feat(memory): add incremental transcript compaction

tests/unit/test_token_meter.py

@@ -0,0 +1,173 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for the M0 session token metering pipeline (pure recording)."""
+
+from unittest.mock import Mock
+from uuid import uuid4
+
+from langchain_core.messages import AIMessage
+
+from artemis.services.token_meter import (
+    SessionTokenMeter,
+    extract_usage,
+    get_meter,
+    record_llm_usage,
+)
+
+
+def _msg_with_usage(prompt=1000, completion=50, cached=0):
+    usage = {
+        "input_tokens": prompt,
+        "output_tokens": completion,
+        "total_tokens": prompt + completion,
+    }
+    if cached:
+        usage["input_token_details"] = {"cache_read": cached}
+    return AIMessage(content="ok", usage_metadata=usage)
+
+
+def test_extract_usage_from_usage_metadata():
+    usage = extract_usage(_msg_with_usage(prompt=1200, completion=80, cached=900))
+    assert usage == {
+        "prompt_tokens": 1200,
+        "completion_tokens": 80,
+        "total_tokens": 1280,
+        "cached_tokens": 900,
+    }
+
+
+def test_extract_usage_from_response_metadata_fallback():
+    msg = AIMessage(content="ok")
+    msg.response_metadata = {
+        "usage_metadata": {
+            "prompt_token_count": 500,
+            "candidates_token_count": 25,
+            "total_token_count": 525,
+            "cached_content_token_count": 400,
+        }
+    }
+    usage = extract_usage(msg)
+    assert usage == {
+        "prompt_tokens": 500,
+        "completion_tokens": 25,
+        "total_tokens": 525,
+        "cached_tokens": 400,
+    }
+
+
+def test_extract_usage_absent_returns_none():
+    assert extract_usage(AIMessage(content="no usage")) is None
+    assert extract_usage(object()) is None
+
+
+def test_meter_accumulates_session_totals():
+    meter = SessionTokenMeter("s1")
+    meter.record({"prompt_tokens": 100, "completion_tokens": 10, "cached_tokens": 0})
+    snapshot = meter.record(
+        {"prompt_tokens": 300, "completion_tokens": 20, "cached_tokens": 250}
+    )
+    assert snapshot == {
+        "session_llm_calls": 2,
+        "session_prompt_tokens": 400,
+        "session_completion_tokens": 30,
+        "session_cached_tokens": 250,
+        "session_cache_hit_calls": 1,
+    }
+    assert meter.last_prompt_tokens == 300
+
+
+def test_record_llm_usage_records_trace_with_context_base():
+    engine = Mock()
+    engine.current_session_id = uuid4()
+    engine.current_step_id = uuid4()
+
+    payload = record_llm_usage(
+        engine, _msg_with_usage(prompt=2000, completion=100, cached=1500), source="google:test"
+    )
+
+    assert payload is not None
+    assert payload["context_base_tokens"] == 2000
+    assert payload["cached_tokens"] == 1500
+    assert payload["source"] == "google:test"
+
+    engine.record_trace.assert_called_once()
+    kwargs = engine.record_trace.call_args.kwargs
+    assert kwargs["type"] == "llm_call"
+    assert kwargs["name"] == "llm_usage"
+    assert kwargs["step_id"] == engine.current_step_id
+    assert kwargs["payload"]["prompt_tokens"] == 2000
+
+
+def test_record_llm_usage_accumulates_across_calls_same_session():
+    engine = Mock()
+    engine.current_session_id = uuid4()
+    engine.current_step_id = None
+
+    record_llm_usage(engine, _msg_with_usage(prompt=100, completion=5))
+    payload = record_llm_usage(engine, _msg_with_usage(prompt=250, completion=10, cached=90))
+
+    assert payload["session_llm_calls"] == 2
+    assert payload["session_prompt_tokens"] == 350
+    assert payload["session_cached_tokens"] == 90
+    assert payload["session_cache_hit_calls"] == 1
+
+    meter = get_meter(engine.current_session_id)
+    assert meter.last_prompt_tokens == 250
+
+
+def test_record_llm_usage_never_raises_and_skips_gracefully():
+    # No engine
+    assert record_llm_usage(None, _msg_with_usage()) is None
+
+    # Engine without a session
+    engine = Mock()
+    engine.current_session_id = None
+    assert record_llm_usage(engine, _msg_with_usage()) is None
+    engine.record_trace.assert_not_called()
+
+    # Response without usage
+    engine2 = Mock()
+    engine2.current_session_id = uuid4()
+    assert record_llm_usage(engine2, AIMessage(content="nope")) is None
+    engine2.record_trace.assert_not_called()
+
+    # record_trace blowing up must not propagate
+    engine3 = Mock()
+    engine3.current_session_id = uuid4()
+    engine3.record_trace.side_effect = RuntimeError("boom")
+    assert record_llm_usage(engine3, _msg_with_usage()) is None
+
+
+def test_record_llm_usage_lens_calls_do_not_touch_last_prompt_tokens():
+    """Background lens calls accumulate totals but never overwrite the live
+    context base (last_prompt_tokens) consumed by the compaction thresholds."""
+    engine = Mock()
+    engine.current_session_id = uuid4()
+    engine.current_step_id = None
+
+    record_llm_usage(engine, _msg_with_usage(prompt=15000))
+    meter = get_meter(engine.current_session_id)
+    assert meter.last_prompt_tokens == 15000
+
+    payload = record_llm_usage(
+        engine,
+        _msg_with_usage(prompt=300, completion=40),
+        source="lens:visual_transition:test-model",
+        update_last_prompt=False,
+    )
+    assert payload["source"] == "lens:visual_transition:test-model"
+    assert meter.last_prompt_tokens == 15000  # untouched by the lens call
+    assert meter.llm_calls == 2
+    assert meter.prompt_tokens == 15300