Langfuse Reference Architecture
Production-grade Langfuse architecture patterns and best practices.
by jeremylongshore · MIT
The skill
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".
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
},
};
// Pass the selected environment from the application's configuration boundary.
// Keeping configuration resolution outside tracing makes this module deterministic
// and straightforward to test.
export function getTracingConfig(env: Environment = "development") {
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 |
Output
Produce an architecture decision record identifying the selected deployment tier, tracing boundary, context-propagation method, failure mode, retention owner, and rollback path. Include the tested revision and state whether the evidence comes from a local, staging, or production-like environment.
Examples
A growth-stage service can keep the SDK's internal queue and add AsyncLocalStorage propagation, then test that a request and downstream worker share the trace context. An enterprise deployment can put a queue between the application and self-hosted Langfuse, trip the circuit breaker during an export failure drill, and prove application traffic continues while telemetry recovers.
Resources
Use this skill
Connect your agent to the Skill Store MCP server and ask for it by ref: jeremylongshore/langfuse-reference-architecture-84742693164.
Details
- Publisher
- jeremylongshore
- Category
- Software Development › DevOps & cloud
- License
- MIT
- Updated
- 2026-10-19
References
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