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https://github.com/microsoft/agent-framework.git
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Update foundry hosting samples
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.venv
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__pycache__
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*.pyc
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*.pyo
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*.pyd
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.Python
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FOUNDRY_PROJECT_ENDPOINT="..."
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AZURE_AI_MODEL_DEPLOYMENT_NAME="..."
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TOOLBOX_NAME="..."
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FROM python:3.12-slim
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WORKDIR /app
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COPY . user_agent/
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WORKDIR /app/user_agent
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RUN if [ -f requirements.txt ]; then \
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pip install -r requirements.txt; \
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else \
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echo "No requirements.txt found"; \
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fi
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EXPOSE 8088
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CMD ["python", "main.py"]
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# What this sample demonstrates
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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.
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## Creating a Foundry Toolbox
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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).
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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).
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> 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).
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## How It Works
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### Model Integration
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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.
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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`.
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See [main.py](main.py) for the full implementation.
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### Agent Hosting
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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.
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## Running the Agent Host
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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.
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## Interacting with the agent
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> 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.
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Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
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```bash
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curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "What tools do you have?"}'
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```
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## Deploying the Agent to Foundry
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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
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description: >
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An Agent Framework agent with Foundry Toolbox integration.
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metadata:
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tags:
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- Agent Framework
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- AI Agent Hosting
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- Azure AI AgentServer
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- Responses Protocol
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- Streaming
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template:
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name: agent-framework-agent-with-foundry-toolbox-responses
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kind: hosted
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protocols:
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- protocol: responses
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version: 1.0.0
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environment_variables:
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- name: AZURE_AI_MODEL_DEPLOYMENT_NAME
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value: "{{AZURE_AI_MODEL_DEPLOYMENT_NAME}}"
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- name: TOOLBOX_NAME
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value: "agent-tools"
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resources:
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- kind: model
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id: gpt-4.1-mini
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name: AZURE_AI_MODEL_DEPLOYMENT_NAME
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- kind: toolbox
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name: agent-tools
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tools:
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- type: web_search
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name: web_search
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- type: code_interpreter
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name: code_interpreter
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kind: hosted
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name: agent-framework-agent-with-foundry-toolbox-responses
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protocols:
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- protocol: responses
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version: 1.0.0
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resources:
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cpu: "0.25"
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memory: 0.5Gi
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import Agent
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from agent_framework.foundry import FoundryChatClient
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from agent_framework_foundry_hosting import ResponsesHostServer
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from azure.identity import DefaultAzureCredential
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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async def main():
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=DefaultAzureCredential(),
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)
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# Load the named toolbox from the Foundry project. Omitting `version`
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# resolves the toolbox's current default version at runtime.
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toolbox = await client.get_toolbox(os.environ["TOOLBOX_NAME"])
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agent = Agent(
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client=client,
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instructions="You are a friendly assistant. Keep your answers brief.",
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tools=toolbox,
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# History will be managed by the hosting infrastructure, thus there
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# is no need to store history by the service. Learn more at:
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# https://developers.openai.com/api/reference/resources/responses/methods/create
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default_options={"store": False},
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)
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server = ResponsesHostServer(agent)
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await server.run_async()
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if __name__ == "__main__":
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asyncio.run(main())
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+2
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agent-framework
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agent-framework-foundry-hosting
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