Merge origin/main into devin/1785721011-security-process-tools

Both conflicts were additive collisions: an import block that gained a line on
each side, and two test suites appending cases at the same point in the file.

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

@@ -300,6 +300,16 @@ def test_make_model_settings_forces_required_for_anyllm_routed_openai_model() ->
     assert settings.tool_choice == "required"
 
 
+def test_make_model_settings_disables_parallel_tool_calls_by_default() -> None:
+    assert make_model_settings("none", model_name="gpt-4o").parallel_tool_calls is False
+
+
+def test_make_model_settings_omits_parallel_tool_calls_without_tools() -> None:
+    settings = make_model_settings("none", model_name="gpt-4o", has_tools=False)
+
+    assert settings.parallel_tool_calls is None
+
+
 def test_make_model_settings_sets_request_timeout() -> None:
     settings = make_model_settings(
         "none",
@@ -381,3 +391,32 @@ def test_scan_targets_drop_empty_and_duplicate_entries() -> None:
     }
 
     assert build_scan_targets(config) == ["https://app.example.com"]
+
+
+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()