Jeremylongshore Claude Code Plugins Plus Skills Langfuse Sdk Patterns

Langfuse SDK Patterns

Overview

Production-quality patterns for the Langfuse SDK: singleton clients, the observe wrapper, startActiveObservation for nested traces, session tracking, graceful shutdown, and error-safe tracing.

Prerequisites

• Completed langfuse-install-auth setup
• Understanding of async/await patterns
• For v4+: @langfuse/tracing, @langfuse/otel, @opentelemetry/sdk-node

Instructions

Pattern 1: Singleton Client with Graceful Shutdown

// src/lib/langfuse.ts -- single file, import everywhere
import { LangfuseClient } from "@langfuse/client";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

// Singleton client for prompts, datasets, scores
let client: LangfuseClient | null = null;
export function getLangfuseClient(): LangfuseClient {
  if (!client) {
    client = new LangfuseClient();
  }
  return client;
}

// One-time OTel setup (call at app entry point)
let sdk: NodeSDK | null = null;
export function initTracing(): NodeSDK {
  if (!sdk) {
    sdk = new NodeSDK({
      spanProcessors: [new LangfuseSpanProcessor()],
    });
    sdk.start();

    // Graceful shutdown on process exit
    const shutdown = async () => {
      await sdk?.shutdown();
      process.exit(0);
    };
    process.on("SIGTERM", shutdown);
    process.on("SIGINT", shutdown);
  }
  return sdk;
}

Legacy v3 singleton:

import { Langfuse } from "langfuse";

let instance: Langfuse | null = null;

export function getLangfuse(): Langfuse {
  if (!instance) {
    instance = new Langfuse({
      flushAt: 15,
      flushInterval: 10000,
    });
    process.on("beforeExit", () => instance?.shutdownAsync());
  }
  return instance;
}

Pattern 2: observe Wrapper for Existing Functions

The observe wrapper is the most ergonomic way to add tracing. It wraps any function and auto-creates a span.

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

// Wrap existing functions -- no internal changes needed
const fetchUserProfile = observe(async (userId: string) => {
  updateActiveObservation({ input: { userId } });
  const profile = await db.users.findById(userId);
  updateActiveObservation({ output: { found: !!profile } });
  return profile;
});

// Mark LLM calls as generations
const summarize = observe(
  { name: "summarize-text", asType: "generation" },
  async (text: string) => {
    updateActiveObservation({ model: "gpt-4o-mini", input: text });
    const result = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "user", content: `Summarize: ${text}` }],
    });
    const output = result.choices[0].message.content;
    updateActiveObservation({
      output,
      usage: {
        promptTokens: result.usage?.prompt_tokens,
        completionTokens: result.usage?.completion_tokens,
      },
    });
    return output;
  }
);

// When called inside another observed function, spans auto-nest
const pipeline = observe(async (userId: string) => {
  const profile = await fetchUserProfile(userId);
  const summary = await summarize(profile.bio);
  return { profile, summary };
});

Pattern 3: startActiveObservation for Inline Control

Use when you need fine-grained control over observation lifecycle within a function:

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

async function processOrder(orderId: string) {
  return await startActiveObservation("process-order", async () => {
    updateActiveObservation({ input: { orderId } });

    // Nested spans are automatic
    const validated = await startActiveObservation("validate", async () => {
      const result = await validateOrder(orderId);
      updateActiveObservation({ output: { valid: result.valid } });
      return result;
    });

    if (!validated.valid) {
      updateActiveObservation({ output: { error: "validation failed" } });
      return { success: false };
    }

    // Generation span for LLM call
    const description = await startActiveObservation(
      { name: "generate-confirmation", asType: "generation" },
      async () => {
        updateActiveObservation({ model: "gpt-4o-mini" });
        const result = await generateConfirmation(orderId);
        updateActiveObservation({ output: result });
        return result;
      }
    );

    updateActiveObservation({ output: { success: true } });
    return { success: true, description };
  });
}

Pattern 4: Session and User Tracking

Link traces across conversation turns for user-level analytics:

// v4+: Set session/user via observation metadata
await startActiveObservation("chat-turn", async () => {
  updateActiveObservation({
    metadata: {
      sessionId: "session-abc-123",
      userId: "user-456",
    },
  });
  // All nested observations inherit this context
  await handleUserMessage(message);
});

// v3: Set directly on trace
const trace = langfuse.trace({
  name: "chat-turn",
  sessionId: "session-abc-123", // Groups traces into a session
  userId: "user-456",           // Links to user analytics
  input: { message },
});

Pattern 5: Error-Safe Tracing

Never let tracing failures break your application:

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

const safeObserve = <T extends (...args: any[]) => Promise<any>>(
  name: string,
  fn: T
): T => {
  return (async (...args: Parameters<T>) => {
    try {
      return await observe({ name }, async () => {
        updateActiveObservation({ input: args });
        const result = await fn(...args);
        updateActiveObservation({ output: result });
        return result;
      })();
    } catch (tracingError) {
      // If tracing fails, still run the function
      console.warn(`Tracing error in ${name}:`, tracingError);
      return fn(...args);
    }
  }) as T;
};

// Usage -- function works even if Langfuse is down
const processRequest = safeObserve("process-request", async (input: string) => {
  return await callLLM(input);
});

Pattern 6: Legacy v3 -- Always End Spans

// Always use try/finally to ensure .end() is called
const span = trace.span({ name: "risky-operation", input: data });
try {
  const result = await riskyOperation(data);
  span.end({ output: result });
  return result;
} catch (error) {
  span.end({ level: "ERROR", statusMessage: String(error) });
  throw error;
}

Anti-Patterns to Avoid

Resources

• TypeScript SDK Instrumentation
• Advanced SDK Configuration
• Python Decorators
• Event Queuing/Batching

Next Steps

For OpenAI/LangChain tracing examples, see langfuse-core-workflow-a.

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