langfuse-observability

Langfuse Observability

Overview

Set up monitoring for your Langfuse integration: Prometheus metrics for trace/generation throughput, Grafana dashboards, alert rules, and integration with Langfuse's built-in analytics dashboards and Metrics API.

Prerequisites

• Langfuse SDK integrated and producing traces
• For custom metrics: Prometheus + Grafana (or compatible stack)
• For Langfuse analytics: access to the Langfuse UI dashboard

Instructions

Step 1: Langfuse Built-In Dashboards

Langfuse provides pre-built dashboards in the UI at https://cloud.langfuse.com (or your self-hosted URL):

• Overview: Total traces, generations, scores, and errors
• Cost Dashboard: Token usage and costs over time, broken down by model, user, session
• Latency Dashboard: Response times across models and user segments
• Custom Dashboards: Build your own with the query engine (multi-level aggregations, filters by user/model/tag)

Accessing via Metrics API:

import { LangfuseClient } from "@langfuse/client";

const langfuse = new LangfuseClient();

// Fetch aggregated metrics programmatically
const traces = await langfuse.api.traces.list({
  fromTimestamp: new Date(Date.now() - 3600000).toISOString(), // Last hour
  limit: 100,
});

console.log(`Traces in last hour: ${traces.data.length}`);

// Get observations with cost data
const observations = await langfuse.api.observations.list({
  type: "GENERATION",
  fromTimestamp: new Date(Date.now() - 86400000).toISOString(),
  limit: 500,
});

const totalCost = observations.data.reduce(
  (sum, obs) => sum + (obs.calculatedTotalCost || 0), 0
);
console.log(`Total cost (24h): $${totalCost.toFixed(4)}`);

Step 2: Prometheus Metrics for Your App

Track the health of your Langfuse integration with custom Prometheus metrics:

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

const registry = new Registry();

export const metrics = {
  tracesCreated: new Counter({
    name: "langfuse_traces_created_total",
    help: "Total traces created",
    labelNames: ["status"],
    registers: [registry],
  }),

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

  tokensUsed: new Counter({
    name: "langfuse_tokens_total",
    help: "Total tokens used",
    labelNames: ["model", "type"],
    registers: [registry],
  }),

  costUsd: new Counter({
    name: "langfuse_cost_usd_total",
    help: "Total LLM cost in USD",
    labelNames: ["model"],
    registers: [registry],
  }),

  flushErrors: new Counter({
    name: "langfuse_flush_errors_total",
    help: "Total flush/export errors",
    registers: [registry],
  }),
};

export { registry };

// src/lib/traced-llm.ts -- Instrumented LLM wrapper
import { observe, updateActiveObservation } from "@langfuse/tracing";
import { metrics } from "./langfuse-metrics";
import OpenAI from "openai";

const openai = new OpenAI();

export const tracedLLM = observe(
  { name: "llm-call", asType: "generation" },
  async (model: string, messages: OpenAI.ChatCompletionMessageParam[]) => {
    const start = Date.now();
    updateActiveObservation({ model, input: messages });

    try {
      const response = await openai.chat.completions.create({ model, messages });

      const duration = (Date.now() - start) / 1000;
      metrics.generationDuration.observe({ model }, duration);
      metrics.tracesCreated.inc({ status: "success" });

      if (response.usage) {
        metrics.tokensUsed.inc({ model, type: "prompt" }, response.usage.prompt_tokens);
        metrics.tokensUsed.inc({ model, type: "completion" }, response.usage.completion_tokens);
      }

      updateActiveObservation({
        output: response.choices[0].message.content,
        usage: {
          promptTokens: response.usage?.prompt_tokens,
          completionTokens: response.usage?.completion_tokens,
        },
      });

      return response.choices[0].message.content;
    } catch (error) {
      metrics.tracesCreated.inc({ status: "error" });
      throw error;
    }
  }
);

Step 3: Expose Metrics Endpoint

// src/routes/metrics.ts
import { registry } from "../lib/langfuse-metrics";

app.get("/metrics", async (req, res) => {
  res.set("Content-Type", registry.contentType);
  res.end(await registry.metrics());
});

Step 4: Prometheus Scrape Config

# prometheus.yml
scrape_configs:
  - job_name: "llm-app"
    scrape_interval: 15s
    static_configs:
      - targets: ["llm-app:3000"]

Step 5: Grafana Dashboard

{
  "panels": [
    {
      "title": "LLM Requests/min",
      "type": "graph",
      "targets": [{ "expr": "rate(langfuse_traces_created_total[5m]) * 60" }]
    },
    {
      "title": "Generation Latency P95",
      "type": "graph",
      "targets": [{ "expr": "histogram_quantile(0.95, rate(langfuse_generation_duration_seconds_bucket[5m]))" }]
    },
    {
      "title": "Cost/Hour",
      "type": "stat",
      "targets": [{ "expr": "rate(langfuse_cost_usd_total[1h]) * 3600" }]
    },
    {
      "title": "Error Rate",
      "type": "graph",
      "targets": [{ "expr": "rate(langfuse_traces_created_total{status='error'}[5m]) / rate(langfuse_traces_created_total[5m])" }]
    }
  ]
}

Step 6: Alert Rules

# alertmanager-rules.yml
groups:
  - name: langfuse
    rules:
      - alert: HighLLMErrorRate
        expr: rate(langfuse_traces_created_total{status="error"}[5m]) / rate(langfuse_traces_created_total[5m]) > 0.05
        for: 5m
        labels: { severity: critical }
        annotations:
          summary: "LLM error rate above 5%"

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

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

Key Metrics Reference

Metric	Type	Purpose
langfuse_traces_created_total	Counter	LLM request throughput + error rate
langfuse_generation_duration_seconds	Histogram	Latency percentiles
langfuse_tokens_total	Counter	Token usage tracking
langfuse_cost_usd_total	Counter	Budget monitoring
langfuse_flush_errors_total	Counter	SDK health

Error Handling

Issue	Cause	Solution
Missing metrics	No instrumentation	Use the tracedLLM wrapper
High cardinality	Too many label values	Limit to model + status only
Alert storms	Thresholds too low	Start conservative, tune over time
Metrics endpoint slow	Large registry	Use summary instead of histogram for high-volume

Resources

• Langfuse Metrics Overview
• Custom Dashboards
• Metrics API
• Token & Cost Tracking