chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import logging
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import litellm
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import pytest
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from litellm.types.utils import Choices, Message, ModelResponse, Usage
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from agents.extensions.models.litellm_model import LitellmModel
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from agents.model_settings import ModelSettings
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from agents.models.interface import ModelTracing
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@pytest.mark.allow_call_model_methods
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@pytest.mark.asyncio
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async def test_extra_body_is_forwarded(monkeypatch):
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"""
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Forward `extra_body` via LiteLLM's dedicated kwarg.
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This ensures that provider-specific request fields stay nested under `extra_body`
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so LiteLLM can merge them into the upstream request body itself.
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"""
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captured: dict[str, object] = {}
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async def fake_acompletion(model, messages=None, **kwargs):
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captured.update(kwargs)
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msg = Message(role="assistant", content="ok")
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choice = Choices(index=0, message=msg)
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return ModelResponse(choices=[choice], usage=Usage(0, 0, 0))
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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settings = ModelSettings(
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temperature=0.1, extra_body={"cached_content": "some_cache", "foo": 123}
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)
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model = LitellmModel(model="test-model")
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await model.get_response(
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system_instructions=None,
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input=[],
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model_settings=settings,
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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)
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assert captured["extra_body"] == {"cached_content": "some_cache", "foo": 123}
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assert "cached_content" not in captured
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assert "foo" not in captured
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@pytest.mark.allow_call_model_methods
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@pytest.mark.asyncio
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async def test_extra_body_reasoning_effort_is_promoted(monkeypatch):
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"""
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Ensure reasoning_effort from extra_body is promoted to the top-level parameter.
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"""
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captured: dict[str, object] = {}
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async def fake_acompletion(model, messages=None, **kwargs):
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captured.update(kwargs)
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msg = Message(role="assistant", content="ok")
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choice = Choices(index=0, message=msg)
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return ModelResponse(choices=[choice], usage=Usage(0, 0, 0))
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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# GitHub issue context: https://github.com/openai/openai-agents-python/issues/1764.
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settings = ModelSettings(
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extra_body={"reasoning_effort": "none", "cached_content": "some_cache"}
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)
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model = LitellmModel(model="test-model")
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await model.get_response(
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system_instructions=None,
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input=[],
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model_settings=settings,
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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)
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assert captured["reasoning_effort"] == "none"
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assert captured["extra_body"] == {"cached_content": "some_cache"}
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assert settings.extra_body == {"reasoning_effort": "none", "cached_content": "some_cache"}
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@pytest.mark.allow_call_model_methods
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@pytest.mark.asyncio
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async def test_reasoning_effort_prefers_model_settings(monkeypatch):
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"""
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Verify explicit ModelSettings.reasoning takes precedence over extra_body entries.
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"""
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from openai.types.shared import Reasoning
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captured: dict[str, object] = {}
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async def fake_acompletion(model, messages=None, **kwargs):
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captured.update(kwargs)
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msg = Message(role="assistant", content="ok")
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choice = Choices(index=0, message=msg)
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return ModelResponse(choices=[choice], usage=Usage(0, 0, 0))
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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settings = ModelSettings(
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reasoning=Reasoning(effort="low"),
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extra_body={"reasoning_effort": "high"},
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)
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model = LitellmModel(model="test-model")
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await model.get_response(
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system_instructions=None,
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input=[],
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model_settings=settings,
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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)
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# reasoning_effort is string when no summary is provided (backward compatible)
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assert captured["reasoning_effort"] == "low"
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assert "extra_body" not in captured
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assert settings.extra_body == {"reasoning_effort": "high"}
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@pytest.mark.allow_call_model_methods
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@pytest.mark.asyncio
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async def test_extra_body_reasoning_effort_overrides_extra_args(monkeypatch):
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"""
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Ensure extra_body reasoning_effort wins over extra_args when both are provided.
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"""
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captured: dict[str, object] = {}
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async def fake_acompletion(model, messages=None, **kwargs):
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captured.update(kwargs)
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msg = Message(role="assistant", content="ok")
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choice = Choices(index=0, message=msg)
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return ModelResponse(choices=[choice], usage=Usage(0, 0, 0))
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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# GitHub issue context: https://github.com/openai/openai-agents-python/issues/1764.
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settings = ModelSettings(
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extra_body={"reasoning_effort": "none"},
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extra_args={"reasoning_effort": "low", "custom_param": "custom"},
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)
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model = LitellmModel(model="test-model")
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await model.get_response(
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system_instructions=None,
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input=[],
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model_settings=settings,
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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)
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assert captured["reasoning_effort"] == "none"
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assert captured["custom_param"] == "custom"
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assert "extra_body" not in captured
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assert settings.extra_args == {"reasoning_effort": "low", "custom_param": "custom"}
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@pytest.mark.allow_call_model_methods
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@pytest.mark.asyncio
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async def test_extra_body_metadata_stays_nested(monkeypatch):
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"""
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Keep extra_body metadata nested even when top-level metadata is also set.
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LiteLLM resolves top-level metadata and extra_body separately. Flattening the nested
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metadata dict loses the caller's intended request shape for OpenAI-compatible proxies.
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"""
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captured: dict[str, object] = {}
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async def fake_acompletion(model, messages=None, **kwargs):
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captured.update(kwargs)
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msg = Message(role="assistant", content="ok")
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choice = Choices(index=0, message=msg)
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return ModelResponse(choices=[choice], usage=Usage(0, 0, 0))
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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settings = ModelSettings(
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metadata={"sdk": "agents"},
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extra_body={
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"metadata": {"trace_user_id": "user-123", "generation_id": "gen-456"},
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"cached_content": "some_cache",
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},
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)
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model = LitellmModel(model="test-model")
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await model.get_response(
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system_instructions=None,
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input=[],
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model_settings=settings,
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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)
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assert captured["metadata"] == {"sdk": "agents"}
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assert captured["extra_body"] == {
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"metadata": {"trace_user_id": "user-123", "generation_id": "gen-456"},
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"cached_content": "some_cache",
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}
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@pytest.mark.allow_call_model_methods
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model_name",
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[
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"openai/gpt-5-mini",
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"anthropic/claude-sonnet-4-5",
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"gemini/gemini-2.5-pro",
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],
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)
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async def test_reasoning_summary_uses_scalar_effort_and_warns(
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monkeypatch, caplog: pytest.LogCaptureFixture, model_name: str
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):
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"""
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Ensure reasoning.summary does not change the LiteLLM chat-completions argument shape.
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LitellmModel should continue to pass a scalar reasoning_effort value and warn that summary
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is ignored on this path, regardless of the provider encoded in the model string.
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"""
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from openai.types.shared import Reasoning
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captured: dict[str, object] = {}
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async def fake_acompletion(model, messages=None, **kwargs):
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captured.update(kwargs)
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msg = Message(role="assistant", content="ok")
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choice = Choices(index=0, message=msg)
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return ModelResponse(choices=[choice], usage=Usage(0, 0, 0))
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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settings = ModelSettings(
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reasoning=Reasoning(effort="medium", summary="auto"),
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)
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model = LitellmModel(model=model_name)
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with caplog.at_level(logging.WARNING, logger="openai.agents"):
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await model.get_response(
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system_instructions=None,
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input=[],
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model_settings=settings,
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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)
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assert captured["reasoning_effort"] == "medium"
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warning_messages = [
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record.message
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for record in caplog.records
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if "does not forward Reasoning.summary" in record.message
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]
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assert len(warning_messages) == 1
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