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".
Install
mkdir -p .claude/skills/langfuse-reference-architecture && curl -L -o skill.zip "https://mcp.directory/api/skills/download/4832" && unzip -o skill.zip -d .claude/skills/langfuse-reference-architecture && rm skill.zipInstalls to .claude/skills/langfuse-reference-architecture
About this skill
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() {
if (sdk) return sdk;
sdk = new NodeSDK({
spanProcessors: [
new LangfuseSpanProcessor({
exportIntervalMillis: 5000,
maxExportBatchSize: 50,
}),
],
});
sdk.start();
// Graceful shutdown
for (const signal of ["SIGTERM", "SIGINT"]) {
process.on(signal, async () => {
console.log(`Received ${signal}, flushing traces...`);
await sdk?.shutdown();
process.exit(0);
});
}
return sdk;
}
// Singleton client for non-tracing operations
let client: LangfuseClient | null = null;
export function getLangfuseClient(): LangfuseClient {
if (!client) client = new LangfuseClient();
return client;
}
// Request context for user/session tracking
interface RequestContext {
userId?: string;
sessionId?: string;
requestId: string;
}
const requestStore = new AsyncLocalStorage<RequestContext>();
export function getRequestContext(): RequestContext | undefined {
return requestStore.getStore();
}
export function runWithContext<T>(ctx: RequestContext, fn: () => T): T {
return requestStore.run(ctx, fn);
}
Pattern 2: Express Middleware for Automatic Tracing
// src/middleware/tracing.ts
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import { runWithContext, getRequestContext } from "../lib/tracing";
import { randomUUID } from "crypto";
import type { Request, Response, NextFunction } from "express";
export function langfuseMiddleware() {
return (req: Request, res: Response, next: NextFunction) => {
const ctx = {
requestId: req.headers["x-request-id"]?.toString() || randomUUID(),
userId: req.headers["x-user-id"]?.toString(),
sessionId: req.headers["x-session-id"]?.toString(),
};
runWithContext(ctx, () => {
startActiveObservation(`${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());
Pattern 3: Cross-Service Trace Correlation
For microservices, propagate trace context via HTTP headers:
// 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);
});
});
Pattern 4: Multi-Environment Configuration
// 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;
}
Pattern 5: Graceful Degradation
When Langfuse is unavailable, the app must keep running:
// 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;
}
Architecture Decision Matrix
| 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 |
Error Handling
| 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 |
Resources
More by jeremylongshore
View all skills by jeremylongshore →You might also like
flutter-development
aj-geddes
Build beautiful cross-platform mobile apps with Flutter and Dart. Covers widgets, state management with Provider/BLoC, navigation, API integration, and material design.
drawio-diagrams-enhanced
jgtolentino
Create professional draw.io (diagrams.net) diagrams in XML format (.drawio files) with integrated PMP/PMBOK methodologies, extensive visual asset libraries, and industry-standard professional templates. Use this skill when users ask to create flowcharts, swimlane diagrams, cross-functional flowcharts, org charts, network diagrams, UML diagrams, BPMN, project management diagrams (WBS, Gantt, PERT, RACI), risk matrices, stakeholder maps, or any other visual diagram in draw.io format. This skill includes access to custom shape libraries for icons, clipart, and professional symbols.
ui-ux-pro-max
nextlevelbuilder
"UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 8 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient."
godot
bfollington
This skill should be used when working on Godot Engine projects. It provides specialized knowledge of Godot's file formats (.gd, .tscn, .tres), architecture patterns (component-based, signal-driven, resource-based), common pitfalls, validation tools, code templates, and CLI workflows. The `godot` command is available for running the game, validating scripts, importing resources, and exporting builds. Use this skill for tasks involving Godot game development, debugging scene/resource files, implementing game systems, or creating new Godot components.
nano-banana-pro
garg-aayush
Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports both text-to-image generation and image-to-image editing with configurable resolution (1K default, 2K, or 4K for high resolution). DO NOT read the image file first - use this skill directly with the --input-image parameter.
fastapi-templates
wshobson
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
Related MCP Servers
Browse all serversAnalyze your Cursor Chat History for coding insights, development patterns, and best practices with powerful search and
Boost productivity with Task Master: an AI-powered tool for project management and agile development workflows, integrat
Boost Postgres performance with Postgres MCP Pro—AI-driven index tuning, health checks, and safe, intelligent SQL optimi
Empower your workflows with Perplexity Ask MCP Server—seamless integration of AI research tools for real-time, accurate
pg-aiguide — Version-aware PostgreSQL docs and best practices tailored for AI coding assistants. Improve queries, migrat
Securely join MySQL databases with Read MySQL for read-only query access and in-depth data analysis.
Stay ahead of the MCP ecosystem
Get weekly updates on new skills and servers.