fix: use valid Renovate Docker managers

containers/Dockerfile

@@ -118,6 +118,7 @@ ENV AGENT_BROWSER_USER_AGENT="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
 ENV AGENT_BROWSER_ARGS="--disable-blink-features=AutomationControlled,--no-first-run,--no-default-browser-check,--lang=en-US"
 ENV AGENT_BROWSER_SCREENSHOT_DIR=/workspace/.agent-browser-screenshots
 ENV AGENT_BROWSER_IDLE_TIMEOUT_MS=180000
+USER root
 RUN set -eu; \
     { \
         for var in AGENT_BROWSER_EXECUTABLE_PATH AGENT_BROWSER_USER_AGENT \
@@ -130,6 +131,7 @@ RUN set -eu; \
     install -m 0644 /tmp/agent-browser.sh /etc/profile.d/agent-browser.sh; \
     rm /tmp/agent-browser.sh; \
     env -i bash -lc 'test "${AGENT_BROWSER_IDLE_TIMEOUT_MS}" = "180000"'
+USER pentester
 RUN /home/pentester/.npm-global/bin/agent-browser doctor --offline --quick
 
 RUN set -eux; \

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "strix-agent"
-version = "1.5.2"
+version = "1.5.3"
 description = "Open-source AI Hackers for your apps"
 readme = "README.md"
 license = "Apache-2.0"

renovate.json

@@ -0,0 +1,30 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "extends": [
+    "config:recommended"
+  ],
+  "minimumReleaseAge": "7 days",
+  "internalChecksFilter": "strict",
+  "enabledManagers": [
+    "npm",
+    "github-actions",
+    "dockerfile",
+    "pep621"
+  ],
+  "packageRules": [
+    {
+      "groupName": "non-major dependencies",
+      "matchUpdateTypes": [
+        "minor",
+        "patch"
+      ]
+    },
+    {
+      "matchManagers": [
+        "github-actions",
+        "dockerfile"
+      ],
+      "pinDigests": true
+    }
+  ]
+}

strix/config/models.py

@@ -652,27 +652,31 @@ def _install_openrouter_stream_cost_capture() -> None:
     litellm.OpenrouterConfig = _StrixOpenrouterConfig  # type: ignore[misc]
 
 
-_OPENROUTER_ATTRIBUTION_HEADERS = {
+OPENROUTER_ATTRIBUTION_HEADERS = {
     "HTTP-Referer": "https://strix.ai",
     "X-Title": "Strix",
     "X-OpenRouter-Categories": "cli-agent",
 }
 
 
+def is_openrouter_model(model_name: str | None) -> bool:
+    return bool(model_name) and "openrouter/" in (model_name or "").strip().lower()
+
+
 def _configure_openrouter_attribution(model_name: str | None) -> None:
     import litellm
 
     current: object = litellm.headers
     existing: dict[str, str] = current if isinstance(current, dict) else {}
-    if not model_name or "openrouter/" not in model_name.strip().lower():
-        if any(key in existing for key in _OPENROUTER_ATTRIBUTION_HEADERS):
+    if not is_openrouter_model(model_name):
+        if any(key in existing for key in OPENROUTER_ATTRIBUTION_HEADERS):
             remaining = {
-                k: v for k, v in existing.items() if k not in _OPENROUTER_ATTRIBUTION_HEADERS
+                k: v for k, v in existing.items() if k not in OPENROUTER_ATTRIBUTION_HEADERS
             }
             litellm.headers = remaining or None  # type: ignore[assignment]
         return
 
-    litellm.headers = {**existing, **_OPENROUTER_ATTRIBUTION_HEADERS}  # type: ignore[assignment]
+    litellm.headers = {**existing, **OPENROUTER_ATTRIBUTION_HEADERS}  # type: ignore[assignment]
 
 
 def _configure_extra_headers(llm: LlmSettings) -> None:

strix/core/inputs.py

@@ -10,10 +10,12 @@ from openai.types.shared import Reasoning
 
 from strix.config.models import (
     DEFAULT_MODEL_RETRY,
+    OPENROUTER_ATTRIBUTION_HEADERS,
     bedrock_route_supports_prompt_caching,
     is_bedrock_route,
     is_claude_model,
     is_known_openai_bare_model,
+    is_openrouter_model,
     model_supports_reasoning,
     request_timeout_extra_args,
 )
@@ -203,12 +205,13 @@ def make_model_settings(
     extra_headers: dict[str, str] | None = None,
     has_tools: bool = True,
 ) -> ModelSettings:
+    headers = _request_headers(model_name, extra_headers)
     model_settings = ModelSettings(
         parallel_tool_calls=False if has_tools else None,
         retry=DEFAULT_MODEL_RETRY,
         include_usage=True,
         extra_args=request_timeout_extra_args(request_timeout),
-        extra_headers=dict(extra_headers) if extra_headers else None,
+        extra_headers=headers,
     )
     if (
         reasoning_effort is not None
@@ -231,6 +234,17 @@ def make_model_settings(
     return model_settings
 
 
+def _request_headers(
+    model_name: str, extra_headers: dict[str, str] | None
+) -> dict[str, str] | None:
+    headers: dict[str, str] = {}
+    if is_openrouter_model(model_name):
+        headers.update(OPENROUTER_ATTRIBUTION_HEADERS)
+    if extra_headers:
+        headers.update(extra_headers)
+    return headers or None
+
+
 def _reasoning_settings(
     effort: ReasoningEffort,
     extra_args: dict[str, Any] | None,

strix/report/pricing.py

@@ -0,0 +1,54 @@
+"""LiteLLM model-name resolution for local cost estimates."""
+
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import Any, cast
+
+
+@lru_cache(maxsize=512)
+def resolve_litellm_model(model: str) -> str | None:
+    """Return a provider-qualified model name that LiteLLM can price."""
+    try:
+        import litellm
+
+        normalized = model.strip()
+        for prefix in ("litellm/", "any-llm/", "openai/"):
+            if normalized.startswith(prefix):
+                normalized = normalized.removeprefix(prefix)
+                break
+        if not normalized:
+            return None
+
+        model_cost = cast(
+            "dict[str, dict[str, Any]]",
+            getattr(litellm, "model_cost"),  # noqa: B009
+        )
+        bare_entry = model_cost.get(normalized)
+        if "/" not in normalized and isinstance(bare_entry, dict):
+            provider = bare_entry.get("litellm_provider")
+            if isinstance(provider, str) and provider:
+                return f"{provider}/{normalized}"
+        if "/" in normalized and isinstance(bare_entry, dict):
+            return normalized
+
+        names = [normalized]
+        if "/" in normalized:
+            names.append(normalized.rsplit("/", 1)[-1])
+        for name in names:
+            matches = sorted(key for key in model_cost if key.endswith(f"/{name}"))
+            if not matches:
+                continue
+            prices = {
+                (
+                    model_cost[key].get("input_cost_per_token"),
+                    model_cost[key].get("output_cost_per_token"),
+                )
+                for key in matches
+                if isinstance(model_cost.get(key), dict)
+            }
+            if len(matches) == 1 or len(prices) == 1:
+                return matches[0]
+        return None  # noqa: TRY300
+    except Exception:  # noqa: BLE001
+        return None

strix/report/state.py

@@ -14,6 +14,7 @@ from agents.usage import Usage
 from strix.config import codex
 from strix.config.loader import load_settings
 from strix.core.paths import run_dir_for
+from strix.report.pricing import resolve_litellm_model
 from strix.report.sarif import write_sarif
 from strix.report.usage import LLMUsageLedger
 from strix.report.writer import (
@@ -696,10 +697,13 @@ def _estimate_response_cost(kwargs: Any, completion_response: Any) -> float | No
         candidates.append(model.rsplit("/", 1)[-1])
 
     for candidate in candidates:
+        resolved = resolve_litellm_model(candidate)
+        if not resolved:
+            continue
         try:
             value = completion_cost(
-                completion_response={"model": candidate, "usage": usage_payload},
-                model=candidate,
+                completion_response={"model": resolved, "usage": usage_payload},
+                model=resolved,
             )
         except Exception:  # nosec B112  # noqa: BLE001, S112
             continue

strix/report/usage.py

@@ -7,6 +7,8 @@ from typing import Any
 
 from agents.usage import Usage, deserialize_usage, serialize_usage
 
+from strix.report.pricing import resolve_litellm_model
+
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +20,9 @@ class LLMUsageLedger:
         self._total_usage = Usage()
         self._agent_usage: dict[str, Usage] = {}
         self._agent_metadata: dict[str, dict[str, str]] = {}
-        self._total_cost = 0.0
+        self._observed_cost = 0.0
+        self._estimated_cost = 0.0
+        self._has_observed_cost = False
         # When True, tokens are still tracked but cost stays $0 — the run is on a
         # model subscription, so there is no metered per-token charge to report.
         self.zero_cost = False
@@ -44,10 +48,10 @@ class LLMUsageLedger:
         if model:
             metadata["model"] = model
 
-        if not self.zero_cost and not _is_litellm_routed(model):
+        if not self.zero_cost:
             estimated = _estimate_litellm_cost(usage, model)
             if estimated:
-                self._total_cost += estimated
+                self._estimated_cost += estimated
 
         return True
 
@@ -55,15 +59,18 @@ class LLMUsageLedger:
         if self.zero_cost:
             return
         if isinstance(cost, int | float) and cost > 0:
-            self._total_cost += float(cost)
+            self._observed_cost += float(cost)
+            self._has_observed_cost = True
 
     @property
     def total_cost(self) -> float:
-        return _round_cost(self._total_cost)
+        if self.zero_cost:
+            return 0.0
+        return _round_cost(self._observed_cost if self._has_observed_cost else self._estimated_cost)
 
     def to_record(self) -> dict[str, Any]:
         record = serialize_usage(self._total_usage)
-        record["cost"] = _round_cost(self._total_cost)
+        record["cost"] = self.total_cost
         record["agents"] = []
 
         agent_tokens = {aid: _resolve_total_tokens(u) for aid, u in self._agent_usage.items()}
@@ -72,7 +79,7 @@ class LLMUsageLedger:
             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
+                self.total_cost * (agent_tokens[agent_id] / total_tokens) if total_tokens else 0.0
             )
 
             agent_record = serialize_usage(usage)
@@ -92,7 +99,9 @@ class LLMUsageLedger:
         self._total_usage = Usage()
         self._agent_usage.clear()
         self._agent_metadata.clear()
-        self._total_cost = 0.0
+        self._observed_cost = 0.0
+        self._estimated_cost = 0.0
+        self._has_observed_cost = False
 
         if not isinstance(raw_usage, dict):
             return
@@ -103,7 +112,9 @@ class LLMUsageLedger:
             logger.exception("Failed to hydrate aggregate llm_usage from run.json")
             self._total_usage = Usage()
 
-        self._total_cost = _float_or_zero(raw_usage.get("cost"))
+        persisted_cost = _float_or_zero(raw_usage.get("cost"))
+        self._observed_cost = persisted_cost
+        self._estimated_cost = persisted_cost
 
         for raw_agent in raw_usage.get("agents") or []:
             if not isinstance(raw_agent, dict):
@@ -136,15 +147,6 @@ def _resolve_total_tokens(usage: Usage) -> int:
     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:
     return bool(
         usage.requests
@@ -201,24 +203,23 @@ def _estimate_litellm_entry_cost(entry: Any, model: str) -> float | None:
 
     candidates = [model]
     if "/" in model:
-        candidates.append(model.split("/", 1)[-1])
+        candidates.append(model.rsplit("/", 1)[-1])
 
-    cost: Any = None
     for candidate in candidates:
+        resolved = resolve_litellm_model(candidate)
+        if not resolved:
+            continue
         try:
             cost = completion_cost(
-                completion_response={"model": candidate, "usage": usage_payload},
-                model=model,
+                completion_response={"model": resolved, "usage": usage_payload},
+                model=resolved,
             )
-            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
+        if cost > 0:
+            return float(cost)
+    logger.debug("LiteLLM cost estimate unavailable for model %s", model)
+    return None
 
 
 def _litellm_model_name(model: str | None) -> str | None:

tests/test_cost_tracking.py

@@ -143,7 +143,7 @@ def test_cost_callback_estimates_cost_with_bare_model_fallback() -> None:
     }
 
     def fake_completion_cost(**kwargs: object) -> float:
-        if kwargs["model"] == "gpt-4o-mini":
+        if kwargs["model"] == "openai/gpt-4o-mini":
             return 0.025
         raise ValueError(kwargs["model"])
 

tests/test_inputs.py

@@ -361,3 +361,32 @@ def test_make_model_settings_timeout_survives_reasoning_resolve() -> None:
 
     assert settings.extra_args is not None
     assert settings.extra_args["timeout"] == 120.0
+
+
+def test_openrouter_attribution_rides_on_the_request_headers() -> None:
+    # litellm.headers is ignored once a request carries any header of its own,
+    # so the attribution must be part of the per-request headers.
+    headers = make_model_settings(
+        None, model_name="openrouter/anthropic/claude-sonnet-4-5"
+    ).extra_headers
+    assert headers == {
+        "HTTP-Referer": "https://strix.ai",
+        "X-Title": "Strix",
+        "X-OpenRouter-Categories": "cli-agent",
+    }
+
+
+def test_openrouter_attribution_absent_for_other_providers() -> None:
+    assert make_model_settings(None, model_name="anthropic/claude-sonnet-4-5").extra_headers is None
+
+
+def test_user_headers_override_openrouter_attribution() -> None:
+    headers = make_model_settings(
+        None,
+        model_name="openrouter/anthropic/claude-sonnet-4-5",
+        extra_headers={"X-Title": "Custom", "X-Tenant": "acme"},
+    ).extra_headers
+    assert headers is not None
+    assert headers["X-Title"] == "Custom"
+    assert headers["X-Tenant"] == "acme"
+    assert headers["HTTP-Referer"] == "https://strix.ai"

tests/test_pricing.py

@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import litellm
+from agents.usage import Usage
+
+from strix.report.pricing import resolve_litellm_model
+from strix.report.usage import LLMUsageLedger
+
+
+def test_resolves_common_bare_model_names() -> None:
+    resolve_litellm_model.cache_clear()
+    assert resolve_litellm_model("deepseek-v4-flash") == "deepseek/deepseek-v4-flash"
+    assert resolve_litellm_model("openai/deepseek-v4-flash") == "deepseek/deepseek-v4-flash"
+    assert resolve_litellm_model("grok-4.5") == "xai/grok-4.5"
+    assert resolve_litellm_model("MiniMax-M3") == "minimax/MiniMax-M3"
+
+
+def test_resolver_returns_none_for_unresolvable_model() -> None:
+    resolve_litellm_model.cache_clear()
+    assert resolve_litellm_model("provider/not-a-real-model") is None
+
+
+def test_ledger_uses_estimate_when_routed_provider_reports_no_cost() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+
+    with patch("litellm.completion_cost", return_value=0.42):
+        ledger.record(agent_id="a", usage=usage, model="openai/deepseek-v4-flash")
+
+    assert ledger.total_cost == 0.42
+
+
+def test_ledger_prefers_observed_cost_over_estimate() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+
+    with patch("litellm.completion_cost", return_value=0.42):
+        ledger.record(agent_id="a", usage=usage, model="openai/deepseek-v4-flash")
+    ledger.record_observed_cost(0.17)
+
+    assert ledger.total_cost == 0.17
+
+
+def test_hydrated_estimate_continues_accumulating_new_estimates() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+    ledger.hydrate({"cost": 0.42})
+
+    with patch("litellm.completion_cost", return_value=0.17):
+        ledger.record(agent_id="a", usage=usage, model="openai/deepseek-v4-flash")
+
+    assert ledger.total_cost == 0.59
+
+
+def test_zero_cost_disables_both_observed_and_estimated_costs() -> None:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.output_tokens = 200
+    usage.total_tokens = 1200
+    ledger = LLMUsageLedger()
+    ledger.zero_cost = True
+
+    with patch("litellm.completion_cost", return_value=0.42) as estimate:
+        ledger.record(agent_id="a", usage=usage, model="deepseek-v4-flash")
+        ledger.record_observed_cost(1.0)
+
+    estimate.assert_not_called()
+    assert ledger.total_cost == 0.0
+
+
+def test_resolver_uses_provider_when_bare_entry_has_one() -> None:
+    original = litellm.model_cost
+    litellm.model_cost = {
+        "example": {
+            "litellm_provider": "example-provider",
+            "input_cost_per_token": 1.0,
+            "output_cost_per_token": 2.0,
+        }
+    }
+    try:
+        resolve_litellm_model.cache_clear()
+        assert resolve_litellm_model("example") == "example-provider/example"
+    finally:
+        litellm.model_cost = original
+        resolve_litellm_model.cache_clear()
+
+
+def test_resolver_does_not_guess_between_differently_priced_providers() -> None:
+    original = litellm.model_cost
+    litellm.model_cost = {
+        "provider-a/example": {
+            "input_cost_per_token": 1.0,
+            "output_cost_per_token": 2.0,
+        },
+        "provider-b/example": {
+            "input_cost_per_token": 3.0,
+            "output_cost_per_token": 4.0,
+        },
+    }
+    try:
+        resolve_litellm_model.cache_clear()
+        assert resolve_litellm_model("example") is None
+    finally:
+        litellm.model_cost = original
+        resolve_litellm_model.cache_clear()

uv.lock

@@ -2378,7 +2378,7 @@ wheels = [
 
 [[package]]
 name = "strix-agent"
-version = "1.5.2"
+version = "1.5.3"
 source = { editable = "." }
 dependencies = [
     { name = "caido-sdk-client" },