Evan Mattson
09a3d0d307
Fixes microsoft/agent-framework#3295. When the OpenAI Responses chat client sends a request that carries previous_response_id / conversation_id / conversation, the server already has the prior turn's response items and rejects duplicates with "Duplicate item found with id fc_xxx". The chat client was re-sending them inline whenever the input messages still carried the items in additional_properties (workflow replay, history providers, etc.), which broke any tool-using agent with persistent history. Decisions: - Single chokepoint: _prepare_message_for_openai. When the resulting request uses service-side storage, drop function_call, reasoning, approval-request/response, and local-shell-call items from the wire input. Keep function_result with its call_id; the server pairs it to the prior function_call via that key. - function_result is preserved unconditionally except for the local-shell variant, which carries its own server-issued item id. - No public API change. Wire format change is subtractive and only on requests that would otherwise 400. - Re-pointed the strict-xfail in test_full_conversation.py from #4047 to #3295. Kept xfail because the test asserts executor-level session-id clearing, which is the defense-in-depth half tracked by 3295-03; this slice closes the wire-level half. Files: - python/packages/openai/agent_framework_openai/_chat_client.py: strip rule applied alongside the existing reasoning-item branch. - python/packages/openai/tests/openai/test_openai_chat_client.py: four new tests pin the contract (function_call, approval, local-shell-call stripped under storage; everything kept without storage). Updated pre-existing tests that exercised the storage-on path to either pass request_uses_service_side_storage=False explicitly or assert the new strip behavior. - python/packages/foundry/tests/foundry/test_foundry_chat_client.py: same explicit storage-off opt-in for the inherited test. - python/packages/core/tests/workflow/test_full_conversation.py: re-pointed xfail reason to #3295 and the executor-level follow-up. Notes for next iteration: - 3295-01 (HITL wire-format validation against live OpenAI/Foundry) was not run; it requires the user's API credentials. The PRD design is locked but the empirical confirmation is still pending. If script 3 fails on either provider, this slice may need to be revisited. - 3295-03 (clear service_session_id in AgentExecutor on full-history replay) remains open. After it lands the xfail in test_full_conversation.py can be removed. - pytest was not run in this iteration because uv-based pytest commands required interactive approval. Validation rests on careful reading; next iteration should run the openai + core test suites.
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
Each toolbox is reachable as an MCP server. Connect to the toolbox's MCP endpoint with MCPStreamableHTTPTool — 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)