Evan Mattson
f3db60fa65
* Python: Support GPT-5 verbosity option and restore Foundry agent_reference Adds verbosity as a typed Literal["low","medium","high"] field on OpenAIChatOptions (Responses API) and OpenAIChatCompletionOptions (Chat Completions API), set in the same way as the existing reasoning options. For the Responses API, top-level verbosity is translated to the nested text.verbosity shape the OpenAI service expects. The same field flows through to FoundryChatClient via the existing FoundryChatOptions alias. Also fixes #5582: PR #5447 removed the agent_reference injection from RawFoundryAgentChatClient._prepare_options, so first-turn calls against a Foundry Prompt Agent went out without model and without agent_reference and were rejected by the Responses API with "Missing required parameter: 'model'". Restores the injection on the non-preview path (allow_preview=False) and adds a guard test that asserts the preview path does not inject agent_reference, since the preview SDK injects it via project_client.get_openai_client(agent_name=...). Closes #5516 Closes #5582 * Python: Address Copilot review on PR #5619 - Foundry verbosity sample docstring: replace the misleading "set deployment name on model=" instruction with the actual env-var pattern the sample relies on (FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL). - _build_agent_reference docstring: clarify the helper is used for both Prompt Agents and HostedAgents on the non-preview path. - Add a Responses API test that locks in the documented precedence rule: when both top-level verbosity and text["verbosity"] are supplied, the top-level value wins. * Python: Drop redundant Foundry verbosity sample and list OpenAI sample in README - Remove samples/02-agents/providers/foundry/foundry_chat_client_verbosity.py per review feedback. The verbosity functionality is identical across the OpenAI and Foundry clients (FoundryChatOptions is an alias of OpenAIChatOptions), so a single sample on the OpenAI side is sufficient. - Add the new client_verbosity.py entry to the OpenAI samples README.
Agent Framework Foundry
This package contains the Microsoft Foundry integrations for Microsoft Agent Framework, including Foundry chat clients, preconfigured Foundry agents, Foundry embedding clients, and Foundry memory providers.
Toolboxes
A toolbox is a named, versioned bundle of hosted tool configurations — code interpreter, file search, image generation, MCP, web search, and so on — stored inside a Microsoft Foundry project. Toolboxes let you manage tool configuration once and reuse it across agents.
Authoring a toolbox
Toolboxes can be authored two ways:
- Foundry portal — create and version toolboxes through the UI without touching code.
- Programmatically — use the
azure-ai-projectsSDK to create, update, and version toolboxes from Python.
Toolbox authoring APIs (
ToolboxVersionObject,ToolboxObject,project_client.beta.toolboxes.*) requireazure-ai-projects>=2.1.0. Earlier versions can only consume toolboxes that already exist.
Using toolboxes with FoundryAgent
For hosted FoundryAgent, the toolbox must already be attached to the agent in the Microsoft Foundry project. Once attached, the agent invokes its toolbox tools transparently — no client-side wiring required — and you interact with the agent the same way you would with any other tool-equipped Foundry agent.
Using toolboxes with FoundryChatClient
There are two patterns for wiring a toolbox into a FoundryChatClient-backed agent.
1. Fetch, optionally filter, and pass the tools directly
Load the toolbox from the Microsoft Foundry project, optionally select a subset of its tools, and hand them to an Agent alongside any other tools you own:
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient, select_toolbox_tools
client = FoundryChatClient(...)
toolbox = await client.get_toolbox("my-toolbox", version="3")
# Pass the whole toolbox:
agent = Agent(client=client, tools=toolbox)
# Or filter to a subset first:
selected = select_toolbox_tools(toolbox, include_types=["code_interpreter", "mcp"])
agent = Agent(client=client, tools=selected)
See foundry_chat_client_with_toolbox.py for a full example, including combining multiple toolboxes.
2. Connect to the toolbox's MCP endpoint with MCPStreamableHTTPTool
Each toolbox is reachable as an MCP server. Instead of fetching and fanning out its individual tool definitions, you can point a MAF MCPStreamableHTTPTool at the toolbox's MCP endpoint — the agent then discovers and calls its tools over MCP at runtime:
from agent_framework import Agent, MCPStreamableHTTPTool
from agent_framework.foundry import FoundryChatClient
async with Agent(
client=FoundryChatClient(...),
instructions="You are a helpful assistant. Use the toolbox tools when useful.",
tools=MCPStreamableHTTPTool(
name="my_toolbox",
description="Tools served by my Foundry toolbox",
url="https://<your-toolbox-mcp-endpoint>",
),
) as agent:
result = await agent.run("What tools are available?")
print(result.text)