Update foundry hosting samples

This commit is contained in:
Tao Chen
2026-04-24 11:48:17 -07:00
parent 0b69d7fd15
commit 334ce4dfe0
55 changed files with 653 additions and 259 deletions
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.venv
__pycache__
*.pyc
*.pyo
*.pyd
.Python
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FOUNDRY_PROJECT_ENDPOINT="..."
AZURE_AI_MODEL_DEPLOYMENT_NAME="..."
TOOLBOX_NAME="..."
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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 [Agent Framework](https://github.com/microsoft/agent-framework) agent that uses **Foundry Toolbox** for tool discovery and hosted using the **Responses protocol**. Foundry Toolbox is a managed tool registry in Microsoft Foundry that lets you define tools centrally and share them across agents.
## Creating a Foundry Toolbox
You can create a Foundry Toolbox by code. Refer to this sample for an example: [Foundry Toolbox CRUD Sample](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-projects/samples/hosted_agents/sample_toolboxes_crud.py).
You can also create a Foundry Toolbox in the Foundry portal. Read more about it [in the Foundry toolbox documentation](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/toolbox).
> If you set up a project with this sample and provision the resources using `azd provision`, a Foundry Toolbox will be created with the specified tools in [`agent.manifest.yaml`](agent.manifest.yaml).
## How It Works
### Model Integration
The agent uses `FoundryChatClient` from the Agent Framework to create an OpenAI-compatible Responses client. It loads a named Foundry Toolbox via `client.get_toolbox(name)` — the toolbox is a server-side bundle of tool configurations (e.g., `code_interpreter`, `web_search`) defined in the Foundry portal or by `azd provision`. Omitting `version` resolves the toolbox's current default version at runtime.
The sample then narrows the toolbox to a subset of tool types via `select_toolbox_tools(toolbox, include_types=[...])` before handing it to the agent. This demonstrates how one toolbox can be reused across agents that each expose only the tools they need — here, the agent only sees `code_interpreter` even though the toolbox also includes `web_search`.
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.
## 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
> Depending on how you run the agent host, you can invoke the agent using `curl` (`Invoke-WebRequest` in PowerShell) or `azd`. Please refer to the [parent README](../../README.md) for more details. Use this README for sample queries you can send to the agent.
Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
```bash
curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "What tools do you have?"}'
```
## 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.
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name: agent-framework-agent-with-foundry-toolbox-responses
description: >
An Agent Framework agent with Foundry Toolbox integration.
metadata:
tags:
- Agent Framework
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Streaming
template:
name: agent-framework-agent-with-foundry-toolbox-responses
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
environment_variables:
- name: AZURE_AI_MODEL_DEPLOYMENT_NAME
value: "{{AZURE_AI_MODEL_DEPLOYMENT_NAME}}"
- name: TOOLBOX_NAME
value: "agent-tools"
resources:
- kind: model
id: gpt-4.1-mini
name: AZURE_AI_MODEL_DEPLOYMENT_NAME
- kind: toolbox
name: agent-tools
tools:
- type: web_search
name: web_search
- type: code_interpreter
name: code_interpreter
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kind: hosted
name: agent-framework-agent-with-foundry-toolbox-responses
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from agent_framework_foundry_hosting import ResponsesHostServer
from azure.identity import DefaultAzureCredential
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
async def main():
client = FoundryChatClient(
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
credential=DefaultAzureCredential(),
)
# Load the named toolbox from the Foundry project. Omitting `version`
# resolves the toolbox's current default version at runtime.
toolbox = await client.get_toolbox(os.environ["TOOLBOX_NAME"])
agent = Agent(
client=client,
instructions="You are a friendly assistant. Keep your answers brief.",
tools=toolbox,
# 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())
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agent-framework
agent-framework-foundry-hosting