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".
What this skill does
# 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 | NotRelated in Design
contribute
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mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
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plaid
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