fix(toolchoice): default required tool choice on OpenAI-compatible custom endpoints

Reasoning models on OpenAI-compatible custom endpoints (e.g. GLM / Kimi)
often reason and reply with prose instead of emitting native tool calls
under the default auto tool choice, stalling the tool-driven scan loop
while burning tokens. Default force_required_tool_choice to auto (None):
enable tool_choice=required when an api_base is set (still gated to
models that accept it), off otherwise. Explicit 0/1 overrides.

strix/config/models.py

@@ -277,6 +277,51 @@ def _configure_litellm_compatibility() -> None:
     litellm.suppress_debug_info = True
 
     _register_litellm_cost_callback()
+    _install_openrouter_stream_cost_capture()
+
+
+def _install_openrouter_stream_cost_capture() -> None:
+    """Preserve OpenRouter's per-stream cost, which LiteLLM drops when streaming.
+
+    OpenRouter reports the real charge in ``usage.cost`` of the final stream
+    chunk, but LiteLLM rebuilds streamed responses from token-only fields and
+    discards it (its non-streamed path stashes the cost in hidden params; the
+    streaming path does not). Every scan streams, so without this the cost is
+    lost and Strix falls back to a cost-map estimate that is missing entirely
+    for new models (e.g. kimi-k3), reporting $0. Subclass the OpenRouter
+    streaming handler to record the cost keyed by response id so the cost
+    callback can recover the exact charge for the matching rebuilt response.
+    """
+    import litellm
+    from litellm.llms.openrouter.chat.transformation import (
+        OpenRouterChatCompletionStreamingHandler,
+        OpenrouterConfig,
+    )
+
+    from strix.report.state import streamed_openrouter_costs
+
+    class _StrixOpenRouterStreamingHandler(OpenRouterChatCompletionStreamingHandler):
+        def chunk_parser(self, chunk: dict[str, Any]) -> Any:
+            stream = super().chunk_parser(chunk)
+            streamed_openrouter_costs.remember(
+                chunk.get("id") or getattr(stream, "id", None), chunk.get("usage")
+            )
+            return stream
+
+    class _StrixOpenrouterConfig(OpenrouterConfig):
+        def get_model_response_iterator(
+            self, streaming_response: Any, sync_stream: bool, json_mode: bool | None = False
+        ) -> Any:
+            return _StrixOpenRouterStreamingHandler(
+                streaming_response=streaming_response,
+                sync_stream=sync_stream,
+                json_mode=json_mode,
+            )
+
+    # LiteLLM's provider-config factory reads litellm.OpenrouterConfig at call
+    # time, so overriding the attribute is enough for the subclass to take
+    # effect. (type: ignore — mypy rejects reassigning a class attribute.)
+    litellm.OpenrouterConfig = _StrixOpenrouterConfig  # type: ignore[misc]
 
 
 _OPENROUTER_ATTRIBUTION_HEADERS = {

strix/config/settings.py

@@ -36,8 +36,11 @@ class LlmSettings(BaseSettings):
         ),
     )
     reasoning_effort: ReasoningEffort = Field(default="high", alias="STRIX_REASONING_EFFORT")
-    force_required_tool_choice: bool = Field(
-        default=False,
+    # None = auto: force required tool choice on OpenAI-compatible custom
+    # endpoints (where reasoning models otherwise burn tokens thinking without
+    # ever calling a tool), off elsewhere. True/False overrides explicitly.
+    force_required_tool_choice: bool | None = Field(
+        default=None,
         alias="STRIX_FORCE_REQUIRED_TOOL_CHOICE",
     )
     prompt_cache: bool = Field(

strix/core/inputs.py

@@ -129,7 +129,8 @@ def make_model_settings(
     reasoning_effort: ReasoningEffort | None,
     *,
     model_name: str,
-    force_required_tool_choice: bool = False,
+    force_required_tool_choice: bool | None = None,
+    custom_api_base: bool = False,
     request_timeout: float | None = None,
     prompt_cache: bool = True,
 ) -> ModelSettings:
@@ -147,7 +148,14 @@ def make_model_settings(
         model_settings = model_settings.resolve(
             ModelSettings(reasoning=Reasoning(effort=reasoning_effort)),
         )
-    if force_required_tool_choice and _accepts_required_tool_choice(model_name):
+    # Strix is fully tool-driven, so a turn that returns prose instead of a tool
+    # call stalls the scan. Reasoning models on OpenAI-compatible custom
+    # endpoints (e.g. GLM / Kimi via cortecs) do exactly that under the default
+    # ``auto`` tool choice, so default to ``required`` there; ``None`` means auto.
+    use_required = (
+        custom_api_base if force_required_tool_choice is None else force_required_tool_choice
+    )
+    if use_required and _accepts_required_tool_choice(model_name):
         model_settings = model_settings.resolve(ModelSettings(tool_choice="required"))
 
     cache_extra_args = _prompt_cache_extra_args(model_name) if prompt_cache else None

strix/core/runner.py

@@ -248,6 +248,7 @@ async def run_strix_scan(
             settings.llm.reasoning_effort,
             model_name=resolved_model,
             force_required_tool_choice=settings.llm.force_required_tool_choice,
+            custom_api_base=bool(settings.llm.api_base),
             request_timeout=settings.llm.timeout,
             prompt_cache=settings.llm.prompt_cache,
         )

strix/report/state.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import subprocess
+import threading
 from collections.abc import Callable
 from datetime import UTC, datetime
 from importlib.metadata import PackageNotFoundError, version
@@ -95,6 +96,8 @@ def get_global_report_state() -> Optional["ReportState"]:
 def set_global_report_state(report_state: "ReportState") -> None:
     global _global_report_state  # noqa: PLW0603
     _global_report_state = report_state
+    # New run: drop any streamed-cost entries a prior run left unconsumed.
+    streamed_openrouter_costs.clear()
 
 
 class ReportState:
@@ -507,6 +510,72 @@ class ReportState:
         self._sync_llm_usage_record()
 
 
+def openrouter_stream_cost(usage: Any) -> float | None:
+    """Total OpenRouter-reported cost from a raw stream ``usage`` block, or None.
+
+    Non-BYOK responses bill everything to ``usage.cost``. BYOK responses put the
+    OpenRouter fee in ``usage.cost`` (often 0) and the provider charge in
+    ``usage.cost_details.upstream_inference_cost``, so BYOK totals sum the two.
+    """
+    if not isinstance(usage, dict):
+        return None
+    total = 0.0
+    cost = usage.get("cost")
+    if isinstance(cost, int | float) and cost > 0:
+        total += float(cost)
+    if bool(usage.get("is_byok")):
+        details = usage.get("cost_details")
+        upstream = details.get("upstream_inference_cost") if isinstance(details, dict) else None
+        if isinstance(upstream, int | float) and upstream > 0:
+            total += float(upstream)
+    return total if total > 0 else None
+
+
+def _response_id(completion_response: Any) -> str | None:
+    response_id = getattr(completion_response, "id", None)
+    if response_id is None and isinstance(completion_response, dict):
+        response_id = cast("dict[str, Any]", completion_response).get("id")
+    return response_id if isinstance(response_id, str) and response_id else None
+
+
+class StreamedOpenRouterCosts:
+    """Correlates OpenRouter's per-stream cost from the parser to the cost callback.
+
+    LiteLLM rebuilds streamed responses from token-only chunks and drops the
+    ``usage.cost`` OpenRouter reports in its final stream chunk (its non-streamed
+    path preserves it; streaming snapshots hidden params at stream start). Every
+    scan streams, so the OpenRouter streaming handler (see strix.config.models)
+    records the cost here keyed by response id, and the callback takes it back out
+    for the matching rebuilt response. Entries are removed on read; ``clear()``
+    runs per scan so nothing accumulates across runs.
+    """
+
+    def __init__(self) -> None:
+        self._costs: dict[str, float] = {}
+        self._lock = threading.Lock()
+
+    def remember(self, response_id: Any, usage: Any) -> None:
+        cost = openrouter_stream_cost(usage)
+        if cost is None or not (isinstance(response_id, str) and response_id):
+            return
+        with self._lock:
+            self._costs[response_id] = cost
+
+    def take(self, completion_response: Any) -> float | None:
+        response_id = _response_id(completion_response)
+        if response_id is None:
+            return None
+        with self._lock:
+            return self._costs.pop(response_id, None)
+
+    def clear(self) -> None:
+        with self._lock:
+            self._costs.clear()
+
+
+streamed_openrouter_costs = StreamedOpenRouterCosts()
+
+
 def litellm_cost_callback(
     kwargs: Any,
     completion_response: Any,
@@ -541,6 +610,11 @@ def litellm_cost_callback(
     if cost is None:
         cost = _usage_reported_cost(completion_response)
 
+    # Recover the exact OpenRouter cost the streaming handler stashed for this
+    # response — LiteLLM drops it from streamed usage, so nothing above sees it.
+    if cost is None:
+        cost = streamed_openrouter_costs.take(completion_response)
+
     if cost is None:
         cost = _estimate_response_cost(kwargs, completion_response)
 

tests/test_cost_tracking.py

@@ -7,9 +7,25 @@ from unittest.mock import MagicMock, patch
 
 import litellm
 import pytest
+from litellm.types.utils import LlmProviders
+from litellm.utils import ProviderConfigManager
 
-from strix.config.models import _configure_litellm_compatibility
-from strix.report.state import litellm_cost_callback
+from strix.config.models import (
+    _configure_litellm_compatibility,
+    _install_openrouter_stream_cost_capture,
+)
+from strix.report.state import (
+    ReportState,
+    litellm_cost_callback,
+    openrouter_stream_cost,
+    set_global_report_state,
+    streamed_openrouter_costs,
+)
+
+
+@pytest.fixture(autouse=True)
+def _clear_streamed_costs() -> None:
+    streamed_openrouter_costs.clear()
 
 
 def test_streaming_logging_stays_enabled_for_cost_callback() -> None:
@@ -151,3 +167,90 @@ def test_cost_callback_records_nothing_when_no_cost_available() -> None:
         litellm_cost_callback({"response_cost": None, "model": "x/y"}, response)
 
     report_state.record_observed_llm_cost.assert_not_called()
+
+
+def test_openrouter_stream_cost_extracts_plain_and_byok_totals() -> None:
+    assert openrouter_stream_cost({"cost": 0.003168}) == pytest.approx(0.003168)
+    assert openrouter_stream_cost(
+        {"cost": 0.01, "is_byok": True, "cost_details": {"upstream_inference_cost": 0.2}}
+    ) == pytest.approx(0.21)
+    # Upstream cost is only added for BYOK responses.
+    assert openrouter_stream_cost(
+        {"cost": 0.05, "is_byok": False, "cost_details": {"upstream_inference_cost": 0.04}}
+    ) == pytest.approx(0.05)
+    assert openrouter_stream_cost({"prompt_tokens": 10}) is None
+    assert openrouter_stream_cost(None) is None
+
+
+def test_cost_callback_recovers_streamed_openrouter_cost_by_response_id() -> None:
+    report_state = MagicMock()
+    streamed_openrouter_costs.remember("gen-abc", {"cost": 0.42})
+    # LiteLLM strips cost from the rebuilt streamed usage; only the id survives.
+    response = SimpleNamespace(id="gen-abc", usage=SimpleNamespace(cost=None), _hidden_params={})
+
+    with (
+        patch("strix.report.state.get_global_report_state", return_value=report_state),
+        patch("litellm.completion_cost", side_effect=ValueError("unknown model")),
+    ):
+        litellm_cost_callback({"response_cost": None, "model": "moonshotai/kimi-k3"}, response)
+
+    report_state.record_observed_llm_cost.assert_called_once_with(0.42)
+    # The entry is consumed so a later response cannot double-count it.
+    assert streamed_openrouter_costs.take(response) is None
+
+
+def test_streamed_openrouter_cost_prefers_provider_report_over_estimate() -> None:
+    report_state = MagicMock()
+    streamed_openrouter_costs.remember("gen-xyz", {"cost": 0.9})
+    response = SimpleNamespace(
+        id="gen-xyz",
+        usage=SimpleNamespace(prompt_tokens=10, completion_tokens=5, total_tokens=15),
+        _hidden_params={},
+    )
+
+    with (
+        patch("strix.report.state.get_global_report_state", return_value=report_state),
+        patch("litellm.completion_cost", return_value=0.1) as estimate,
+    ):
+        litellm_cost_callback({"response_cost": None, "model": "moonshotai/kimi-k3"}, response)
+
+    report_state.record_observed_llm_cost.assert_called_once_with(0.9)
+    estimate.assert_not_called()
+
+
+def test_streamed_openrouter_costs_ignores_entries_without_cost() -> None:
+    streamed_openrouter_costs.remember("gen-none", {"prompt_tokens": 10})
+    streamed_openrouter_costs.remember("", {"cost": 0.5})
+    assert streamed_openrouter_costs.take(SimpleNamespace(id="gen-none")) is None
+
+
+def test_streamed_openrouter_costs_cleared_on_new_run() -> None:
+    streamed_openrouter_costs.remember("gen-stale", {"cost": 0.7})
+    set_global_report_state(ReportState.__new__(ReportState))
+    assert streamed_openrouter_costs.take(SimpleNamespace(id="gen-stale")) is None
+
+
+def test_openrouter_stream_handler_records_cost() -> None:
+    _install_openrouter_stream_cost_capture()
+    # Resolve the config the way LiteLLM does in production so we prove the
+    # override is actually reachable through provider resolution, not just as a
+    # directly-constructed class.
+    config = ProviderConfigManager.get_provider_chat_config(
+        model="moonshotai/kimi-k3", provider=LlmProviders.OPENROUTER
+    )
+    assert config is not None
+    assert type(config).__name__ == "_StrixOpenrouterConfig"
+    handler = config.get_model_response_iterator(streaming_response=iter([]), sync_stream=True)
+
+    chunk = {
+        "id": "gen-stream",
+        "created": 1,
+        "model": "moonshotai/kimi-k3",
+        "choices": [{"index": 0, "delta": {"content": None}}],
+        "usage": {"prompt_tokens": 89, "completion_tokens": 138, "cost": 0.0035055},
+    }
+    handler.chunk_parser(chunk)
+
+    assert streamed_openrouter_costs.take(SimpleNamespace(id="gen-stream")) == pytest.approx(
+        0.0035055
+    )

tests/test_inputs.py

@@ -255,6 +255,44 @@ def test_make_model_settings_forces_required_for_anyllm_routed_openai_model() ->
     assert settings.tool_choice == "required"
 
 
+def test_make_model_settings_auto_forces_required_on_custom_openai_endpoint() -> None:
+    # GLM / Kimi via cortecs: openai/-routed reasoning model on a custom base.
+    settings = make_model_settings(
+        None,
+        model_name="openai/glm-5.2",
+        custom_api_base=True,
+    )
+
+    assert settings.tool_choice == "required"
+
+
+def test_make_model_settings_auto_skips_required_without_custom_endpoint() -> None:
+    settings = make_model_settings(None, model_name="openai/glm-5.2")
+
+    assert settings.tool_choice is None
+
+
+def test_make_model_settings_explicit_false_overrides_custom_endpoint() -> None:
+    settings = make_model_settings(
+        None,
+        model_name="openai/glm-5.2",
+        force_required_tool_choice=False,
+        custom_api_base=True,
+    )
+
+    assert settings.tool_choice is None
+
+
+def test_make_model_settings_auto_skips_required_for_non_openai_custom_endpoint() -> None:
+    settings = make_model_settings(
+        None,
+        model_name="anthropic/claude-3-7-sonnet-latest",
+        custom_api_base=True,
+    )
+
+    assert settings.tool_choice is None
+
+
 def test_make_model_settings_sets_request_timeout() -> None:
     settings = make_model_settings(
         "none",

tests/test_runner_rate_limit.py

@@ -38,6 +38,7 @@ async def test_persistent_rate_limit_stops_gracefully(
             model="openai/gpt-4o",
             reasoning_effort="high",
             force_required_tool_choice=False,
+            api_base=None,
             timeout=300,
             prompt_cache=True,
         ),

tests/test_runner_root_prompt.py

@@ -46,6 +46,7 @@ def _patch_engine_scaffold(
             model="openai/gpt-4o",
             reasoning_effort="high",
             force_required_tool_choice=False,
+            api_base=None,
             timeout=300,
             prompt_cache=True,
         ),