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Evan Mattson 317ef4491e Python: Fix hosted MCP replay producing orphan function_call_output (#5581 )

* Python: Fix hosted MCP replay producing orphan function_call_output

Resolves part of #5546. After a turn ran a hosted MCP / Foundry-toolbox-MCP
tool, the next turn's replayed input array carried a function_call_output
with an mcp_* call_id and no matching function_call, and the Responses API
returned a 400.

Two layers covered here:

* Chat-client serialize layer (packages/openai): adds mcp_server_tool_call
  and mcp_server_tool_result cases to _prepare_message_for_openai and
  _prepare_content_for_openai. Pairs are coalesced via a post-pass into a
  single mcp_call input item carrying both arguments and output. Orphan
  results are dropped (debug-logged) rather than serialized as orphan
  function_call_output, which is what the Responses API rejected.

* Host read layer (packages/foundry_hosting): _item_to_message and
  _output_item_to_message now route custom_tool_call_output whose
  call_id.startswith("mcp_") to Content.from_mcp_server_tool_result.
  Non-mcp_ call_ids continue to produce Content.from_function_result.
  Symmetric with the host write-side choice for hosted-MCP results.

Two further fixes (agentserver SDK additions, host write-side single-item
emission) remain tracked on the issue and depend on an SDK release.

* Python: Fix pyright unknown-type in _stringify_mcp_output

cast(Sequence[Any], output) after the isinstance check so pyright stops
flagging the loop variable as unknown. Also normalizes a couple of
em-dashes in docstrings I introduced in the prior commit.

* Python: Harden _stringify_mcp_output for dict-shaped MCP outputs

Address Copilot review on PR #5581. Today the helper falls back to
str() for any non-string, non-text-attribute entry, which produces
Python repr (single-quoted dicts) for the canonical MCP raw-JSON
text-content shape `{"type": "text", "text": "..."}` and any other
dict-shaped output.

Three small changes:

* List-entry path: prefer plain string entries, then `.text` attribute
  (Content objects), then `entry["text"]` for Mapping entries in the
  canonical MCP shape, then JSON-encode anything else.
* Final fallback: `json.dumps(output, default=str)` so Mappings and
  scalars produce valid JSON rather than Python repr.
* Two new unit tests covering the dict-with-text shape and the
  non-text-dict JSON fallback.

* Python: Suppress mypy redundant-cast on _stringify_mcp_output narrowing

The cast is needed by pyright (reportUnknownVariableType) but mypy
considers it redundant after the preceding isinstance narrowing.
Pyright's behavior is correct for the strict-mode reporting we run,
so keep the cast and silence mypy on the line.

317ef4491e · 2026-04-30 21:01:52 +00:00

History

agent_framework_openai

Python: Fix hosted MCP replay producing orphan function_call_output (#5581 )

2026-04-30 21:01:52 +00:00

tests

Python: Fix hosted MCP replay producing orphan function_call_output (#5581 )

2026-04-30 21:01:52 +00:00

AGENTS.md

Python: [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces (#4990 )

2026-03-31 20:36:21 +00:00

LICENSE

Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818 )

2026-03-25 09:56:29 +00:00

pyproject.toml

Python: bump package versions for 1.2.2 release (#5561 )

2026-04-29 17:51:48 +09:00

README.md

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

README.md

agent-framework-openai

OpenAI integration for Microsoft Agent Framework.

This package provides:

OpenAIChatClient for the OpenAI Responses API
OpenAIChatCompletionClient for the Chat Completions API
OpenAIEmbeddingClient for embeddings

Installation

pip install agent-framework-openai

Which chat client should I use?

Use OpenAIChatClient for new work unless you specifically need the Chat Completions API.

OpenAIChatClient uses the Responses API and is the preferred general-purpose chat client.
OpenAIChatCompletionClient uses the Chat Completions API and is mainly for compatibility with existing Chat Completions-based integrations.

The previous deprecated Responses alias has been removed. Use OpenAIChatClient directly.

Environment variables

OpenAI

These variables are used when the client is configured for OpenAI:

Variable	Purpose
`OPENAI_API_KEY`	OpenAI API key
`OPENAI_ORG_ID`	OpenAI organization ID
`OPENAI_BASE_URL`	Custom OpenAI-compatible base URL
`OPENAI_MODEL`	Generic fallback model
`OPENAI_CHAT_MODEL`	Preferred model for `OpenAIChatClient`
`OPENAI_CHAT_COMPLETION_MODEL`	Preferred model for `OpenAIChatCompletionClient`
`OPENAI_EMBEDDING_MODEL`	Preferred model for `OpenAIEmbeddingClient`

Model lookup order:

OpenAIChatClient: OPENAI_CHAT_MODEL -> OPENAI_MODEL
OpenAIChatCompletionClient: OPENAI_CHAT_COMPLETION_MODEL -> OPENAI_MODEL
OpenAIEmbeddingClient: OPENAI_EMBEDDING_MODEL -> OPENAI_MODEL

These model variables are only consulted when you do not pass model= directly. In other words, OpenAIChatClient(model="...") ignores OPENAI_CHAT_MODEL, and OpenAIChatCompletionClient(model="...") ignores OPENAI_CHAT_COMPLETION_MODEL.

Azure OpenAI

These variables are used when the client is configured for Azure OpenAI:

Variable	Purpose
`AZURE_OPENAI_ENDPOINT`	Azure OpenAI resource endpoint
`AZURE_OPENAI_BASE_URL`	Full Azure OpenAI base URL (`.../openai/v1`)
`AZURE_OPENAI_API_KEY`	Azure OpenAI API key
`AZURE_OPENAI_API_VERSION`	Azure OpenAI API version
`AZURE_OPENAI_MODEL`	Generic fallback deployment
`AZURE_OPENAI_CHAT_MODEL`	Preferred deployment for `OpenAIChatClient`
`AZURE_OPENAI_CHAT_COMPLETION_MODEL`	Preferred deployment for `OpenAIChatCompletionClient`
`AZURE_OPENAI_EMBEDDING_MODEL`	Preferred deployment for `OpenAIEmbeddingClient`

Deployment lookup order:

OpenAIChatClient: AZURE_OPENAI_CHAT_MODEL -> AZURE_OPENAI_MODEL
OpenAIChatCompletionClient: AZURE_OPENAI_CHAT_COMPLETION_MODEL -> AZURE_OPENAI_MODEL
OpenAIEmbeddingClient: AZURE_OPENAI_EMBEDDING_MODEL -> AZURE_OPENAI_MODEL

For Azure routing, the same rule applies: the client-specific deployment variable is checked first, then the generic AZURE_OPENAI_MODEL fallback. Passing model= overrides both environment variables.

When both OpenAI and Azure environment variables are present, the generic clients prefer OpenAI when OPENAI_API_KEY is configured. To use Azure explicitly, pass azure_endpoint or credential.

OpenAI example

from agent_framework.openai import OpenAIChatClient

client = OpenAIChatClient(model="gpt-4.1")

Azure OpenAI example

from azure.identity.aio import AzureCliCredential

from agent_framework.openai import OpenAIChatClient

client = OpenAIChatClient(
    model="my-responses-deployment",
    azure_endpoint="https://my-resource.openai.azure.com",
    credential=AzureCliCredential(),
)

ChatClient vs ChatCompletionClient

Use OpenAIChatClient when you want the Responses API as your default chat surface.

Use OpenAIChatCompletionClient when you specifically need the Chat Completions API:

from agent_framework.openai import OpenAIChatCompletionClient

client = OpenAIChatCompletionClient(model="gpt-4o-mini")