langfuse-reference-architecture

Production-grade Langfuse architecture patterns and best practices. Use when designing LLM observability infrastructure, planning Langfuse deployment, or implementing enterprise-grade tracing architecture. Trigger with phrases like "langfuse architecture", "langfuse design", "langfuse infrastructure", "langfuse enterprise", "langfuse at scale".

Ir a la instalación

Repositorio: jeremylongshore/claude-code-plugins-plus-skills
Última actividad en el origen: 17 de julio de 2026 a las 22:18
Idioma detectado de SKILL.md: inglés
Estrellas: 2604
Forks: 378

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Volver a la búsqueda Volver a la instalación

name	langfuse-reference-architecture
description	Production-grade Langfuse architecture patterns and best practices. Use when designing LLM observability infrastructure, planning Langfuse deployment, or implementing enterprise-grade tracing architecture. Trigger with phrases like "langfuse architecture", "langfuse design", "langfuse infrastructure", "langfuse enterprise", "langfuse at scale".
allowed-tools	Read, Write, Edit
version	1.12.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
tags	["saas","langfuse","deployment","observability","llm"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Langfuse Reference Architecture

Overview

Production-grade architecture patterns for Langfuse LLM observability: singleton SDK, context propagation with AsyncLocalStorage, cross-service trace correlation, multi-environment configurations, and scale strategies.

Prerequisites

Understanding of distributed systems and async patterns
Node.js 18+ with OpenTelemetry SDK
For v4+: @langfuse/tracing, @langfuse/otel, @opentelemetry/sdk-node

Architecture Tiers

Tier	Scale	Architecture	Langfuse Host
Starter	< 100K traces/day	Direct SDK, Cloud	Langfuse Cloud
Growth	100K-1M traces/day	Singleton + batching	Cloud or Self-hosted
Enterprise	1M+ traces/day	Queue-buffered + sampling	Self-hosted (HA)

Instructions

Pattern 1: Singleton SDK with Context Propagation

// src/lib/tracing.ts -- Single module for all tracing
import { LangfuseClient } from "@langfuse/client";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";
import { AsyncLocalStorage } from "async_hooks";

// Singleton OTel SDK
let sdk: NodeSDK | null = null;

export function initTracing() {
   (sdk)  sdk;

  sdk =  ({
    : [
       ({
        : ,
        : ,
      }),
    ],
  });
  sdk.();

  
   ( signal  [, ]) {
    process.(signal,  () => {
      .();
       sdk?.();
      process.();
    });
  }

   sdk;
}


 :  |  = ;

  ():  {
   (!client) client =  ();
   client;
}


  {
  ?: ;
  ?: ;
  : ;
}

 requestStore =  <>();

  ():  |  {
   requestStore.();
}

  runWithContext<T>(: , :  T): T {
   requestStore.(ctx, fn);
}

// src/middleware/tracing.ts
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import { runWithContext, getRequestContext } from "../lib/tracing";
import { randomUUID } from "crypto";
import type { Request, Response, NextFunction } from "express";

export function langfuseMiddleware() {
  return (req: Request, res: Response, next: NextFunction) => {
    const ctx = {
      requestId: req.headers["x-request-id"]?.toString() || randomUUID(),
      userId: req.headers["x-user-id"]?.toString(),
      sessionId: req.headers["x-session-id"]?.toString(),
    };

    runWithContext(ctx, () => {
      startActiveObservation(`${req.method} ${req.path}`, async () => {
        updateActiveObservation({
          input: {
            method: req.method,
            path: req.path,
            query: req.query,
          },
          metadata: {
            userId: ctx.userId,
            sessionId: ctx.sessionId,
            requestId: ctx.requestId,
          },
        });

        // Capture response
        const originalEnd = res.end.bind(res);
        res.end = function (...args: any[]) {
          updateActiveObservation({
            output: { statusCode: res.statusCode },
          });
          return originalEnd(...args);
        } as any;

        next();
      }).catch(next);
    });
  };
}

// Usage
import express from "express";
import { initTracing } from "./lib/tracing";
import { langfuseMiddleware } from "./middleware/tracing";

initTracing();
const app = express();
app.use(langfuseMiddleware());

// Service A: Inject trace context into outbound requests
import { context, propagation } from "@opentelemetry/api";

async function callServiceB(data: any) {
  const headers: Record<string, string> = {};

  // OTel propagation injects traceparent header automatically
  propagation.inject(context.active(), headers);

  const response = await fetch("https://service-b.internal/api/process", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      ...headers, // Includes traceparent, tracestate
    },
    body: JSON.stringify(data),
  });

  return response.json();
}

// Service B: Extract and continue trace context
import { context, propagation } from "@opentelemetry/api";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";

app.post("/api/process", async (req, res) => {
  // OTel automatically extracts context from incoming headers
  // when using standard HTTP instrumentation.
  // Any startActiveObservation call will be a child of the extracted trace.

  await startActiveObservation("service-b-process", async () => {
    updateActiveObservation({ input: req.body });
    const result = await processData(req.body);
    updateActiveObservation({ output: result });
    res.json(result);
  });
});

// src/config/langfuse.ts
type Environment = "development" | "staging" | "production";

const configs: Record<Environment, {
  exportIntervalMillis: number;
  maxExportBatchSize: number;
  sampleRate: number;
}> = {
  development: {
    exportIntervalMillis: 1000,   // Immediate visibility
    maxExportBatchSize: 1,
    sampleRate: 1.0,              // Trace everything
  },
  staging: {
    exportIntervalMillis: 5000,
    maxExportBatchSize: 25,
    sampleRate: 0.5,              // 50% sampling
  },
  production: {
    exportIntervalMillis: 10000,
    maxExportBatchSize: 100,
    sampleRate: 0.1,              // 10% sampling
  },
};

export function getTracingConfig() {
  const env = (process.env.NODE_ENV || "development") as Environment;
  return configs[env] || configs.development;
}

// The v4+ SDK with OTel handles this gracefully:
// - Failed exports are logged but don't throw
// - Events are buffered in the queue
// - Queue drops oldest events when maxQueueSize is exceeded
//
// For additional safety at the application level:

import { observe, updateActiveObservation } from "@langfuse/tracing";

let tracingHealthy = true;
let consecutiveFailures = 0;
const MAX_FAILURES = 10;

export function safeTrace<T extends (...args: any[]) => Promise<any>>(
  name: string,
  fn: T
): T {
  return (async (...args: Parameters<T>) => {
    if (!tracingHealthy) {
      return fn(...args); // Circuit breaker open
    }

    try {
      const result = await observe({ name }, async () => {
        updateActiveObservation({ input: args });
        const r = await fn(...args);
        updateActiveObservation({ output: r });
        return r;
      })();
      consecutiveFailures = 0;
      return result;
    } catch (error) {
      consecutiveFailures++;
      if (consecutiveFailures >= MAX_FAILURES) {
        tracingHealthy = false;
        console.error("Langfuse tracing disabled (circuit breaker open)");
        // Re-enable after 5 minutes
        setTimeout(() => { tracingHealthy = true; consecutiveFailures = 0; }, 300000);
      }
      return fn(...args);
    }
  }) as T;
}

Decision	Starter	Growth	Enterprise
Langfuse host	Cloud	Cloud or Self-hosted	Self-hosted (HA)
SDK version	v4+	v4+	v4+ with custom processor
Sampling	100%	50-100%	5-20% + error always
Context propagation	Not needed	AsyncLocalStorage	OTel + HTTP headers
Queue buffer	SDK internal	SDK internal	External (SQS/Kafka)
Failover	None	Log-and-continue	Circuit breaker

Issue	Cause	Solution
Multiple SDK instances	No singleton	Centralize in `tracing.ts` module
Lost traces on deploy	No SIGTERM handler	Register shutdown handler
Cross-service trace gaps	No context propagation	Inject OTel `traceparent` header
Scale bottleneck	Direct SDK at high volume	Add queue buffer or increase sampling