[Draft] keep alive test sample

hugging-face-tool-agent/Dockerfile

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+FROM python:3.11-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"]

hugging-face-tool-agent/agent.yaml

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+name: HuggingFace-Agent
+description: Hugging Face agent
+metadata:
+  example:
+    - role: user
+      content: |-
+        What are the trending models in the OpenLLM Leaderboard?
+  tags:
+    - Microsoft Agent Framework
+template:
+  name: HuggingFace-Agent
+  kind: hosted
+  environment_variables:
+    - name: AZURE_OPENAI_ENDPOINT
+      value: ${AZURE_OPENAI_ENDPOINT}
+    - name: OPENAI_API_VERSION
+      value: 2025-03-01-preview
+    - name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
+      value: "{{chat}}"
+    - name: AZURE_AI_PROJECT_TOOL_CONNECTION_ID
+      value: "HuggingFaceMCPServer"
+resources:
+  - kind: model
+    id: gpt-5
+    name: chat

hugging-face-tool-agent/main.py

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+# Copyright (c) Microsoft. All rights reserved.
+"""Example showing how to use an agent factory function with ToolClient.
+
+This sample demonstrates how to pass a factory function to from_agent_framework
+that receives a ToolClient and returns an AgentProtocol. This pattern allows
+the agent to be created dynamically with access to tools from Azure AI Tool
+Client at runtime.
+"""
+
+import asyncio
+import os
+from typing import List
+from dotenv import load_dotenv
+from agent_framework import AIFunction
+from agent_framework.azure import AzureOpenAIChatClient
+
+from azure.ai.agentserver.agentframework import from_agent_framework
+from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider
+
+load_dotenv()
+
+
+def create_agent_factory():
+    """Create a factory function that builds an agent with ToolClient.
+
+    This function returns a factory that takes a ToolClient and returns
+    an AgentProtocol. The agent is created at runtime for every request,
+    allowing it to access the latest tool configuration dynamically.
+    """
+
+    async def agent_factory(tools: List[AIFunction]) -> AzureOpenAIChatClient:
+        """Factory function that creates an agent using the provided tools.
+
+        :param tools: The list of AIFunction tools available to the agent.
+        :type tools: List[AIFunction]
+        :return: An Agent Framework ChatAgent instance.
+        :rtype: ChatAgent
+        """
+        # List all available tools from the ToolClient
+        print("Fetching tools from Azure AI Tool Client via factory...")
+        print(f"Found {len(tools)} tools:")
+        for tool in tools:
+            print(f"  - tool: {tool.name}, description: {tool.description}")
+
+        if not tools:
+            print("\nNo tools found!")
+            print("Make sure your Azure AI project has tools configured.")
+            raise ValueError("No tools available to create agent")
+
+        azure_credential = DefaultAzureCredential()
+        token_provider = get_bearer_token_provider(azure_credential, "https://cognitiveservices.azure.com/.default")
+        # Create the Agent Framework agent with the tools
+        print("\nCreating Agent Framework agent with tools from factory...")
+        agent = AzureOpenAIChatClient(ad_token_provider=token_provider).create_agent(
+            name="ToolClientAgent",
+            instructions="You are a helpful assistant with access to various tools.",
+            tools=tools,
+        )
+
+        print("Agent created successfully!")
+        return agent
+
+    return agent_factory
+
+
+async def quickstart():
+    """Build and return an AgentFrameworkCBAgent using an agent factory function."""
+
+    # Get configuration from environment
+    project_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT")
+
+    if not project_endpoint:
+        raise ValueError(
+            "AZURE_AI_PROJECT_ENDPOINT environment variable is required. "
+            "Set it to your Azure AI project endpoint, e.g., "
+            "https://<your-account>.services.ai.azure.com/api/projects/<your-project>"
+        )
+
+    # Create Azure credentials
+    credential = DefaultAzureCredential()
+
+    # Create a factory function that will build the agent at runtime
+    # The factory will receive a ToolClient when the agent first runs
+    agent_factory = create_agent_factory()
+
+    tool_connection_id = os.getenv("AZURE_AI_PROJECT_TOOL_CONNECTION_ID")
+    # Pass the factory function to from_agent_framework instead of a compiled agent
+    # The agent will be created on every agent run with access to ToolClient
+    print("Creating Agent Framework adapter with factory function...")
+    adapter = from_agent_framework(
+        agent_factory,
+        credentials=credential,
+        tools=[
+            {"type": "mcp", "project_connection_id": tool_connection_id}
+
+            ]
+    )
+
+    print("Adapter created! Agent will be built on every request.")
+    return adapter
+
+
+async def main():  # pragma: no cover - sample entrypoint
+    """Main function to run the agent."""
+    adapter = await quickstart()
+
+    if adapter:
+        print("\nStarting agent server...")
+        print("The agent factory will be called for every request that arrives.")
+        await adapter.run_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+

hugging-face-tool-agent/requirements.txt

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+azure_ai_agentserver_core-1.0.0b7-py3-none-any.whl
+azure_ai_agentserver_agentframework-1.0.0b7-py3-none-any.whl
+pytest==8.4.2
+python-dotenv==1.1.1