer
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
+from pydantic import Field
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+@tool(approval_mode="never_require", description="Get the current location of the user.")
+def get_current_location() -> str:
+    """Get the current location of the agent."""
+    locations = ["New York", "London", "Paris", "Tokyo"]
+    return locations[randint(0, len(locations) - 1)]
+
+
+@tool(approval_mode="never_require")
+def get_weather(
+    location: Annotated[str, Field(description="The location to get the weather for.")],
+) -> str:
+    """Get the weather for a given location."""
+    conditions = ["sunny", "cloudy", "rainy", "stormy"]
+    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
+
+
+async def main():
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=DefaultAzureCredential(),
+    )
+
+    agent = Agent(
+        client=client,
+        instructions="You are a friendly assistant. Keep your answers brief.",
+        tools=[get_weather, get_current_location],
+        # History will be managed by the hosting infrastructure, thus there
+        # is no need to store history by the service. Learn more at:
+        # https://developers.openai.com/api/reference/resources/responses/methods/create
+        default_options={"store": False},
+    )
+
+    server = ResponsesHostServer(agent)
+    await server.run_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,2 @@
+agent-framework-foundry
+agent-framework-foundry-hosting>=1.0.0a260630