Langfuse Observability

Langfuse Observability

Overview

Set up comprehensive observability for Langfuse integrations including metrics, dashboards, and alerts.

Prerequisites

• Prometheus or compatible metrics backend
• Grafana or similar dashboarding tool
• AlertManager or PagerDuty configured
• Langfuse SDK integrated

Key Metrics

Instructions

Step 1: Implement Prometheus Metrics

// lib/​langfuse/​metrics.ts
import { Registry, Counter, Histogram, Gauge } from "prom-client";

const registry = new Registry();

// Trace metrics
export const traceCounter = new Counter({
  name: "langfuse_traces_total",
  help: "Total Langfuse traces created",
  labelNames: ["name", "status", "environment"],
  registers: [registry],
});

// Generation metrics
export const generationCounter = new Counter({
  name: "langfuse_generations_total",
  help: "Total LLM generations",
  labelNames: ["model", "status"],
  registers: [registry],
});

export const generationDuration = new Histogram({
  name: "langfuse_generation_duration_seconds",
  help: "LLM generation duration",
  labelNames: ["model"],
  buckets: [0.1, 0.5, 1, 2, 5, 10, 30, 60],
  registers: [registry],
});

// Token metrics
export const tokenCounter = new Counter({
  name: "langfuse_tokens_total",
  help: "Total tokens used",
  labelNames: ["model", "type"], // type: prompt, completion
  registers: [registry],
});

// Cost metrics
export const costCounter = new Counter({
  name: "langfuse_cost_usd_total",
  help: "Total LLM cost in USD",
  labelNames: ["model"],
  registers: [registry],
});

// Error metrics
export const errorCounter = new Counter({
  name: "langfuse_errors_total",
  help: "Langfuse errors by type",
  labelNames: ["error_type", "operation"],
  registers: [registry],
});

// SDK health metrics
export const flushDuration = new Histogram({
  name: "langfuse_flush_duration_seconds",
  help: "Langfuse SDK flush duration",
  buckets: [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5],
  registers: [registry],
});

export const pendingEventsGauge = new Gauge({
  name: "langfuse_pending_events",
  help: "Number of events pending flush",
  registers: [registry],
});

export { registry };

Step 2: Create Instrumented Langfuse Wrapper

// lib/​langfuse/​instrumented.ts
import { Langfuse } from "langfuse";
import {
  traceCounter,
  generationCounter,
  generationDuration,
  tokenCounter,
  costCounter,
  errorCounter,
  flushDuration,
} from "./​metrics";

const MODEL_PRICING: Record<string, { input: number; output: number }> = {
  "gpt-4-turbo": { input: 10.0, output: 30.0 },
  "gpt-4o": { input: 5.0, output: 15.0 },
  "gpt-4o-mini": { input: 0.15, output: 0.6 },
  "claude-3-sonnet": { input: 3.0, output: 15.0 },
};

class InstrumentedLangfuse {
  private langfuse: Langfuse;
  private environment: string;

  constructor(config: ConstructorParameters<typeof Langfuse>[0]) {
    this.langfuse = new Langfuse(config);
    this.environment = process.env.NODE_ENV || "development";
  }

  trace(params: Parameters<typeof this.langfuse.trace>[0]) {
    const trace = this.langfuse.trace(params);

    traceCounter.inc({
      name: params.name || "unknown",
      status: "created",
      environment: this.environment,
    });

    // Wrap update to track completion
    const originalUpdate = trace.update.bind(trace);
    trace.update = (updateParams) => {
      if (updateParams.level === "ERROR") {
        traceCounter.inc({
          name: params.name || "unknown",
          status: "error",
          environment: this.environment,
        });
      } else if (updateParams.output) {
        traceCounter.inc({
          name: params.name || "unknown",
          status: "completed",
          environment: this.environment,
        });
      }
      return originalUpdate(updateParams);
    };

    // Wrap generation to track LLM calls
    const originalGeneration = trace.generation.bind(trace);
    trace.generation = (genParams) => {
      const startTime = Date.now();
      const generation = originalGeneration(genParams);
      const model = genParams.model || "unknown";

      generationCounter.inc({ model, status: "started" });

      // Wrap end to track completion
      const originalEnd = generation.end.bind(generation);
      generation.end = (endParams) => {
        const duration = (Date.now() - startTime) / 1000;
        generationDuration.observe({ model }, duration);
        generationCounter.inc({ model, status: "completed" });

        // Track tokens
        if (endParams?.usage) {
          const { promptTokens = 0, completionTokens = 0 } = endParams.usage;

          tokenCounter.inc({ model, type: "prompt" }, promptTokens);
          tokenCounter.inc({ model, type: "completion" }, completionTokens);

          // Track cost
          const pricing = MODEL_PRICING[model];
          if (pricing) {
            const cost =
              (promptTokens / 1_000_000) * pricing.input +
              (completionTokens / 1_000_000) * pricing.output;
            costCounter.inc({ model }, cost);
          }
        }

        return originalEnd(endParams);
      };

      return generation;
    };

    return trace;
  }

  async flushAsync() {
    const timer = flushDuration.startTimer();
    try {
      await this.langfuse.flushAsync();
    } catch (error) {
      errorCounter.inc({ error_type: "flush_error", operation: "flush" });
      throw error;
    } finally {
      timer();
    }
  }

  async shutdownAsync() {
    return this.langfuse.shutdownAsync();
  }
}

export const langfuse = new InstrumentedLangfuse({
  publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
  secretKey: process.env.LANGFUSE_SECRET_KEY!,
});

Step 3: Expose Metrics Endpoint

// api/​metrics/​route.ts (Next.js) or app.get('/​metrics') (Express)
import { registry } from "@/​lib/​langfuse/​metrics";

export async function GET() {
  const metrics = await registry.metrics();

  return new Response(metrics, {
    headers: {
      "Content-Type": registry.contentType,
    },
  });
}

Step 4: Configure Prometheus Scraping

# prometheus.yml
scrape_configs:
  - job_name: "llm-app"
    static_configs:
      - targets: ["app:3000"]
    metrics_path: "/​api/​metrics"
    scrape_interval: 15s

  - job_name: "langfuse-cloud"
    static_configs:
      - targets: ["cloud.langfuse.com"]
    scheme: https
    metrics_path: "/​api/​public/​metrics"
    bearer_token: "${LANGFUSE_PUBLIC_KEY}"

Step 5: Create Grafana Dashboard

{
  "dashboard": {
    "title": "Langfuse LLM Observability",
    "panels": [
      {
        "title": "LLM Requests per Second",
        "type": "timeseries",
        "targets": [{
          "expr": "rate(langfuse_generations_total[5m])",
          "legendFormat": "{{model}}"
        }]
      },
      {
        "title": "LLM Latency (P50/​P95/​P99)",
        "type": "timeseries",
        "targets": [
          {
            "expr": "histogram_quantile(0.5, rate(langfuse_generation_duration_seconds_bucket[5m]))",
            "legendFormat": "P50"
          },
          {
            "expr": "histogram_quantile(0.95, rate(langfuse_generation_duration_seconds_bucket[5m]))",
            "legendFormat": "P95"
          },
          {
            "expr": "histogram_quantile(0.99, rate(langfuse_generation_duration_seconds_bucket[5m]))",
            "legendFormat": "P99"
          }
        ]
      },
      {
        "title": "Token Usage by Model",
        "type": "timeseries",
        "targets": [{
          "expr": "rate(langfuse_tokens_total[1h])",
          "legendFormat": "{{model}} - {{type}}"
        }]
      },
      {
        "title": "LLM Cost (USD/​hour)",
        "type": "stat",
        "targets": [{
          "expr": "sum(rate(langfuse_cost_usd_total[1h])) * 3600"
        }]
      },
      {
        "title": "Error Rate",
        "type": "timeseries",
        "targets": [{
          "expr": "rate(langfuse_errors_total[5m])",
          "legendFormat": "{{error_type}}"
        }]
      }
    ]
  }
}

Step 6: Configure Alerts

# alerts/​langfuse.yaml
groups:
  - name: langfuse_alerts
    rules:
      - alert: LangfuseHighErrorRate
        expr: |
          rate(langfuse_errors_total[5m]) /
          rate(langfuse_generations_total[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Langfuse error rate > 5%"
          description: "LLM error rate is {{ $value | humanizePercentage }}"

      - alert: LangfuseHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(langfuse_generation_duration_seconds_bucket[5m])
          ) > 10
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "LLM P95 latency > 10s"

      - alert: LangfuseHighCost
        expr: |
          sum(rate(langfuse_cost_usd_total[1h])) * 24 > 100
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Projected daily LLM cost > $100"

      - alert: LangfuseFlushBacklog
        expr: langfuse_pending_events > 1000
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Langfuse event backlog > 1000"

      - alert: LangfuseDown
        expr: up{job="llm-app"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "LLM application is down"

Output

• Prometheus metrics for all Langfuse operations
• Instrumented Langfuse wrapper
• Metrics endpoint for scraping
• Grafana dashboard configuration
• AlertManager rules

Metrics Reference

Error Handling

Resources

• Prometheus Best Practices
• Grafana Dashboards
• AlertManager
• Langfuse Analytics

Next Steps

For incident response, see langfuse-incident-runbook.