Log every model call's time and tokens

Each call through BudgetedClient emits llm.call: seconds, input,
output and cached tokens, and the answering model when a fallback
answered; a failed call logs how long it ran before it failed.
Usage now carries the server's cached-prompt count on both routes.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01J5Utyfzw7gF7UhjRDEtjTk

nisse-core/src/nisse_core/models/context.py

@@ -31,6 +31,7 @@ dangerous direction.
 """
 
 import json
+import time
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass, replace
 from typing import Any
@@ -297,6 +298,31 @@ class BudgetedClient:
     def last_rate_limit_wait_s(self) -> float:
         return float(getattr(self._inner, "last_rate_limit_wait_s", 0.0) or 0.0)
 
+    def _log_call(self, model: str, started: float, error: str | None = None) -> None:
+        """One event per model call: how long it took and what it used, so a
+        slow turn shows whether reading or writing took the time."""
+        if self._oplog is None:
+            return
+        fields: dict[str, Any] = {
+            "model": model,
+            "seconds": round(time.monotonic() - started, 1),
+        }
+        if error is not None:
+            self._oplog.emit("llm.call", cause="failed", error=error, **fields)
+            return
+        usage = self.last_usage
+        answered = self.last_model
+        if answered and answered != model:
+            fields["answered_by"] = answered
+        self._oplog.emit(
+            "llm.call",
+            cause="answered",
+            input_tokens=int(usage.get("input", 0)),
+            output_tokens=int(usage.get("output", 0)),
+            cached_tokens=int(usage.get("cached", 0)),
+            **fields,
+        )
+
     def budget_for(self, model: str) -> Budget:
         window, model_max_output = self._limits_for(model)
         ceiling = self._max_output_tokens
@@ -360,17 +386,23 @@ class BudgetedClient:
                 )
         if require_tool_call is None:
             require_tool_call = bool(tools) and self._enforces_tool_choice(model)
-        produced = self._inner.complete(
-            fitted,
-            tools,
-            model,
-            system=system,
-            max_tokens=budget.max_output,
-            # Every caller that offers tools needs one called: the executor
-            # finishes with finish, the planner with submit_plan or
-            # ask_human, a delegation with hand_back.
-            require_tool_call=require_tool_call,
-        )
+        started = time.monotonic()
+        try:
+            produced = self._inner.complete(
+                fitted,
+                tools,
+                model,
+                system=system,
+                max_tokens=budget.max_output,
+                # Every caller that offers tools needs one called: the executor
+                # finishes with finish, the planner with submit_plan or
+                # ask_human, a delegation with hand_back.
+                require_tool_call=require_tool_call,
+            )
+        except Exception as e:
+            self._log_call(model, started, error=type(e).__name__)
+            raise
+        self._log_call(model, started)
         reported = int(self.last_usage.get("input", 0))
         if reported > 0:
             estimated = estimate_tokens(fitted, system, tools)

nisse-core/src/nisse_core/models/llm.py

@@ -244,6 +244,10 @@ class LiteLLMClient:
         self.last_usage = {
             "input": int(usage.get("prompt_tokens") or 0),
             "output": int(usage.get("completion_tokens") or 0),
+            # Prompt tokens the server reused from its cache, when it says.
+            "cached": int(
+                (usage.get("prompt_tokens_details") or {}).get("cached_tokens") or 0
+            ),
         }
         out: list[CanonicalEvent] = []
         if message.get("content"):
@@ -371,6 +375,7 @@ class AnthropicPassthroughClient:
         self.last_usage = {
             "input": int(usage.get("input_tokens") or 0),
             "output": int(usage.get("output_tokens") or 0),
+            "cached": int(usage.get("cache_read_input_tokens") or 0),
         }
         out: list[CanonicalEvent] = []
         for block in content:

nisse-core/tests/test_context.py

@@ -1,6 +1,7 @@
 """The context budget (architecture L6)."""
 
 import io
+import json
 from pathlib import Path
 from typing import Any
 
@@ -25,6 +26,7 @@ from nisse_core.models.context import (
     fit,
     head_and_tail,
 )
+from nisse_core.models.llm import LLMError
 from nisse_core.oplog import OpLog
 from nisse_core.state.store import StateStore
 
@@ -190,7 +192,7 @@ def test_reported_usage_only_ever_raises_the_estimate() -> None:
     assert under_then_over._factors["m"] == 2.0  # never loosened
 
 
-def test_a_request_that_fits_is_sent_whole_and_nothing_is_logged() -> None:
+def test_a_request_that_fits_is_sent_whole_and_no_cut_is_logged() -> None:
     stream = io.StringIO()
     client = BudgetedClient(
         RecordingClient(), lambda m: (100_000, None), oplog=OpLog("t", stream)
@@ -198,7 +200,49 @@ def test_a_request_that_fits_is_sent_whole_and_nothing_is_logged() -> None:
     events = transcript(3, 100)
     client.complete(events, None, "m")
     assert client.last_trim is None
-    assert stream.getvalue() == ""
+    assert "context.trimmed" not in stream.getvalue()
+
+
+def logged(stream: io.StringIO, event: str) -> list[dict[str, Any]]:
+    records = [json.loads(line) for line in stream.getvalue().splitlines()]
+    return [r for r in records if r["event"] == event]
+
+
+def test_every_call_logs_its_time_and_tokens() -> None:
+    # A slow turn should show whether reading or writing took the time (#174).
+    stream = io.StringIO()
+    inner = RecordingClient(reported_input=900)
+    inner.last_usage["cached"] = 800
+    inner.last_model = "fallback-model"
+    client = BudgetedClient(inner, lambda m: (100_000, None), oplog=OpLog("t", stream))
+    client.complete(transcript(1, 10), None, "m")
+    [call] = logged(stream, "llm.call")
+    assert call["cause"] == "answered"
+    assert call["model"] == "m"
+    assert call["answered_by"] == "fallback-model"
+    assert (call["input_tokens"], call["output_tokens"], call["cached_tokens"]) == (
+        900,
+        5,
+        800,
+    )
+    assert isinstance(call["seconds"], float)
+
+
+def test_a_failed_call_logs_how_long_it_ran_and_still_raises() -> None:
+    class TimesOut(RecordingClient):
+        def complete(self, *args, **kwargs):
+            raise LLMError("LLM request returned 408: timed out")
+
+    stream = io.StringIO()
+    client = BudgetedClient(
+        TimesOut(), lambda m: (100_000, None), oplog=OpLog("t", stream)
+    )
+    with pytest.raises(LLMError):
+        client.complete(transcript(1, 10), None, "m")
+    [call] = logged(stream, "llm.call")
+    assert call["cause"] == "failed"
+    assert call["error"] == "LLMError"
+    assert "input_tokens" not in call
 
 
 def test_a_cut_is_recorded_and_logged() -> None:

nisse-core/tests/test_llm.py

@@ -484,6 +484,48 @@ def test_the_answering_model_is_recorded() -> None:
     assert client.last_model == "gpt-test-1"
 
 
+def test_usage_includes_what_the_server_reused_from_its_cache() -> None:
+    client = make_client(
+        lambda request: httpx.Response(
+            200,
+            json={
+                "model": "qwen",
+                "choices": [{"message": {"role": "assistant", "content": "hi"}}],
+                "usage": {
+                    "prompt_tokens": 900,
+                    "completion_tokens": 40,
+                    "prompt_tokens_details": {"cached_tokens": 800},
+                },
+            },
+        )
+    )
+    client.complete(EVENTS, tools=None, model="qwen")
+    assert client.last_usage == {"input": 900, "output": 40, "cached": 800}
+
+    passthrough = AnthropicPassthroughClient(
+        "https://litellm.test",
+        "tok",
+        transport=httpx.MockTransport(
+            lambda request: httpx.Response(
+                200,
+                json={
+                    "model": "claude-opus-5-5",
+                    "stop_reason": "end_turn",
+                    "content": [{"type": "text", "text": "hi"}],
+                    "usage": {
+                        "input_tokens": 50,
+                        "output_tokens": 7,
+                        "cache_read_input_tokens": 30,
+                    },
+                },
+            )
+        ),
+        sleep=lambda _s: None,
+    )
+    passthrough.complete(EVENTS, tools=None, model="claude-opus-5-5")
+    assert passthrough.last_usage == {"input": 50, "output": 7, "cached": 30}
+
+
 # --- outages delay, never fail (L5) --------------------------------------------