fix(observability): restore historical continuity in the AI Usage and GPU Metrics dashboards

grafana/dashboards/ai-usage.json

@@ -291,7 +291,7 @@
       "datasource": { "type": "prometheus", "uid": "prometheus" },
       "fieldConfig": { "defaults": { "unit": "currencyUSD", "color": { "mode": "palette-classic" }, "custom": { "lineWidth": 2, "fillOpacity": 10 } } },
       "options": { "legend": { "showLegend": true, "placement": "bottom" }, "tooltip": { "mode": "multi" } },
-      "targets": [{ "refId": "A", "expr": "sum by (provider) (loopover_ai_cost_usd_total) or vector(0)", "legendFormat": "{{provider}}" }]
+      "targets": [{ "refId": "A", "expr": "sum by (provider) ((loopover_ai_cost_usd_total or gittensory_ai_cost_usd_total)) or vector(0)", "legendFormat": "{{provider}}" }]
     },
     {
       "id": 16,
@@ -304,7 +304,7 @@
       "targets": [
         {
           "refId": "A",
-          "expr": "sum by (provider) ((rate(loopover_ai_input_tokens_total[5m]) + rate(loopover_ai_output_tokens_total[5m])) * 60)",
+          "expr": "sum by (provider) (((rate(loopover_ai_input_tokens_total[5m]) or rate(gittensory_ai_input_tokens_total[5m])) + (rate(loopover_ai_output_tokens_total[5m]) or rate(gittensory_ai_output_tokens_total[5m]))) * 60)",
           "legendFormat": "{{provider}}"
         }
       ]
@@ -318,8 +318,8 @@
       "fieldConfig": { "defaults": { "unit": "short", "color": { "mode": "palette-classic" }, "custom": { "lineWidth": 2, "fillOpacity": 10 } } },
       "options": { "legend": { "showLegend": true, "placement": "bottom" }, "tooltip": { "mode": "multi" } },
       "targets": [
-        { "refId": "A", "expr": "sum by (model, effort) (increase(loopover_ai_requests_total[1h]))", "legendFormat": "{{model}} · {{effort}}" },
-        { "refId": "B", "expr": "sum by (primary, fallback) (increase(loopover_ai_review_model_fallback_total[1h]))", "legendFormat": "fallback {{primary}}→{{fallback}}" }
+        { "refId": "A", "expr": "sum by (model, effort) ((increase(loopover_ai_requests_total[1h]) or increase(gittensory_ai_requests_total[1h])))", "legendFormat": "{{model}} · {{effort}}" },
+        { "refId": "B", "expr": "sum by (primary, fallback) ((increase(loopover_ai_review_model_fallback_total[1h]) or increase(gittensory_ai_review_model_fallback_total[1h])))", "legendFormat": "fallback {{primary}}→{{fallback}}" }
       ]
     },
     {
@@ -331,8 +331,8 @@
       "fieldConfig": { "defaults": { "unit": "short", "color": { "mode": "palette-classic" }, "custom": { "lineWidth": 2, "fillOpacity": 10 } } },
       "options": { "legend": { "showLegend": true, "placement": "bottom" }, "tooltip": { "mode": "multi" } },
       "targets": [
-        { "refId": "A", "expr": "sum by (provider, kind) (loopover_ai_input_tokens_total)", "legendFormat": "{{provider}} {{kind}} in" },
-        { "refId": "B", "expr": "sum by (provider, kind) (loopover_ai_output_tokens_total)", "legendFormat": "{{provider}} {{kind}} out" }
+        { "refId": "A", "expr": "sum by (provider, kind) ((loopover_ai_input_tokens_total or gittensory_ai_input_tokens_total))", "legendFormat": "{{provider}} {{kind}} in" },
+        { "refId": "B", "expr": "sum by (provider, kind) ((loopover_ai_output_tokens_total or gittensory_ai_output_tokens_total))", "legendFormat": "{{provider}} {{kind}} out" }
       ]
     },
     {
@@ -347,7 +347,7 @@
       "targets": [
         {
           "refId": "A",
-          "expr": "sum by (model, effort) (increase(loopover_ai_requests_total{provider=\"codex\"}[$__rate_interval]))",
+          "expr": "sum by (model, effort) ((increase(loopover_ai_requests_total{provider=\"codex\"}[$__rate_interval]) or increase(gittensory_ai_requests_total{provider=\"codex\"}[$__rate_interval])))",
           "legendFormat": "{{model}} / {{effort}}"
         }
       ]
@@ -361,8 +361,8 @@
       "fieldConfig": { "defaults": { "unit": "short", "custom": { "drawStyle": "bars", "fillOpacity": 70, "lineWidth": 1, "stacking": { "mode": "normal" } } } },
       "options": { "legend": { "showLegend": true, "placement": "bottom" }, "tooltip": { "mode": "multi", "sort": "desc" } },
       "targets": [
-        { "refId": "A", "expr": "sum by (kind) (increase(loopover_ai_input_tokens_total{provider=\"codex\"}[$__rate_interval]))", "legendFormat": "input {{kind}}" },
-        { "refId": "B", "expr": "sum by (kind) (increase(loopover_ai_output_tokens_total{provider=\"codex\"}[$__rate_interval]))", "legendFormat": "output {{kind}}" }
+        { "refId": "A", "expr": "sum by (kind) ((increase(loopover_ai_input_tokens_total{provider=\"codex\"}[$__rate_interval]) or increase(gittensory_ai_input_tokens_total{provider=\"codex\"}[$__rate_interval])))", "legendFormat": "input {{kind}}" },
+        { "refId": "B", "expr": "sum by (kind) ((increase(loopover_ai_output_tokens_total{provider=\"codex\"}[$__rate_interval]) or increase(gittensory_ai_output_tokens_total{provider=\"codex\"}[$__rate_interval])))", "legendFormat": "output {{kind}}" }
       ]
     },
     {

grafana/dashboards/gpu-metrics.json

@@ -95,7 +95,7 @@
       "targets": [
         {
           "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" },
-          "expr": "sum by (provider, request_kind) (rate(loopover_ai_provider_request_duration_seconds_count[5m]))",
+          "expr": "sum by (provider, request_kind) ((rate(loopover_ai_provider_request_duration_seconds_count[5m]) or rate(gittensory_ai_provider_request_duration_seconds_count[5m])))",
           "legendFormat": "{{provider}} / {{request_kind}}",
           "refId": "A"
         }
@@ -110,9 +110,9 @@
       "title": "AI Request Latency (p50 / p95 / p99)",
       "type": "timeseries",
       "targets": [
-        { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.50, sum by (le) (rate(loopover_ai_provider_request_duration_seconds_bucket[5m])))", "legendFormat": "p50", "refId": "A" },
-        { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.95, sum by (le) (rate(loopover_ai_provider_request_duration_seconds_bucket[5m])))", "legendFormat": "p95", "refId": "B" },
-        { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.99, sum by (le) (rate(loopover_ai_provider_request_duration_seconds_bucket[5m])))", "legendFormat": "p99", "refId": "C" }
+        { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.50, sum by (le) ((rate(loopover_ai_provider_request_duration_seconds_bucket[5m]) or rate(gittensory_ai_provider_request_duration_seconds_bucket[5m]))))", "legendFormat": "p50", "refId": "A" },
+        { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.95, sum by (le) ((rate(loopover_ai_provider_request_duration_seconds_bucket[5m]) or rate(gittensory_ai_provider_request_duration_seconds_bucket[5m]))))", "legendFormat": "p95", "refId": "B" },
+        { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "expr": "histogram_quantile(0.99, sum by (le) ((rate(loopover_ai_provider_request_duration_seconds_bucket[5m]) or rate(gittensory_ai_provider_request_duration_seconds_bucket[5m]))))", "legendFormat": "p99", "refId": "C" }
       ]
     },
     {
@@ -127,7 +127,7 @@
       "targets": [
         {
           "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" },
-          "expr": "sum by (provider, request_kind) (rate(loopover_ai_provider_request_errors_total[5m]))",
+          "expr": "sum by (provider, request_kind) ((rate(loopover_ai_provider_request_errors_total[5m]) or rate(gittensory_ai_provider_request_errors_total[5m])))",
           "legendFormat": "{{provider}} / {{request_kind}}",
           "refId": "A"
         }

test/unit/selfhost-grafana-ai-usage-dashboard.test.ts

@@ -190,16 +190,49 @@ describe("Loopover - AI usage dashboard (Phase B2 consolidation)", () => {
   it("carries over the exact Prometheus expressions from the removed dashboards, byte-for-byte (no copy-paste drift)", () => {
     const targets = readDashboard().panels.flatMap((panel) => panel.targets ?? []);
     // From gittensory.json's removed "AI Usage & Cost" row.
-    expect(targets.some((t) => t.expr === "sum by (provider) (loopover_ai_cost_usd_total) or vector(0)")).toBe(true);
-    expect(targets.some((t) => t.expr === "sum by (provider) ((rate(loopover_ai_input_tokens_total[5m]) + rate(loopover_ai_output_tokens_total[5m])) * 60)")).toBe(true);
-    expect(targets.some((t) => t.expr === "sum by (model, effort) (increase(loopover_ai_requests_total[1h]))")).toBe(true);
-    expect(targets.some((t) => t.expr === "sum by (primary, fallback) (increase(loopover_ai_review_model_fallback_total[1h]))")).toBe(true);
+    expect(targets.some((t) => t.expr === "sum by (provider) ((loopover_ai_cost_usd_total or gittensory_ai_cost_usd_total)) or vector(0)")).toBe(true);
+    expect(targets.some((t) => t.expr === "sum by (provider) (((rate(loopover_ai_input_tokens_total[5m]) or rate(gittensory_ai_input_tokens_total[5m])) + (rate(loopover_ai_output_tokens_total[5m]) or rate(gittensory_ai_output_tokens_total[5m]))) * 60)")).toBe(true);
+    expect(targets.some((t) => t.expr === "sum by (model, effort) ((increase(loopover_ai_requests_total[1h]) or increase(gittensory_ai_requests_total[1h])))")).toBe(true);
+    expect(targets.some((t) => t.expr === "sum by (primary, fallback) ((increase(loopover_ai_review_model_fallback_total[1h]) or increase(gittensory_ai_review_model_fallback_total[1h])))")).toBe(true);
     // From codex-usage.json.
-    expect(targets.some((t) => t.expr === "sum by (model, effort) (increase(loopover_ai_requests_total{provider=\"codex\"}[$__rate_interval]))")).toBe(true);
+    expect(targets.some((t) => t.expr === "sum by (model, effort) ((increase(loopover_ai_requests_total{provider=\"codex\"}[$__rate_interval]) or increase(gittensory_ai_requests_total{provider=\"codex\"}[$__rate_interval])))")).toBe(true);
     // From claude-usage.json's OTEL section (uses $claudeModel, not $model, to stay independent of the durable-log filters).
     expect(targets.some((t) => t.expr === "sum(last_over_time(claude_code_cost_usage_USD_total{model=~\"$claudeModel\"}[$__range]))")).toBe(true);
   });
 
+  // REGRESSION: #5522 hard-cutover renamed this dashboard's loopover_ai_* queries from their pre-rebrand
+  // gittensory_ai_* names with no historical fallback, so every panel here only ever showed data recorded
+  // after that cutover -- confirmed live (both metric names have real historical series in Prometheus).
+  // Mirrors the (loopover_x or gittensory_x) union fix applied to grafana/dashboards/gittensory.json in
+  // #6779/#6787, including that fix's own lesson: a label matcher like {provider="codex"} must bind to each
+  // side of the union individually, never to the closing paren of the union as a whole.
+  it("unions every loopover_ai_* query with its pre-rebrand gittensory_ai_* counterpart for historical continuity (#5522 follow-up)", () => {
+    const targets = readDashboard().panels.flatMap((panel) => panel.targets ?? []);
+
+    for (const target of targets) {
+      if (!target.expr?.includes("loopover_ai_")) continue;
+      expect(target.expr, `missing historical union: ${target.expr}`).toContain("gittensory_ai_");
+      expect(target.expr, `invalid PromQL -- label matcher applied after a closing paren: ${target.expr}`).not.toMatch(/\)\s*\{/);
+    }
+
+    expect(targets.some((t) => t.expr === 'sum by (provider, kind) ((loopover_ai_input_tokens_total or gittensory_ai_input_tokens_total))')).toBe(true);
+    expect(targets.some((t) => t.expr === 'sum by (provider, kind) ((loopover_ai_output_tokens_total or gittensory_ai_output_tokens_total))')).toBe(true);
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          'sum by (kind) ((increase(loopover_ai_input_tokens_total{provider="codex"}[$__rate_interval]) or increase(gittensory_ai_input_tokens_total{provider="codex"}[$__rate_interval])))',
+      ),
+    ).toBe(true);
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          'sum by (kind) ((increase(loopover_ai_output_tokens_total{provider="codex"}[$__rate_interval]) or increase(gittensory_ai_output_tokens_total{provider="codex"}[$__rate_interval])))',
+      ),
+    ).toBe(true);
+  });
+
   it("keeps the Claude OTEL section on its own $claudeModel variable, never the durable log's $provider/$feature/$model", () => {
     const dashboard = readDashboard();
     const otelRowIndex = dashboard.panels.findIndex((p) => p.title?.includes("Claude Code native OTEL"));

test/unit/selfhost-grafana-gpu-metrics-dashboard.test.ts

@@ -0,0 +1,73 @@
+import { readFileSync } from "node:fs";
+import { join } from "node:path";
+import { describe, expect, it } from "vitest";
+
+type DashboardTarget = { expr?: string };
+type DashboardPanel = { title?: string; targets?: DashboardTarget[] };
+type Dashboard = { panels: DashboardPanel[] };
+
+const dashboardPath = join(process.cwd(), "grafana/dashboards/gpu-metrics.json");
+
+function readDashboard(): Dashboard {
+  return JSON.parse(readFileSync(dashboardPath, "utf8")) as Dashboard;
+}
+
+describe("LoopOver GPU Metrics Grafana dashboard", () => {
+  // REGRESSION: #5522 hard-cutover renamed this dashboard's 3 loopover_ai_provider_* queries from their
+  // pre-rebrand gittensory_ai_provider_* names with no historical fallback, so every panel here only ever
+  // showed data recorded after that cutover -- confirmed live, both metric names have real historical series
+  // in Prometheus going back well past the cutover. Mirrors the (loopover_x or gittensory_x) union fix
+  // already shipped for grafana/dashboards/gittensory.json (#6779/#6787) and ai-usage.json (#5522 follow-up).
+  it("unions every loopover_ai_provider_* query with its pre-rebrand gittensory_ai_provider_* counterpart for historical continuity", () => {
+    const targets = readDashboard().panels.flatMap((panel) => panel.targets ?? []);
+
+    for (const target of targets) {
+      if (!target.expr?.includes("loopover_ai_provider")) continue;
+      expect(target.expr, `missing historical union: ${target.expr}`).toContain("gittensory_ai_provider");
+      expect(target.expr, `invalid PromQL -- label matcher applied after a closing paren: ${target.expr}`).not.toMatch(/\)\s*\{/);
+    }
+
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          "sum by (provider, request_kind) ((rate(loopover_ai_provider_request_duration_seconds_count[5m]) or rate(gittensory_ai_provider_request_duration_seconds_count[5m])))",
+      ),
+    ).toBe(true);
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          "histogram_quantile(0.50, sum by (le) ((rate(loopover_ai_provider_request_duration_seconds_bucket[5m]) or rate(gittensory_ai_provider_request_duration_seconds_bucket[5m]))))",
+      ),
+    ).toBe(true);
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          "histogram_quantile(0.95, sum by (le) ((rate(loopover_ai_provider_request_duration_seconds_bucket[5m]) or rate(gittensory_ai_provider_request_duration_seconds_bucket[5m]))))",
+      ),
+    ).toBe(true);
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          "histogram_quantile(0.99, sum by (le) ((rate(loopover_ai_provider_request_duration_seconds_bucket[5m]) or rate(gittensory_ai_provider_request_duration_seconds_bucket[5m]))))",
+      ),
+    ).toBe(true);
+    expect(
+      targets.some(
+        (t) =>
+          t.expr ===
+          "sum by (provider, request_kind) ((rate(loopover_ai_provider_request_errors_total[5m]) or rate(gittensory_ai_provider_request_errors_total[5m])))",
+      ),
+    ).toBe(true);
+  });
+
+  it("declares a stable title and uid", () => {
+    const dashboard = readDashboard() as Dashboard & { title?: string; uid?: string };
+
+    expect(dashboard.title).toBe("LoopOver — GPU Metrics");
+    expect(dashboard.uid).toBe("loopover-gpu");
+  });
+});