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Evan Mattson 36cafe4e5a Python: Raise clear handler registration error for unresolved TypeVar annotations (#4944 )

* Raise clear handler registration error for unresolved TypeVar (#4943)

Detect unresolved TypeVar in message parameter annotations during handler
registration in both _validate_handler_signature (Executor) and
_validate_function_signature (FunctionExecutor). Raises a ValueError with
an actionable message recommending @handler(input=..., output=...) or
@executor(input=..., output=...) instead of letting TypeVar leak through
to a confusing TypeCompatibilityError during workflow edge validation.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address review feedback for #4943: reorder checks and harden function executor

- Move TypeVar check before validate_workflow_context_annotation in
  _executor.py so users see the more actionable error first
- Wrap get_type_hints in try/except in _function_executor.py matching
  the defensive pattern in _executor.py
- Repurpose duplicate test to cover bounded TypeVar rejection
- Add test_function_executor_allows_concrete_types for test symmetry

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Narrow get_type_hints except clause and add missing tests (#4943)

- Narrow `except Exception` to `except (NameError, AttributeError, RecursionError)`
  in both _executor.py and _function_executor.py so unexpected failures in
  get_type_hints are not silently swallowed.
- Add test_handler_unresolvable_annotation_raises to test_function_executor_future.py
  exercising the except branch of get_type_hints in the function executor path.
- Add test_function_executor_rejects_bounded_typevar_in_message_annotation to
  test_function_executor.py for parity with the Executor bounded TypeVar test.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add error ordering test for TypeVar vs WorkflowContext priority (#4943)

Add test_handler_typevar_error_takes_priority_over_context_error to verify
that when a handler has both a TypeVar message and an unannotated ctx, the
TypeVar error is raised first (the more actionable issue).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Fix image content serialization sending null file_id to Foundry API

Omit file_id from input_image dict when not present instead of including
it as null, which Azure AI Foundry's stricter schema validation rejects.

* Python: Fix Foundry API rejecting rich content in function_call_output

Azure AI Foundry does not support list-format output in function_call_output
items. Add SUPPORTS_RICH_FUNCTION_OUTPUT flag (default True) to
RawOpenAIChatClient, set to False in RawFoundryChatClient so Foundry
falls back to string output for tool results with images/files.

Also omit file_id from input_image dicts when not set, since Foundry
rejects explicit nulls.

* Python: Surface rich tool content as user message when Foundry lacks support

When SUPPORTS_RICH_FUNCTION_OUTPUT is False, image/file items from tool
results are injected as a follow-up user message so the model can still
process the visual content via Foundry's supported user message format.

* Xfail Foundry image integration test for the meantime

---------

Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

36cafe4e5a · 2026-04-07 16:59:17 +00:00

History

agent_framework_openai

Python: Raise clear handler registration error for unresolved TypeVar annotations (#4944 )

2026-04-07 16:59:17 +00:00

tests

Python: Raise clear handler registration error for unresolved TypeVar annotations (#4944 )

2026-04-07 16:59:17 +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: [BREAKING] update to v1.0.0 (#5062 )

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

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")