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".
Best use case
langfuse-reference-architecture is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using langfuse-reference-architecture should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/langfuse-reference-architecture/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How langfuse-reference-architecture Compares
| Feature / Agent | langfuse-reference-architecture | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
```typescript
// 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 ${signal}, flushing traces...`);
await sdk?.shutdown();
process.exit(0);
});
}
return sdk;
}
// Singleton client for non-tracing operations
let client: LangfuseClient | null = null;
export function getLangfuseClient(): LangfuseClient {
if (!client) client = new LangfuseClient();
return client;
}
// Request context for user/session tracking
interface RequestContext {
userId?: string;
sessionId?: string;
requestId: string;
}
const requestStore = new AsyncLocalStorage<RequestContext>();
export function getRequestContext(): RequestContext | undefined {
return requestStore.getStore();
}
export function runWithContext<T>(ctx: RequestContext, fn: () => T): T {
return requestStore.run(ctx, fn);
}
```
### Pattern 2: Express Middleware for Automatic Tracing
```typescript
// src/middleware/tracing.ts
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import { runWithContext, getRequestContext } from "../lib/tracing";
import { randomUUID } from "crypto";
import type { Request, Response, NextFunction } from "express";
export function langfuseMiddleware() {
return (req: Request, res: Response, next: NextFunction) => {
const ctx = {
requestId: req.headers["x-request-id"]?.toString() || randomUUID(),
userId: req.headers["x-user-id"]?.toString(),
sessionId: req.headers["x-session-id"]?.toString(),
};
runWithContext(ctx, () => {
startActiveObservation(`${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:
```typescript
// 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();
}
```
```typescript
// 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
```typescript
// 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:
```typescript
// 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
- [TypeScript SDK Overview](https://langfuse.com/docs/observability/sdk/typescript/overview)
- [Advanced Configuration](https://langfuse.com/docs/observability/sdk/typescript/advanced-usage)
- [Self-Hosting Guide](https://langfuse.com/self-hosting)
- [OpenTelemetry Context Propagation](https://opentelemetry.io/docs/concepts/context-propagation/)Related Skills
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