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Evan Mattson 3a463b8bf6 Python: bump package versions for 1.2.1 release (#5536)
* Python: bump package versions for 1.2.1 release

PATCH bump (1.2.0 -> 1.2.1) for the released cohort. The release window
covers two PRs, no new public APIs:

- agent-framework-core: prevent inner_exception from being lost in
  AgentFrameworkException (#5167)
- samples: add requirements.txt and .env.example to the a2a/ hosting
  sample for pip-based setup (#5510)

Per lockstep convention, all 21 beta packages stamp 1.0.0b260428 and all
3 alpha packages stamp 1.0.0a260428, regardless of per-package code
churn. Every non-core package floor on agent-framework-core is raised to
>=1.2.1 to keep cohort signaling consistent. Date stamp reflects the
local (Asia) cut date 2026-04-28.

* Python: silence pyright unknown-type warnings in hosted-env detection

`azure.ai.agentserver.core` is probed at runtime via `importlib.util.find_spec`
and is not a declared dependency. The existing `# pyright: ignore[reportMissingImports]`
suppresses the missing-import warning, but at `lowest-direct` resolution pyright
still reports the imported symbol (`AgentConfig`) and its members (`from_env`,
`is_hosted`) as unknown, breaking `validate-dependency-bounds-test` for
`packages/core`.

Extend the existing ignore to cover `reportUnknownVariableType` on the import
and `reportUnknownMemberType` on the call site so the bounds check returns to
green. Behavior is unchanged.

Latent since #5455 (shipped in 1.2.0).

* Python: raise agent-framework-gemini lower bound to google-genai>=1.65.0

The Gemini chat client references several `google.genai.types` symbols
(`FileSearch`, `ThinkingLevel`, `SearchTypes`, `McpServer`,
`StreamableHttpTransport`, plus call-site keyword args `mcp_servers` and
`search_types`) that are not present at the lower bound of `google-genai>=1.0.0`.
At `lowest-direct` resolution this caused `validate-dependency-bounds-test` to
fail for `packages/gemini` with eleven `reportAttributeAccessIssue` /
`reportUnknownVariableType` errors.

Walking the upstream `google.genai.types` API:
- `GoogleMaps`, `AuthConfig`: present from 1.40.0
- `FileSearch`: introduced in 1.49.0
- `ThinkingLevel`: introduced in 1.55.0
- `SearchTypes`, `McpServer`, `StreamableHttpTransport`: introduced in 1.65.0

Bump the lower bound to 1.65.0 — the minimum version that exposes every symbol
the package actually uses. Keep the `<2.0.0` upper cap unchanged. With this
bump `validate-dependency-bounds-test` passes for both lower and upper
resolution scenarios across all 27 workspace packages.

Latent since #4847 (Gemini package introduction in 1.1.0); aggravated by
subsequent feature additions that pulled in newer `types.*` symbols.

* Python: add dependabot bumps to 1.2.1 CHANGELOG

Catalog the 15 dependabot dependency updates that merged on `upstream/main`
between python-1.2.0 and the 1.2.1 cut window under a new Changed section:

- Workspace dev/runtime deps: `rich`, `prek`, `python-multipart`, `pyasn1`,
  `pytest` (ag-ui, devui, lab), `uv` (lab)
- Frontend deps: `vite` (devui, chatkit), `postcss` (devui, chatkit, handoff),
  `picomatch` (devui, handoff)

CHANGELOG-only — no source or pyproject.toml changes. PRs themselves merged
upstream independently of this release branch and will be brought in via the
PR merge.
3a463b8bf6 · 2026-04-28 18:23:26 +09:00
History
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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-projects SDK to create, update, and version toolboxes from Python.

Toolbox authoring APIs (ToolboxVersionObject, ToolboxObject, project_client.beta.toolboxes.*) require azure-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)