feat(diagnostics-otel): add genai token usage metric

CHANGELOG.md

@@ -28,6 +28,7 @@ Docs: https://docs.openclaw.ai
 - Diagnostics/OTEL: emit bounded context assembly diagnostics and export `openclaw.context.assembled` spans with prompt/history sizes but no prompt, history, response, or session-key content. Thanks @vincentkoc.
 - Diagnostics/OTEL: export existing tool-loop diagnostics as `openclaw.tool.loop` counters and spans without loop messages, session identifiers, params, or tool output. Thanks @vincentkoc.
 - Diagnostics/OTEL: export diagnostic memory samples and pressure as bounded memory histograms, counters, and pressure spans to help spot leak regressions without session or payload data. Thanks @vincentkoc.
+- Diagnostics/OTEL: add the GenAI `gen_ai.client.token.usage` histogram for input/output model usage while keeping session identifiers and aggregate cache counters out of the semantic metric. Thanks @vincentkoc.
 - Diagnostics/OTEL: add bounded outbound message delivery lifecycle diagnostics and export them as low-cardinality delivery spans/metrics without message body, recipient, room, or media-path data. (#71471) Thanks @vincentkoc and @jlapenna.
 - Diagnostics/OTEL: emit bounded exec-process diagnostics and export them as `openclaw.exec` spans without exposing command text, working directories, or container identifiers. (#71451) Thanks @vincentkoc and @jlapenna.
 - Diagnostics/OTEL: support `OPENCLAW_OTEL_PRELOADED=1` so the plugin can reuse an already-registered OpenTelemetry SDK while keeping OpenClaw diagnostic listeners wired. (#71450) Thanks @vincentkoc and @jlapenna.

extensions/diagnostics-otel/src/service.test.ts

@@ -691,6 +691,55 @@ describe("diagnostics-otel service", () => {
     await service.stop?.(ctx);
   });
 
+  test("exports GenAI client token usage histogram for input and output only", async () => {
+    const service = createDiagnosticsOtelService();
+    const ctx = createOtelContext(OTEL_TEST_ENDPOINT, { metrics: true });
+    await service.start(ctx);
+
+    emitDiagnosticEvent({
+      type: "model.usage",
+      sessionKey: "session-key",
+      channel: "webchat",
+      provider: "openai",
+      model: "gpt-5.4",
+      usage: {
+        input: 12,
+        output: 7,
+        cacheRead: 3,
+        cacheWrite: 2,
+        promptTokens: 17,
+        total: 24,
+      },
+    });
+    await flushDiagnosticEvents();
+
+    expect(telemetryState.meter.createHistogram).toHaveBeenCalledWith(
+      "gen_ai.client.token.usage",
+      expect.objectContaining({
+        unit: "{token}",
+        advice: {
+          explicitBucketBoundaries: expect.arrayContaining([1, 4, 16, 1024, 67108864]),
+        },
+      }),
+    );
+    const genAiTokenUsage = telemetryState.histograms.get("gen_ai.client.token.usage");
+    expect(genAiTokenUsage?.record).toHaveBeenCalledTimes(2);
+    expect(genAiTokenUsage?.record).toHaveBeenCalledWith(12, {
+      "gen_ai.operation.name": "chat",
+      "gen_ai.provider.name": "openai",
+      "gen_ai.request.model": "gpt-5.4",
+      "gen_ai.token.type": "input",
+    });
+    expect(genAiTokenUsage?.record).toHaveBeenCalledWith(7, {
+      "gen_ai.operation.name": "chat",
+      "gen_ai.provider.name": "openai",
+      "gen_ai.request.model": "gpt-5.4",
+      "gen_ai.token.type": "output",
+    });
+    expect(JSON.stringify(genAiTokenUsage?.record.mock.calls)).not.toContain("session-key");
+    await service.stop?.(ctx);
+  });
+
   test("exports run, model call, and tool execution lifecycle spans", async () => {
     const service = createDiagnosticsOtelService();
     const ctx = createOtelContext(OTEL_TEST_ENDPOINT, { traces: true, metrics: true });

extensions/diagnostics-otel/src/service.ts

@@ -52,6 +52,9 @@ const BLOCKED_OTEL_LOG_ATTRIBUTE_KEYS = new Set(["__proto__", "prototype", "cons
 const PRELOADED_OTEL_SDK_ENV = "OPENCLAW_OTEL_PRELOADED";
 const OTEL_SEMCONV_STABILITY_OPT_IN_ENV = "OTEL_SEMCONV_STABILITY_OPT_IN";
 const GEN_AI_LATEST_EXPERIMENTAL_OPT_IN = "gen_ai_latest_experimental";
+const GEN_AI_TOKEN_USAGE_BUCKETS = [
+  1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864,
+];
 
 type OtelContentCapturePolicy = {
   inputMessages: boolean;
@@ -575,6 +578,13 @@ export function createDiagnosticsOtelService(): OpenClawPluginService {
         unit: "1",
         description: "Token usage by type",
       });
+      const genAiTokenUsageHistogram = meter.createHistogram("gen_ai.client.token.usage", {
+        unit: "{token}",
+        description: "Number of input and output tokens used by GenAI client operations",
+        advice: {
+          explicitBucketBoundaries: GEN_AI_TOKEN_USAGE_BUCKETS,
+        },
+      });
       const costCounter = meter.createCounter("openclaw.cost.usd", {
         unit: "1",
         description: "Estimated model cost (USD)",
@@ -854,13 +864,26 @@ export function createDiagnosticsOtelService(): OpenClawPluginService {
           "openclaw.provider": evt.provider ?? "unknown",
           "openclaw.model": evt.model ?? "unknown",
         };
+        const genAiAttrs: Record<string, string> = {
+          "gen_ai.operation.name": "chat",
+          "gen_ai.provider.name": lowCardinalityAttr(evt.provider),
+          ...(evt.model ? { "gen_ai.request.model": lowCardinalityAttr(evt.model) } : {}),
+        };
 
         const usage = evt.usage;
         if (usage.input) {
           tokensCounter.add(usage.input, { ...attrs, "openclaw.token": "input" });
+          genAiTokenUsageHistogram.record(usage.input, {
+            ...genAiAttrs,
+            "gen_ai.token.type": "input",
+          });
         }
         if (usage.output) {
           tokensCounter.add(usage.output, { ...attrs, "openclaw.token": "output" });
+          genAiTokenUsageHistogram.record(usage.output, {
+            ...genAiAttrs,
+            "gen_ai.token.type": "output",
+          });
         }
         if (usage.cacheRead) {
           tokensCounter.add(usage.cacheRead, { ...attrs, "openclaw.token": "cache_read" });