langfuse-observability

Set up comprehensive observability for Langfuse with metrics, dashboards, and alerts. Use when implementing monitoring for LLM operations, setting up dashboards, or configuring alerting for Langfuse integration health. Trigger with phrases like "langfuse monitoring", "langfuse metrics", "langfuse observability", "monitor langfuse", "langfuse alerts", "langfuse dashboard".

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最近来源活动: 2026年7月17日 22:18
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安装方式

默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。

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决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。

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name	langfuse-observability
description	Set up comprehensive observability for Langfuse with metrics, dashboards, and alerts. Use when implementing monitoring for LLM operations, setting up dashboards, or configuring alerting for Langfuse integration health. Trigger with phrases like "langfuse monitoring", "langfuse metrics", "langfuse observability", "monitor langfuse", "langfuse alerts", "langfuse dashboard".
allowed-tools	Read, Write, Edit
version	1.12.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
tags	["saas","langfuse","monitoring","observability","llm"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

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
 observations =  langfuse...({
  : ,
  :  (.() - ).(),
  : ,
});

 totalCost = observations..(
   sum + (obs. || ), 
);
.();

// 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;
    }
  }
);

// 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());
});

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

{
  "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])" }]
    }
  ]
}

# 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"

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

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