feat: add model id on token usage

application/alembic/versions/0015_token_usage_model_id.py

@@ -0,0 +1,34 @@
+"""0015 token_usage model_id — record which model each call ran under.
+
+Adds ``token_usage.model_id`` (canonical id: catalog name for built-ins,
+UUID for BYOM) so analytics can group spend by model. The partial index
+mirrors ``token_usage_request_id_idx`` — it excludes the NULL rows that
+pre-date the column.
+
+Revision ID: 0015_token_usage_model_id
+Revises: 0014_device_token_hash_index
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+
+revision: str = "0015_token_usage_model_id"
+down_revision: Union[str, None] = "0014_device_token_hash_index"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.execute("ALTER TABLE token_usage ADD COLUMN model_id TEXT;")
+    op.execute(
+        'CREATE INDEX token_usage_model_ts_idx '
+        'ON token_usage (model_id, "timestamp" DESC) '
+        "WHERE model_id IS NOT NULL;"
+    )
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS token_usage_model_ts_idx;")
+    op.execute("ALTER TABLE token_usage DROP COLUMN IF EXISTS model_id;")

application/api/user/scheduler_worker.py

@@ -349,6 +349,7 @@ def execute_scheduled_run_body(run_id: str, celery_task_id: Optional[str]) -> Di
                     agent_id=str(agent_id_raw) if agent_id_raw else None,
                     source="schedule",
                     request_id=str(run_id),
+                    model_id=outcome.get("model_id"),
                 )
             except Exception:
                 logger.exception(

application/llm/llm_creator.py

@@ -111,7 +111,7 @@ def create_llm(
 
         # Forward model_user_id so backup/fallback resolves under the
         # owner's scope on shared-agent dispatch.
-        return plugin.llm_class(
+        llm = plugin.llm_class(
             api_key,
             user_api_key,
             decoded_token=decoded_token,
@@ -124,3 +124,7 @@ def create_llm(
             *args,
             **kwargs,
         )
+        # llm.model_id is the upstream name (BYOM resolves it above); stamp
+        # the canonical id (UUID for BYOM) separately for token_usage.
+        llm._canonical_model_id = model_id
+        return llm

application/storage/db/models.py

@@ -103,6 +103,9 @@
     # N rows) count as a single request via DISTINCT in the repository
     # query. NULL on side-channel sources by design.
     Column("request_id", Text),
+    # Added in ``0015_token_usage_model_id``. Canonical model id (catalog
+    # name for built-ins, UUID for BYOM); NULL on un-backfilled rows.
+    Column("model_id", Text),
 )
 
 user_logs_table = Table(

application/storage/db/repositories/token_usage.py

@@ -33,6 +33,7 @@ def insert(
         generated_tokens: int = 0,
         source: str = "agent_stream",
         request_id: Optional[str] = None,
+        model_id: Optional[str] = None,
         timestamp: Optional[datetime] = None,
     ) -> None:
         # Attribution guard: the ``token_usage_attribution_chk`` CHECK
@@ -59,13 +60,13 @@ def insert(
                 INSERT INTO token_usage (
                     user_id, api_key, agent_id,
                     prompt_tokens, generated_tokens,
-                    source, request_id, timestamp
+                    source, request_id, model_id, timestamp
                 )
                 VALUES (
                     :user_id, :api_key,
                     CAST(:agent_id AS uuid),
                     :prompt_tokens, :generated_tokens,
-                    :source, :request_id, COALESCE(:timestamp, now())
+                    :source, :request_id, :model_id, COALESCE(:timestamp, now())
                 )
                 """
             ),
@@ -77,6 +78,7 @@ def insert(
                 "generated_tokens": generated_tokens,
                 "source": source,
                 "request_id": request_id,
+                "model_id": model_id,
                 "timestamp": timestamp,
             },
         )

application/usage.py

@@ -134,6 +134,7 @@ def _persist_call_usage(llm, call_usage):
                     getattr(llm, "_token_usage_source", None) or "agent_stream"
                 ),
                 request_id=getattr(llm, "_request_id", None),
+                model_id=getattr(llm, "_canonical_model_id", None),
             )
     except Exception:
         logger.exception("token_usage persist failed")

scripts/db/backfill_token_usage_model_id.py

@@ -0,0 +1,174 @@
+"""Backfill ``token_usage.model_id`` for rows written before the column.
+
+New rows get ``model_id`` stamped at write time (see
+``application.llm.llm_creator`` / ``application.usage``). This script
+fills the historical NULLs by deriving the model from data we already
+trust, in priority order. A row is only ever filled by the
+highest-priority tier that matches it; tiers run in one transaction so
+each later tier sees only the rows still NULL.
+
+Tiers (both touch only ``source='agent_stream'`` rows)
+-----
+1. ``request_id`` join (high confidence). The route stamps the same
+   ``request_id`` on the token_usage row and the assistant message, so
+   ``conversation_messages.model_id`` is authoritative for the call.
+2. ``agent_id`` + nearest message (medium confidence). For primary rows
+   with no usable ``request_id`` (legacy), copy ``model_id`` from the
+   closest-in-time message of any conversation belonging to the same
+   agent, within ``--window-minutes`` (ties broken toward the later
+   message so re-runs are reproducible).
+
+Side-channel rows (``fallback`` / ``compression`` / ``title`` /
+``rag_condense`` / ``schedule``) are left NULL: they share the primary
+turn's ``request_id`` or agent but often ran a *different* model (a
+backup, a compression override), so copying the primary turn's model
+onto them would mis-attribute spend. New rows already get the correct
+per-call model stamped at write time, so this only concerns history.
+
+Rows that match neither tier are left NULL on purpose — the partial
+index ``token_usage_model_ts_idx`` excludes them, and a model we can't
+tie to the specific call (e.g. the agent's configured default) would
+poison the analytics it feeds.
+
+Both ``model_id`` columns store the canonical id (catalog name for
+built-ins, UUID for BYOM), so BYOM rows backfill to the UUID unchanged.
+
+Usage::
+
+    # Dry-run (default): runs the fills in a rolled-back transaction and
+    # reports exactly how many rows each tier would touch.
+    python scripts/db/backfill_token_usage_model_id.py
+
+    # Commit the backfill.
+    python scripts/db/backfill_token_usage_model_id.py --apply
+
+    # Widen the tier-2 match window (default 5 minutes).
+    python scripts/db/backfill_token_usage_model_id.py --window-minutes 10 --apply
+
+Exit codes:
+    0 — success (dry-run or apply)
+    1 — bad arguments
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+from sqlalchemy import text  # noqa: E402
+
+from application.storage.db.engine import get_engine  # noqa: E402
+
+
+# Tier 1: same request -> same model, primary (agent_stream) rows only.
+# conversation_messages.model_id is authoritative for that turn; fallback
+# / compression rows share the request_id but ran a different model.
+_TIER1 = text(
+    """
+    UPDATE token_usage tu
+    SET model_id = cm.model_id
+    FROM conversation_messages cm
+    WHERE cm.request_id = tu.request_id
+      AND cm.model_id IS NOT NULL
+      AND tu.model_id IS NULL
+      AND tu.request_id IS NOT NULL
+      AND tu.source = 'agent_stream'
+    """
+)
+
+# Tier 2: nearest message of the same agent within the window, primary
+# (agent_stream) rows only. The EXISTS mirror skips rows with no match
+# (else the subquery would set NULL); the ORDER BY tiebreak (later message
+# wins) keeps the pick reproducible across re-runs.
+_TIER2 = text(
+    """
+    UPDATE token_usage tu
+    SET model_id = (
+        SELECT cm.model_id
+        FROM conversation_messages cm
+        JOIN conversations c ON c.id = cm.conversation_id
+        WHERE c.agent_id = tu.agent_id
+          AND cm.model_id IS NOT NULL
+          AND cm.timestamp BETWEEN tu.timestamp - make_interval(mins => :win)
+                               AND tu.timestamp + make_interval(mins => :win)
+        ORDER BY abs(extract(epoch FROM (cm.timestamp - tu.timestamp))), cm.timestamp DESC
+        LIMIT 1
+    )
+    WHERE tu.model_id IS NULL
+      AND tu.agent_id IS NOT NULL
+      AND tu.source = 'agent_stream'
+      AND EXISTS (
+        SELECT 1
+        FROM conversation_messages cm
+        JOIN conversations c ON c.id = cm.conversation_id
+        WHERE c.agent_id = tu.agent_id
+          AND cm.model_id IS NOT NULL
+          AND cm.timestamp BETWEEN tu.timestamp - make_interval(mins => :win)
+                               AND tu.timestamp + make_interval(mins => :win)
+      )
+    """
+)
+
+_COUNT_NULL = text("SELECT count(*) FROM token_usage WHERE model_id IS NULL")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Backfill token_usage.model_id from existing data.",
+    )
+    parser.add_argument(
+        "--apply",
+        action="store_true",
+        help="Commit the backfill. Default is a rolled-back dry-run.",
+    )
+    parser.add_argument(
+        "--window-minutes",
+        type=int,
+        default=5,
+        metavar="N",
+        help="Tier-2 nearest-message match window, in minutes (default 5).",
+    )
+    args = parser.parse_args()
+
+    if args.window_minutes < 0:
+        print("--window-minutes must be >= 0", file=sys.stderr)
+        return 1
+
+    engine = get_engine()
+    with engine.connect() as conn:
+        trans = conn.begin()
+        try:
+            # A one-shot maintenance UPDATE can run well past the engine's
+            # 30s per-statement guardrail; lift it for this transaction.
+            conn.execute(text("SET LOCAL statement_timeout = 0"))
+
+            before = conn.execute(_COUNT_NULL).scalar_one()
+
+            t1 = conn.execute(_TIER1).rowcount or 0
+            t2 = conn.execute(_TIER2, {"win": args.window_minutes}).rowcount or 0
+
+            after = conn.execute(_COUNT_NULL).scalar_one()
+
+            print(f"NULL model_id rows before:         {before}")
+            print(f"  tier 1 (request_id):             {t1}")
+            print(f"  tier 2 (agent + nearest msg):    {t2}")
+            print(f"NULL model_id rows remaining:      {after}")
+
+            if args.apply:
+                trans.commit()
+                print("\nCommitted.")
+            else:
+                trans.rollback()
+                print("\nDry run — rolled back. Re-run with --apply to commit.")
+        except Exception:
+            trans.rollback()
+            raise
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tests/storage/db/repositories/test_token_usage.py

@@ -5,6 +5,7 @@
 from datetime import datetime, timedelta, timezone
 
 import pytest
+from sqlalchemy import text
 
 from application.storage.db.repositories.token_usage import TokenUsageRepository
 
@@ -35,6 +36,33 @@ def test_insert_with_api_key(self, pg_conn):
         assert total == 30
 
 
+class TestModelId:
+    def _latest_model_id(self, conn, user_id):
+        return conn.execute(
+            text(
+                "SELECT model_id FROM token_usage "
+                "WHERE user_id = :u ORDER BY id DESC LIMIT 1"
+            ),
+            {"u": user_id},
+        ).scalar_one()
+
+    def test_persists_model_id(self, pg_conn):
+        repo = _repo(pg_conn)
+        repo.insert(user_id="u-model", prompt_tokens=1, generated_tokens=1, model_id="gpt-4o")
+        assert self._latest_model_id(pg_conn, "u-model") == "gpt-4o"
+
+    def test_persists_byom_uuid_model_id(self, pg_conn):
+        repo = _repo(pg_conn)
+        uuid_id = "11111111-1111-1111-1111-111111111111"
+        repo.insert(user_id="u-byom", prompt_tokens=1, generated_tokens=1, model_id=uuid_id)
+        assert self._latest_model_id(pg_conn, "u-byom") == uuid_id
+
+    def test_model_id_defaults_to_null(self, pg_conn):
+        repo = _repo(pg_conn)
+        repo.insert(user_id="u-nomodel", prompt_tokens=1, generated_tokens=1)
+        assert self._latest_model_id(pg_conn, "u-nomodel") is None
+
+
 class TestSumTokensInRange:
     def test_sums_correctly(self, pg_conn):
         repo = _repo(pg_conn)