Bump 1.0.2 -> 1.0.3 (#537)

Register a litellm.success_callback that captures kwargs['response_cost']
into a new observed-cost bucket on LLMUsageLedger. record() skips the
tokens-times-registry estimate for LiteLLM-routed models so we do not
double-count with the callback; OpenAI direct routes keep estimating
since LiteLLM is not invoked for them. Per-agent attribution for
LiteLLM-routed calls is apportioned by token share at to_record() time.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "strix-agent"
-version = "1.0.2"
+version = "1.0.3"
 description = "Open-source AI Hackers for your apps"
 readme = "README.md"
 license = "Apache-2.0"
@@ -219,6 +219,8 @@ ignore = [
 # ReportState carries scan artifact/report fields and
 # a runtime ``Callable`` annotation on ``vulnerability_found_callback``.
 "strix/report/state.py" = ["TC003", "PLR0912", "PLR0915", "E501", "PERF401"]
+"strix/report/usage.py" = ["PLC0415"]
+"strix/config/models.py" = ["PLC0415"]
 # Interface utility branches per scope-mode / target-type combination;
 # splitting would obscure the decision tree without simplifying it.
 "strix/interface/utils.py" = ["PLR0912", "BLE001", "PLC0415"]

strix/agents/factory.py

@@ -395,7 +395,6 @@ def build_strix_agent(
         instructions=instructions,
         tools=tools,
         tool_use_behavior=_finish_tool_use_behavior,
-        reset_tool_choice=interactive,
         model=None,
         capabilities=[
             Filesystem(

strix/config/models.py

@@ -5,7 +5,8 @@ from __future__ import annotations
 import os
 from typing import TYPE_CHECKING
 
-from agents import set_default_openai_api, set_default_openai_key
+from agents import set_default_openai_api, set_default_openai_key, set_tracing_disabled
+from agents.models.multi_provider import MultiProvider
 from agents.retry import (
     ModelRetryBackoffSettings,
     ModelRetrySettings,
@@ -14,10 +15,31 @@ from agents.retry import (
 
 
 if TYPE_CHECKING:
+    from agents.models.interface import ModelProvider
+
     from strix.config.settings import Settings
 
 
-_SDK_PREFIXES = {"any-llm", "litellm", "openai"}
+class StrixProvider(MultiProvider):
+    """Route any non-OpenAI prefix through LiteLLM with the prefix preserved,
+    so users type ``deepseek/deepseek-chat`` rather than
+    ``litellm/deepseek/deepseek-chat``.
+    """
+
+    def _resolve_prefixed_model(
+        self,
+        *,
+        original_model_name: str,
+        prefix: str,
+        stripped_model_name: str | None,
+    ) -> tuple[ModelProvider, str | None]:
+        if prefix in {"openai", "litellm", "any-llm"}:
+            return super()._resolve_prefixed_model(
+                original_model_name=original_model_name,
+                prefix=prefix,
+                stripped_model_name=stripped_model_name,
+            )
+        return self._get_fallback_provider("litellm"), original_model_name
 
 
 DEFAULT_MODEL_RETRY = ModelRetrySettings(
@@ -37,17 +59,14 @@ DEFAULT_MODEL_RETRY = ModelRetrySettings(
 
 
 def configure_sdk_model_defaults(settings: Settings) -> None:
-    """Apply Strix config to SDK-native defaults.
-
-    OpenAI-compatible base URLs are handled by the SDK OpenAI provider.
-    Non-OpenAI providers should use the SDK's native ``litellm/`` or
-    ``any-llm/`` routing, produced by :func:`normalize_model_name`.
-    """
+    """Apply Strix config to SDK-native defaults."""
     llm = settings.llm
+    set_tracing_disabled(True)
     _configure_litellm_compatibility()
     if llm.api_key:
         set_default_openai_key(llm.api_key, use_for_tracing=False)
         _configure_litellm_default("api_key", llm.api_key)
+        _mirror_api_key_to_provider_env(llm.model, llm.api_key)
     if llm.api_base:
         os.environ["OPENAI_BASE_URL"] = llm.api_base
         _configure_litellm_default("api_base", llm.api_base)
@@ -56,12 +75,50 @@ def configure_sdk_model_defaults(settings: Settings) -> None:
         set_default_openai_api("responses")
 
 
+def _mirror_api_key_to_provider_env(model_name: str | None, api_key: str) -> None:
+    if not model_name:
+        return
+    import litellm
+
+    name = model_name.strip()
+    for prefix in ("litellm/", "any-llm/"):
+        if name.lower().startswith(prefix):
+            name = name[len(prefix) :]
+            break
+    try:
+        report = litellm.validate_environment(model=name.lower())
+    except Exception:  # noqa: BLE001
+        return
+    for env_key in report.get("missing_keys") or []:
+        if env_key.endswith("_API_KEY"):
+            os.environ.setdefault(env_key, api_key)
+
+
 def _configure_litellm_compatibility() -> None:
-    """Enable LiteLLM's permissive param-handling mode."""
+    """Enable LiteLLM's permissive param handling and disable its callbacks."""
     import litellm
 
     litellm.drop_params = True
     litellm.modify_params = True
+    litellm.turn_off_message_logging = True
+    litellm.disable_streaming_logging = True
+    litellm.suppress_debug_info = True
+
+    _register_litellm_cost_callback()
+
+
+def _register_litellm_cost_callback() -> None:
+    import litellm
+
+    from strix.report.state import litellm_cost_callback
+
+    for bucket_name in ("success_callback", "_async_success_callback"):
+        bucket = getattr(litellm, bucket_name, None)
+        if not isinstance(bucket, list):
+            continue
+        if litellm_cost_callback in bucket:
+            continue
+        bucket.append(litellm_cost_callback)
 
 
 def _configure_litellm_default(name: str, value: str) -> None:
@@ -71,30 +128,35 @@ def _configure_litellm_default(name: str, value: str) -> None:
     setattr(litellm, name, value)
 
 
-def normalize_model_name(model_name: str) -> str:
-    """Normalize friendly Strix model names to SDK-native model ids."""
-    model = model_name.strip()
-    if not model:
-        return model
-
-    if "/" in model:
-        prefix = model.split("/", 1)[0].lower()
-        if prefix in _SDK_PREFIXES:
-            return model
-        return f"litellm/{model}"
-
-    lower = model.lower()
-    if lower.startswith("claude"):
-        return f"litellm/anthropic/{model}"
-    if lower.startswith("gemini"):
-        return f"litellm/gemini/{model}"
-
-    return model
-
-
 def uses_chat_completions_tool_schema(model_name: str, settings: Settings) -> bool:
     """Return whether the resolved SDK route can only receive JSON function tools."""
     model = model_name.strip().lower()
-    if model.startswith(("litellm/", "any-llm/")):
+    if "/" in model and not model.startswith("openai/"):
         return True
-    return bool(settings.llm.api_base)
+    if settings.llm.api_base:
+        return True
+    return not model_supports_reasoning(model_name)
+
+
+def model_supports_reasoning(model_name: str) -> bool:
+    import litellm
+
+    name = model_name.strip().lower()
+    for prefix in ("litellm/", "any-llm/", "openai/"):
+        if name.startswith(prefix):
+            name = name[len(prefix) :]
+            break
+    entry = litellm.model_cost.get(name)
+    if entry is None and "/" in name:
+        entry = litellm.model_cost.get(name.rsplit("/", 1)[1])
+    return bool(entry and entry.get("supports_reasoning"))
+
+
+def is_known_openai_bare_model(model_name: str) -> bool:
+    import litellm
+
+    name = model_name.strip().lower()
+    if not name or "/" in name:
+        return False
+    entry = litellm.model_cost.get(name)
+    return bool(entry and entry.get("litellm_provider") == "openai")

strix/core/execution.py

@@ -349,12 +349,20 @@ async def _run_cycle(  # noqa: PLR0912
             )
             await coordinator.attach_stream(agent_id, stream)
             try:
-                async for event in stream.stream_events():
-                    if event_sink is not None:
-                        try:
-                            event_sink(agent_id, event)
-                        except Exception:
-                            logger.exception("stream event sink failed for %s", agent_id)
+                try:
+                    async for event in stream.stream_events():
+                        if event_sink is not None:
+                            try:
+                                event_sink(agent_id, event)
+                            except Exception:
+                                logger.exception("stream event sink failed for %s", agent_id)
+                except RuntimeError as stream_exc:
+                    if "after shutdown" not in str(stream_exc):
+                        raise
+                    logger.warning(
+                        "Ignoring LiteLLM end-of-stream shutdown race for %s",
+                        agent_id,
+                    )
                 if stream.run_loop_exception is not None:
                     raise stream.run_loop_exception
             finally:

strix/core/inputs.py

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any
 from agents.model_settings import ModelSettings
 from openai.types.shared import Reasoning
 
-from strix.config.models import DEFAULT_MODEL_RETRY
+from strix.config.models import DEFAULT_MODEL_RETRY, model_supports_reasoning
 
 
 if TYPE_CHECKING:
@@ -108,20 +108,19 @@ def build_scope_context(scan_config: dict[str, Any]) -> dict[str, Any]:
 
 def make_model_settings(
     reasoning_effort: ReasoningEffort | None,
+    *,
+    model_name: str,
 ) -> ModelSettings:
-    # Anthropic + DeepSeek thinking reject ``tool_choice="required"`` outright
-    # when reasoning is enabled; OpenAI o-series accepts both but doesn't need
-    # the safety net. When reasoning is on we let the model self-select tools
-    # and rely on the system prompt + the ``_finish_tool_use_behavior`` callback
-    # to keep the loop converging on a lifecycle tool.
-    use_reasoning = reasoning_effort is not None and reasoning_effort != "none"
     model_settings = ModelSettings(
         parallel_tool_calls=False,
-        tool_choice=None if use_reasoning else "required",
         retry=DEFAULT_MODEL_RETRY,
         include_usage=True,
     )
-    if use_reasoning:
+    if (
+        reasoning_effort is not None
+        and reasoning_effort != "none"
+        and model_supports_reasoning(model_name)
+    ):
         model_settings = model_settings.resolve(
             ModelSettings(reasoning=Reasoning(effort=reasoning_effort)),
         )

strix/core/runner.py

@@ -15,8 +15,8 @@ from agents.sandbox import SandboxRunConfig
 from strix.agents.factory import build_strix_agent, make_child_factory
 from strix.config import load_settings
 from strix.config.models import (
+    StrixProvider,
     configure_sdk_model_defaults,
-    normalize_model_name,
     uses_chat_completions_tool_schema,
 )
 from strix.core.agents import AgentCoordinator
@@ -90,7 +90,7 @@ async def run_strix_scan(
 
     settings = load_settings()
     configure_sdk_model_defaults(settings)
-    resolved_model = normalize_model_name(model or settings.llm.model or "")
+    resolved_model = (model or settings.llm.model or "").strip()
     if not resolved_model:
         raise RuntimeError(
             "No LLM model configured. Set STRIX_LLM env or pass model= to run_strix_scan().",
@@ -153,9 +153,13 @@ async def run_strix_scan(
         is_whitebox = any(t.get("type") == "local_code" for t in targets)
         skills = list(scan_config.get("skills") or [])
         root_task = build_root_task(scan_config)
-        model_settings = make_model_settings(settings.llm.reasoning_effort)
+        model_settings = make_model_settings(
+            settings.llm.reasoning_effort,
+            model_name=resolved_model,
+        )
         run_config = RunConfig(
             model=resolved_model,
+            model_provider=StrixProvider(),
             model_settings=model_settings,
             sandbox=SandboxRunConfig(client=bundle["client"], session=bundle["session"]),
             trace_include_sensitive_data=False,

strix/interface/main.py

@@ -12,7 +12,6 @@ from pathlib import Path
 
 from agents.model_settings import ModelSettings
 from agents.models.interface import ModelTracing
-from agents.models.multi_provider import MultiProvider
 from docker.errors import DockerException
 from rich.console import Console
 from rich.panel import Panel
@@ -23,7 +22,11 @@ from strix.config import (
     load_settings,
     persist_current,
 )
-from strix.config.models import configure_sdk_model_defaults, normalize_model_name
+from strix.config.models import (
+    StrixProvider,
+    configure_sdk_model_defaults,
+    is_known_openai_bare_model,
+)
 from strix.core.paths import run_dir_for, runtime_state_dir
 from strix.interface.cli import run_cli
 from strix.interface.tui import run_tui
@@ -98,7 +101,8 @@ def validate_environment() -> None:
                 error_text.append("• ", style="white")
                 error_text.append("STRIX_LLM", style="bold cyan")
                 error_text.append(
-                    " - Model name to use (e.g., 'gpt-5.4' or 'claude-sonnet-4-6')\n",
+                    " - Model name to use (e.g., 'openai/gpt-5.4' or "
+                    "'anthropic/claude-opus-4-7')\n",
                     style="white",
                 )
 
@@ -137,7 +141,7 @@ def validate_environment() -> None:
                     )
 
         error_text.append("\nExample setup:\n", style="white")
-        error_text.append("export STRIX_LLM='gpt-5.4'\n", style="dim white")
+        error_text.append("export STRIX_LLM='openai/gpt-5.4'\n", style="dim white")
 
         if missing_optional_vars:
             for var in missing_optional_vars:
@@ -215,7 +219,39 @@ async def warm_up_llm() -> None:
         configure_sdk_model_defaults(settings)
         llm = settings.llm
 
-        model = MultiProvider().get_model(normalize_model_name(llm.model or ""))
+        raw_model = (llm.model or "").strip()
+        if (
+            raw_model
+            and "/" not in raw_model
+            and not is_known_openai_bare_model(raw_model)
+            and not llm.api_base
+        ):
+            warn_text = Text()
+            warn_text.append("UNKNOWN MODEL NAME", style="bold yellow")
+            warn_text.append("\n\n", style="white")
+            warn_text.append(f"'{raw_model}'", style="bold cyan")
+            warn_text.append(
+                " is not a known OpenAI model. Bare names route to OpenAI by default.\n"
+                "If you meant a non-OpenAI provider, use the '",
+                style="white",
+            )
+            warn_text.append("<provider>/<model>", style="bold cyan")
+            warn_text.append(
+                "' form, e.g. 'anthropic/claude-opus-4-7', 'deepseek/deepseek-v4-pro'.",
+                style="white",
+            )
+            console.print(
+                Panel(
+                    warn_text,
+                    title="[bold white]STRIX",
+                    title_align="left",
+                    border_style="yellow",
+                    padding=(1, 2),
+                ),
+            )
+            sys.exit(1)
+
+        model = StrixProvider().get_model(raw_model)
         await asyncio.wait_for(
             model.get_response(
                 system_instructions="You are a helpful assistant.",
@@ -231,7 +267,7 @@ async def warm_up_llm() -> None:
             ),
             timeout=llm.timeout,
         )
-        logger.info("LLM warm-up succeeded for model %s", normalize_model_name(llm.model or ""))
+        logger.info("LLM warm-up succeeded for model %s", (llm.model or "").strip())
 
     except Exception as e:
         logger.exception("LLM warm-up failed")

strix/interface/tui/app.py

@@ -663,9 +663,9 @@ class QuitScreen(ModalScreen):  # type: ignore[misc]
             self.app.pop_screen()
             event.prevent_default()
 
-    def on_button_pressed(self, event: Button.Pressed) -> None:
+    async def on_button_pressed(self, event: Button.Pressed) -> None:
         if event.button.id == "quit":
-            self.app.action_custom_quit()
+            await self.app.action_custom_quit()
         else:
             self.app.pop_screen()
 
@@ -751,6 +751,7 @@ class StrixTUIApp(App):  # type: ignore[misc]
             self.report_state.cleanup()
 
         def signal_handler(_signum: int, _frame: Any) -> None:
+            self._teardown_sandbox_blocking(timeout=10.0)
             self.report_state.cleanup(status="interrupted")
             sys.exit(0)
 
@@ -1142,9 +1143,9 @@ class StrixTUIApp(App):  # type: ignore[misc]
             return (text, Text(), False)
 
         if status == "waiting":
-            keymap = Text()
-            keymap.append("Send message to resume", style="dim")
-            return (Text(" "), keymap, False)
+            text = Text()
+            text.append("Send message to resume", style="dim")
+            return (text, Text(), False)
 
         if status == "running":
             if self._agent_has_real_activity(agent_id):
@@ -1632,9 +1633,9 @@ class StrixTUIApp(App):  # type: ignore[misc]
 
         self.push_screen(HelpScreen())
 
-    def action_request_quit(self) -> None:
+    async def action_request_quit(self) -> None:
         if self.show_splash or not self.is_mounted:
-            self.action_custom_quit()
+            await self.action_custom_quit()
             return
 
         if len(self.screen_stack) > 1:
@@ -1643,7 +1644,7 @@ class StrixTUIApp(App):  # type: ignore[misc]
         try:
             self.query_one("#main_container")
         except (ValueError, Exception):
-            self.action_custom_quit()
+            await self.action_custom_quit()
             return
 
         self.push_screen(QuitScreen())
@@ -1703,16 +1704,38 @@ class StrixTUIApp(App):  # type: ignore[misc]
             self._scan_loop,
         )
 
-    def action_custom_quit(self) -> None:
+    async def action_custom_quit(self) -> None:
+        await asyncio.to_thread(self._teardown_sandbox_blocking, timeout=10.0)
+
         if self._scan_thread and self._scan_thread.is_alive():
             self._scan_stop_event.set()
-
-            self._scan_thread.join(timeout=1.0)
+            self._scan_thread.join(timeout=2.0)
 
         self.report_state.cleanup()
 
         self.exit()
 
+    def _teardown_sandbox_blocking(self, *, timeout: float) -> None:
+        loop = self._scan_loop
+        if loop is None or loop.is_closed():
+            return
+        run_name = self.scan_config.get("run_name")
+        if not run_name:
+            return
+        future = asyncio.run_coroutine_threadsafe(
+            session_manager.cleanup(run_name),
+            loop,
+        )
+        try:
+            future.result(timeout=timeout)
+        except TimeoutError:
+            logger.warning(
+                "Sandbox cleanup timed out after %.1fs; container may still be running",
+                timeout,
+            )
+        except Exception:
+            logger.exception("Sandbox cleanup failed")
+
     def _is_widget_safe(self, widget: Any) -> bool:
         try:
             _ = widget.screen

strix/report/dedupe.py

@@ -8,14 +8,13 @@ from typing import TYPE_CHECKING, Any
 
 from agents.model_settings import ModelSettings
 from agents.models.interface import ModelTracing
-from agents.models.multi_provider import MultiProvider
 from openai.types.responses import ResponseOutputMessage
 
 from strix.config import load_settings
 from strix.config.models import (
     DEFAULT_MODEL_RETRY,
+    StrixProvider,
     configure_sdk_model_defaults,
-    normalize_model_name,
 )
 from strix.report.state import get_global_report_state
 
@@ -188,8 +187,8 @@ async def check_duplicate(
         )
 
         configure_sdk_model_defaults(settings)
-        resolved_model = normalize_model_name(model_name)
-        model = MultiProvider().get_model(resolved_model)
+        resolved_model = model_name.strip()
+        model = StrixProvider().get_model(resolved_model)
         response = await model.get_response(
             system_instructions=DEDUPE_SYSTEM_PROMPT,
             input=user_msg,

strix/report/state.py

@@ -230,6 +230,9 @@ class ReportState:
         ):
             self.save_run_data()
 
+    def record_observed_llm_cost(self, cost: float) -> None:
+        self._llm_usage.record_observed_cost(cost)
+
     def get_total_llm_usage(self) -> dict[str, Any]:
         return dict(self.run_record.get("llm_usage") or self._build_llm_usage_record())
 
@@ -343,3 +346,45 @@ class ReportState:
     def _hydrate_llm_usage(self, raw_usage: Any) -> None:
         self._llm_usage.hydrate(raw_usage)
         self._sync_llm_usage_record()
+
+
+def litellm_cost_callback(
+    kwargs: Any,
+    completion_response: Any,
+    _start_time: Any = None,
+    _end_time: Any = None,
+) -> None:
+    """LiteLLM ``success_callback`` adapter; forwards observed cost to the active scan."""
+    cost: float | None = None
+    raw = kwargs.get("response_cost") if isinstance(kwargs, dict) else None
+    if isinstance(raw, int | float) and raw > 0:
+        cost = float(raw)
+
+    if cost is None:
+        hidden = getattr(completion_response, "_hidden_params", None) or {}
+        candidate = hidden.get("response_cost") if isinstance(hidden, dict) else None
+        if isinstance(candidate, int | float) and candidate > 0:
+            cost = float(candidate)
+        else:
+            headers = hidden.get("additional_headers") or {} if isinstance(hidden, dict) else {}
+            raw = (
+                headers.get("llm_provider-x-litellm-response-cost")
+                if isinstance(headers, dict)
+                else None
+            )
+            try:
+                value = float(raw) if raw is not None else None
+            except (TypeError, ValueError):
+                value = None
+            if value is not None and value > 0:
+                cost = value
+
+    if cost is None or cost <= 0:
+        return
+    report_state = get_global_report_state()
+    if report_state is None:
+        return
+    try:
+        report_state.record_observed_llm_cost(cost)
+    except Exception:
+        logger.exception("Failed to record observed LiteLLM cost")

strix/report/usage.py

@@ -19,7 +19,6 @@ class LLMUsageLedger:
         self._agent_usage: dict[str, Usage] = {}
         self._agent_metadata: dict[str, dict[str, str]] = {}
         self._total_cost = 0.0
-        self._agent_cost: dict[str, float] = {}
 
     def record(
         self,
@@ -33,8 +32,6 @@ class LLMUsageLedger:
             return False
 
         normalized_agent_id = str(agent_id or "unknown")
-        estimated_cost = _estimate_litellm_cost(usage, model)
-
         self._total_usage.add(usage)
         self._agent_usage.setdefault(normalized_agent_id, Usage()).add(usage)
 
@@ -44,31 +41,38 @@ class LLMUsageLedger:
         if model:
             metadata["model"] = model
 
-        if estimated_cost is not None:
-            self._total_cost += estimated_cost
-            self._agent_cost[normalized_agent_id] = (
-                self._agent_cost.get(normalized_agent_id, 0.0) + estimated_cost
-            )
+        if not _is_litellm_routed(model):
+            estimated = _estimate_litellm_cost(usage, model)
+            if estimated:
+                self._total_cost += estimated
 
         return True
 
+    def record_observed_cost(self, cost: float) -> None:
+        if isinstance(cost, int | float) and cost > 0:
+            self._total_cost += float(cost)
+
     def to_record(self) -> dict[str, Any]:
         record = serialize_usage(self._total_usage)
         record["cost"] = _round_cost(self._total_cost)
-        record["cost_source"] = "litellm_estimate"
         record["agents"] = []
 
+        agent_tokens = {aid: _resolve_total_tokens(u) for aid, u in self._agent_usage.items()}
+        total_tokens = sum(agent_tokens.values())
         for agent_id in sorted(self._agent_usage):
             usage = self._agent_usage[agent_id]
             metadata = self._agent_metadata.get(agent_id, {})
+            agent_cost = (
+                self._total_cost * (agent_tokens[agent_id] / total_tokens) if total_tokens else 0.0
+            )
+
             agent_record = serialize_usage(usage)
             agent_record.update(
                 {
                     "agent_id": agent_id,
                     "agent_name": metadata.get("agent_name") or agent_id,
                     "model": metadata.get("model"),
-                    "cost": _round_cost(self._agent_cost.get(agent_id, 0.0)),
-                    "cost_source": "litellm_estimate",
+                    "cost": _round_cost(agent_cost),
                 }
             )
             record["agents"].append(agent_record)
@@ -80,7 +84,6 @@ class LLMUsageLedger:
         self._agent_usage.clear()
         self._agent_metadata.clear()
         self._total_cost = 0.0
-        self._agent_cost.clear()
 
         if not isinstance(raw_usage, dict):
             return
@@ -92,11 +95,8 @@ class LLMUsageLedger:
             self._total_usage = Usage()
 
         self._total_cost = _float_or_zero(raw_usage.get("cost"))
-        agents = raw_usage.get("agents") or []
-        if not isinstance(agents, list):
-            return
 
-        for raw_agent in agents:
+        for raw_agent in raw_usage.get("agents") or []:
             if not isinstance(raw_agent, dict):
                 continue
             agent_id = str(raw_agent.get("agent_id") or "").strip()
@@ -116,7 +116,24 @@ class LLMUsageLedger:
             if isinstance(model, str) and model:
                 metadata["model"] = model
             self._agent_metadata[agent_id] = metadata
-            self._agent_cost[agent_id] = _float_or_zero(raw_agent.get("cost"))
+
+
+def _resolve_total_tokens(usage: Usage) -> int:
+    total = max(0, int(usage.total_tokens or 0))
+    if total > 0:
+        return total
+    prompt = _int_or_zero(getattr(usage, "input_tokens", 0))
+    completion = _int_or_zero(getattr(usage, "output_tokens", 0))
+    return prompt + completion
+
+
+def _is_litellm_routed(model: str | None) -> bool:
+    if not model:
+        return False
+    name = model.strip().lower()
+    if "/" not in name:
+        return False
+    return not name.startswith("openai/")
 
 
 def _usage_has_activity(usage: Usage) -> bool:
@@ -171,18 +188,25 @@ def _estimate_litellm_entry_cost(entry: Any, model: str) -> float | None:
     if completion_details:
         usage_payload["completion_tokens_details"] = completion_details
 
-    try:
-        from litellm import completion_cost
+    from litellm import completion_cost
 
-        cost = completion_cost(
-            completion_response={
-                "model": model.split("/", 1)[-1],
-                "usage": usage_payload,
-            },
-            model=model,
-        )
-    except Exception:  # noqa: BLE001 - LiteLLM raises plain Exception for unknown model prices.
-        logger.debug("LiteLLM cost estimate unavailable for model %s", model, exc_info=True)
+    candidates = [model]
+    if "/" in model:
+        candidates.append(model.split("/", 1)[-1])
+
+    cost: Any = None
+    for candidate in candidates:
+        try:
+            cost = completion_cost(
+                completion_response={"model": candidate, "usage": usage_payload},
+                model=model,
+            )
+            break
+        except Exception:  # nosec B112  # noqa: BLE001, S112
+            continue
+
+    if cost is None:
+        logger.debug("LiteLLM cost estimate unavailable for model %s", model)
         return None
 
     return cost if isinstance(cost, int | float) and cost >= 0 else None

uv.lock

@@ -956,7 +956,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.83.7"
+version = "1.88.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -972,9 +972,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/77/2b/b58bf6bbcbc3d0e55d0a84fdf9128e5b1436517f46fce89b1cd8948ebb81/litellm-1.83.7.tar.gz", hash = "sha256:e2f2cb99df2e2b2eab63f1354faa45c88dd7c8d40c18eb648afb1b349c689633", size = 17791694, upload-time = "2026-04-13T17:35:01.606Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9b/8c/6cfce5d15554e076b8438a955436e9e6e2a2bd39c76539656c1c3861c369/litellm-1.88.0.tar.gz", hash = "sha256:4ff794493e40bd86c6f13e91dcb3e1aad697403fd46a96902196d93356ba48f4", size = 13886026, upload-time = "2026-06-06T23:25:17.93Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/75/80/caeb4cdcad96451ba83ad3ba2a9da08b1e1a915fa845c489f56ea044488b/litellm-1.83.7-py3-none-any.whl", hash = "sha256:5784a1d9a9a4a8acd6ca1e347003a5e2e1b3c749b4d41e7da4904577adade111", size = 16069807, upload-time = "2026-04-13T17:34:58.36Z" },
+    { url = "https://files.pythonhosted.org/packages/da/71/deb6637475253b83eb4577c058863eec4b4ddaef2d08dcd93981b1aa4209/litellm-1.88.0-py3-none-any.whl", hash = "sha256:abd3037e0bf5703f833f5565c87bdfd93578d3de46cbcb36dfa108c3ef58021c", size = 15276203, upload-time = "2026-06-06T23:25:07.257Z" },
 ]
 
 [[package]]
@@ -2035,7 +2035,7 @@ wheels = [
 
 [[package]]
 name = "strix-agent"
-version = "1.0.2"
+version = "1.0.3"
 source = { editable = "." }
 dependencies = [
     { name = "caido-sdk-client" },