Langfuse ObservabilitySAFE

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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: 1.17.0
license: MIT
author: Jeremy Longshore <[email protected]>
tags:
- saas
- langfuse
- monitoring
- observability
- llm
compatibility: Designed for Claude Code
---
# 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:**

```typescript
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:

```typescript
// 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 };
```

```typescript
// 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

```typescript
// 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

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

### Step 5: Grafana Dashboard

```json
{
  "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": [{ "exp

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.