feat(models): STRIX_STREAM_MODE opt-in non-streaming for custom endpoints

Replace the api_base-based non-streaming heuristic with an explicit
STRIX_STREAM_MODE=auto|always|never setting. auto/always stream (unchanged
default); never routes through the non-streaming wrapper for endpoints whose
streamed responses drop tool calls.

strix/config/models.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import contextlib
 import inspect
 import os
+import time
 from typing import TYPE_CHECKING, Any
 
 from agents import (
@@ -13,6 +14,8 @@ from agents import (
     set_tracing_disabled,
 )
 from agents.model_settings import ModelSettings
+from agents.models.fake_id import FAKE_RESPONSES_ID
+from agents.models.interface import Model
 from agents.models.multi_provider import MultiProvider
 from agents.models.openai_responses import OpenAIResponsesModel
 from agents.retry import (
@@ -21,6 +24,12 @@ from agents.retry import (
     RetryPolicyContext,
     retry_policies,
 )
+from openai.types.responses import (
+    Response,
+    ResponseCompletedEvent,
+    ResponseOutputItemDoneEvent,
+    ResponseUsage,
+)
 from openai.types.shared import Reasoning
 
 from strix.config import codex
@@ -30,8 +39,14 @@ from strix.config.loader import load_settings
 if TYPE_CHECKING:
     from collections.abc import AsyncIterator
 
-    from agents.models.interface import Model, ModelProvider
+    from agents.agent_output import AgentOutputSchemaBase
+    from agents.handoffs import Handoff
+    from agents.items import ModelResponse, TResponseInputItem, TResponseStreamEvent
+    from agents.models.interface import ModelProvider, ModelTracing
+    from agents.tool import Tool
+    from agents.usage import Usage
     from openai import AsyncOpenAI
+    from openai.types.responses import ResponsePromptParam
 
     from strix.config.settings import ReasoningEffort, Settings
 
@@ -135,6 +150,99 @@ class _CodexResponsesModel(OpenAIResponsesModel):
                     await result
 
 
+def _to_response_usage(usage: Usage | None) -> ResponseUsage | None:
+    if usage is None:
+        return None
+    return ResponseUsage(
+        input_tokens=usage.input_tokens,
+        output_tokens=usage.output_tokens,
+        total_tokens=usage.total_tokens,
+        input_tokens_details=usage.input_tokens_details,
+        output_tokens_details=usage.output_tokens_details,
+    )
+
+
+class _NonStreamingModel(Model):
+    """Run a model non-streamed but expose the streaming interface the runner uses.
+
+    Some OpenAI-compatible endpoints (notably gateways serving reasoning models)
+    return valid ``tool_calls`` for a non-streamed completion but, when streamed,
+    emit the tool call as plain text or drop it and close the stream — leaving
+    Strix's tool-driven loop with nothing to execute. Selecting
+    ``STRIX_STREAM_MODE=never`` routes through this wrapper, which makes the real
+    request non-streamed (where tool calling works) and synthesizes the minimal
+    event sequence the runner consumes from a stream, so the rest of the pipeline
+    is unchanged. The only user-visible difference is no token-by-token output.
+    """
+
+    def __init__(self, inner: Model) -> None:
+        self._inner = inner
+
+    async def get_response(self, *args: Any, **kwargs: Any) -> ModelResponse:
+        return await self._inner.get_response(*args, **kwargs)
+
+    async def stream_response(
+        self,
+        system_instructions: str | None,
+        input: str | list[TResponseInputItem],  # noqa: A002
+        model_settings: ModelSettings,
+        tools: list[Tool],
+        output_schema: AgentOutputSchemaBase | None,
+        handoffs: list[Handoff],
+        tracing: ModelTracing,
+        *,
+        previous_response_id: str | None = None,
+        conversation_id: str | None = None,
+        prompt: ResponsePromptParam | None = None,
+    ) -> AsyncIterator[TResponseStreamEvent]:
+        model_response = await self._inner.get_response(
+            system_instructions,
+            input,
+            model_settings,
+            tools,
+            output_schema,
+            handoffs,
+            tracing,
+            previous_response_id=previous_response_id,
+            conversation_id=conversation_id,
+            prompt=prompt,
+        )
+
+        sequence = 0
+        for index, item in enumerate(model_response.output):
+            yield ResponseOutputItemDoneEvent(
+                item=item,
+                output_index=index,
+                type="response.output_item.done",
+                sequence_number=sequence,
+            )
+            sequence += 1
+
+        response = Response(
+            id=model_response.response_id or FAKE_RESPONSES_ID,
+            created_at=time.time(),
+            model=str(getattr(self._inner, "model", "")),
+            object="response",
+            output=model_response.output,
+            tool_choice="auto",
+            tools=[],
+            parallel_tool_calls=False,
+            usage=_to_response_usage(model_response.usage),
+        )
+        yield ResponseCompletedEvent(
+            response=response,
+            type="response.completed",
+            sequence_number=sequence,
+        )
+
+    def get_retry_advice(self, request: Any) -> Any:
+        return self._inner.get_retry_advice(request)
+
+
+def _should_run_non_streamed(settings: Settings) -> bool:
+    return settings.llm.stream_mode == "never"
+
+
 class StrixProvider(MultiProvider):
     """Route any non-OpenAI prefix through LiteLLM with the prefix preserved,
     so users type ``deepseek/deepseek-chat`` rather than
@@ -159,14 +267,18 @@ class StrixProvider(MultiProvider):
         return self._get_fallback_provider("litellm"), original_model_name
 
     def get_model(self, model_name: str | None) -> Model:
+        settings = load_settings()
         slug = codex.subscription_model(model_name)
         if slug:
             return _CodexResponsesModel(
                 slug,
                 codex.get_subscription_client(),
-                reasoning_effort=load_settings().llm.reasoning_effort,
+                reasoning_effort=settings.llm.reasoning_effort,
             )
-        return super().get_model(model_name)
+        model = super().get_model(model_name)
+        if _should_run_non_streamed(settings):
+            return _NonStreamingModel(model)
+        return model
 
 
 DEFAULT_MODEL_RETRY = ModelRetrySettings(

strix/config/settings.py

@@ -9,6 +9,7 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
 ReasoningEffort = Literal["none", "minimal", "low", "medium", "high", "xhigh"]
+StreamMode = Literal["auto", "always", "never"]
 
 _BASE_CONFIG = SettingsConfigDict(
     case_sensitive=False,
@@ -40,6 +41,12 @@ class LlmSettings(BaseSettings):
         default=False,
         alias="STRIX_FORCE_REQUIRED_TOOL_CHOICE",
     )
+    # auto/always stream; never runs non-streamed, for endpoints that stream tool
+    # calls incorrectly (some OpenAI-compatible gateways serving reasoning models).
+    stream_mode: StreamMode = Field(
+        default="auto",
+        alias="STRIX_STREAM_MODE",
+    )
     prompt_cache: bool = Field(
         default=True,
         alias="STRIX_PROMPT_CACHE",

tests/test_nonstreaming_model.py

@@ -0,0 +1,173 @@
+"""Tests for the non-streaming wrapper used on custom OpenAI-compatible endpoints."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import TYPE_CHECKING, cast
+from unittest.mock import patch
+
+import pytest
+from agents.items import ModelResponse
+from agents.model_settings import ModelSettings
+from agents.models.interface import Model, ModelTracing
+from agents.usage import Usage
+from openai.types.responses import (
+    ResponseCompletedEvent,
+    ResponseFunctionToolCall,
+    ResponseOutputItemDoneEvent,
+    ResponseStreamEvent,
+)
+
+from strix.config.models import StrixProvider, _NonStreamingModel, _to_response_usage
+
+
+if TYPE_CHECKING:
+    from collections.abc import AsyncIterator
+
+
+def _tool_call() -> ResponseFunctionToolCall:
+    return ResponseFunctionToolCall(
+        arguments='{"command": "ls"}',
+        call_id="call_1",
+        name="terminal_execute",
+        type="function_call",
+    )
+
+
+class _FakeModel(Model):
+    def __init__(self, response: ModelResponse) -> None:
+        self.model = "fake-model"
+        self._response = response
+        self.get_response_calls = 0
+
+    async def get_response(self, *_args: object, **_kwargs: object) -> ModelResponse:
+        self.get_response_calls += 1
+        return self._response
+
+    async def stream_response(  # pragma: no cover
+        self, *_args: object, **_kwargs: object
+    ) -> AsyncIterator[ResponseStreamEvent]:
+        for _ in range(0):
+            yield cast("ResponseStreamEvent", None)
+        raise AssertionError("inner stream_response must never be called")
+
+
+def _settings(*, api_base: str | None, stream_mode: str = "auto") -> SimpleNamespace:
+    return SimpleNamespace(
+        llm=SimpleNamespace(
+            model="openai/glm",
+            api_base=api_base,
+            stream_mode=stream_mode,
+            reasoning_effort="high",
+        )
+    )
+
+
+def test_to_response_usage_maps_token_details() -> None:
+    usage = Usage(requests=1, input_tokens=10, output_tokens=5, total_tokens=15)
+    usage.input_tokens_details.cached_tokens = 4
+    usage.output_tokens_details.reasoning_tokens = 3
+    mapped = _to_response_usage(usage)
+    assert mapped is not None
+    assert (mapped.input_tokens, mapped.output_tokens, mapped.total_tokens) == (10, 5, 15)
+    assert mapped.input_tokens_details.cached_tokens == 4
+    assert mapped.output_tokens_details.reasoning_tokens == 3
+
+
+def test_to_response_usage_none() -> None:
+    assert _to_response_usage(None) is None
+
+
+@pytest.mark.asyncio
+async def test_stream_response_synthesizes_tool_call_from_non_streamed() -> None:
+    tool_call = _tool_call()
+    inner = _FakeModel(
+        ModelResponse(
+            output=[tool_call],
+            usage=Usage(requests=1, input_tokens=10, output_tokens=5, total_tokens=15),
+            response_id="resp_123",
+        )
+    )
+    wrapper = _NonStreamingModel(inner)
+
+    events = [
+        event
+        async for event in wrapper.stream_response(
+            "sys",
+            "hi",
+            ModelSettings(),
+            [],
+            None,
+            [],
+            ModelTracing.DISABLED,
+        )
+    ]
+
+    assert inner.get_response_calls == 1
+    item_done = [e for e in events if isinstance(e, ResponseOutputItemDoneEvent)]
+    completed = [e for e in events if isinstance(e, ResponseCompletedEvent)]
+    assert len(item_done) == 1
+    assert item_done[0].item == tool_call
+    assert len(completed) == 1
+    final = completed[0].response
+    assert final.output == [tool_call]
+    assert final.id == "resp_123"
+    assert final.usage is not None
+    assert final.usage.total_tokens == 15
+    # sequence numbers are strictly increasing
+    assert [e.sequence_number for e in events] == list(range(len(events)))
+
+
+@pytest.mark.asyncio
+async def test_stream_response_delegates_get_response() -> None:
+    inner = _FakeModel(
+        ModelResponse(output=[], usage=Usage(), response_id=None),
+    )
+    wrapper = _NonStreamingModel(inner)
+    result = await wrapper.get_response(
+        "sys", "hi", ModelSettings(), [], None, [], ModelTracing.DISABLED
+    )
+    assert result is inner._response
+    assert inner.get_response_calls == 1
+
+
+def test_get_model_auto_streams_custom_endpoint() -> None:
+    sentinel = _FakeModel(ModelResponse(output=[], usage=Usage(), response_id=None))
+    with (
+        patch("strix.config.models.load_settings", return_value=_settings(api_base="http://x/v1")),
+        patch(
+            "agents.models.multi_provider.MultiProvider.get_model",
+            return_value=sentinel,
+        ),
+    ):
+        model = StrixProvider().get_model("openai/glm")
+    assert model is sentinel
+
+
+def test_get_model_auto_streams_hosted() -> None:
+    sentinel = _FakeModel(ModelResponse(output=[], usage=Usage(), response_id=None))
+    with (
+        patch("strix.config.models.load_settings", return_value=_settings(api_base="")),
+        patch(
+            "agents.models.multi_provider.MultiProvider.get_model",
+            return_value=sentinel,
+        ),
+    ):
+        model = StrixProvider().get_model("openai/gpt-4o")
+    assert model is sentinel
+
+
+def test_get_model_stream_mode_never_wraps() -> None:
+    sentinel = _FakeModel(ModelResponse(output=[], usage=Usage(), response_id=None))
+    with (
+        patch(
+            "strix.config.models.load_settings",
+            return_value=_settings(api_base="http://x/v1", stream_mode="never"),
+        ),
+        patch(
+            "agents.models.multi_provider.MultiProvider.get_model",
+            return_value=sentinel,
+        ),
+    ):
+        model = StrixProvider().get_model("openai/glm")
+    assert isinstance(model, _NonStreamingModel)