langfuse-sdk-patterns

name	langfuse-sdk-patterns
description	Langfuse SDK best practices, patterns, and idiomatic usage. Use when learning Langfuse SDK patterns, implementing proper tracing, or following best practices for LLM observability. Trigger with phrases like "langfuse patterns", "langfuse best practices", "langfuse SDK guide", "how to use langfuse", "langfuse idioms".
allowed-tools	Read, Write, Edit
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Langfuse SDK Patterns

Overview

Best practices and idiomatic patterns for using the Langfuse SDK effectively.

Prerequisites

• Completed langfuse-install-auth setup
• Understanding of async/await patterns
• Familiarity with LLM application structure

Core Concepts

Trace Hierarchy

Trace (top-level, represents complete operation)
├── Span (child operation, any processing step)
│   ├── Span (nested spans allowed)
│   └── Generation (LLM call)
├── Generation (LLM call)
└── Event (point-in-time occurrence)

Instructions

Pattern 1: Singleton Client Instance

// lib/langfuse.ts - Single instance, reused everywhere
import { Langfuse } from "langfuse";

let langfuseInstance: Langfuse | null = null;

export function getLangfuse(): Langfuse {
  if (!langfuseInstance) {
    langfuseInstance = new Langfuse({
      publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
      secretKey: process.env.LANGFUSE_SECRET_KEY!,
      baseUrl: process.env.LANGFUSE_HOST,
    });
  }
  return langfuseInstance;
}

// Clean shutdown
export async function shutdownLangfuse(): Promise<void> {
  if (langfuseInstance) {
    await langfuseInstance.shutdownAsync();
    langfuseInstance = null;
  }
}

Pattern 2: Proper Trace Lifecycle

import { getLangfuse } from "./lib/langfuse";

async function handleRequest(request: Request) {
  const langfuse = getLangfuse();

  // 1. Create trace at operation start
  const trace = langfuse.trace({
    name: "api/chat",
    userId: request.userId,
    sessionId: request.sessionId,
    input: request.body,
    metadata: {
      endpoint: "/api/chat",
      method: "POST",
    },
  });

  try {
    // 2. Do work with spans/generations
    const result = await processRequest(trace, request);

    // 3. Update trace with success output
    trace.update({
      output: result,
      metadata: { status: "success" },
    });

    return result;
  } catch (error) {
    // 4. Update trace with error
    trace.update({
      output: { error: String(error) },
      level: "ERROR",
      statusMessage: String(error),
    });
     error;
  }
  
  
}

Pattern 3: Nested Spans for Complex Operations

async function processRequest(
  trace: ReturnType<typeof langfuse.trace>,
  request: Request
) {
  // Parent span for the entire process
  const processSpan = trace.span({
    name: "process-request",
    input: request.body,
  });

  // Child span for validation
  const validateSpan = processSpan.span({
    name: "validate-input",
    input: request.body,
  });

  const validatedInput = await validateInput(request.body);
  validateSpan.end({ output: validatedInput });

  // Child span for retrieval (e.g., RAG)
  const retrieveSpan = processSpan.span({
    name: "retrieve-context",
    input: { query: validatedInput.query },
  });

  const context = await retrieveContext(validatedInput.query);
  retrieveSpan.end({
    output: { documentCount: context.length },
    metadata: { source: "vector-db" },
  });

  
   generation = processSpan.({
    : ,
    : ,
    : {
      : (validatedInput, context),
    },
  });

   response =  (validatedInput, context);

  generation.({
    : response.,
    : {
      : response..,
      : response..,
    },
  });

  processSpan.({ : response });
   response;
}

Pattern 4: Decorators for Clean Code (Python)

from langfuse.decorators import observe, langfuse_context

@observe()
def process_request(user_input: str) -> str:
    """Automatically creates a trace with function name."""
    # Add metadata to current observation
    langfuse_context.update_current_observation(
        metadata={"input_length": len(user_input)}
    )

    validated = validate_input(user_input)
    context = retrieve_context(validated)
    response = generate_response(validated, context)

    return response

@observe()
def validate_input(user_input: str) -> dict:
    """Creates a child span automatically."""
    return {"query": user_input.strip(), "valid": True}

@observe()
def retrieve_context(query: dict) -> list:
    """Another child span."""
    langfuse_context.update_current_observation(
        metadata={"query": query}
    )
    return ["context1", "context2"]

@observe(as_type="generation")
def generate_response(query: dict, context: list) -> str:
    """Creates a generation observation."""
    langfuse_context.update_current_observation(
        model=,
        model_parameters={: },
        usage={: , : },
    )
     

Pattern 5: Session and User Tracking

// Track conversations across multiple requests
function createConversationTrace(
  userId: string,
  sessionId: string,
  turn: number
) {
  return langfuse.trace({
    name: "conversation-turn",
    userId,
    sessionId, // Links traces into a session view
    metadata: {
      turn,
      timestamp: new Date().toISOString(),
    },
    tags: ["conversation"],
  });
}

// Usage
const trace = createConversationTrace(
  "user-123",
  "session-abc", // Same session across turns
  3 // Turn number
);

Pattern 6: Scores and Evaluation

// Add scores to traces for evaluation
const trace = langfuse.trace({ name: "scored-operation" });

// After operation completes, add scores
langfuse.score({
  traceId: trace.id,
  name: "accuracy",
  value: 0.95, // Numeric score
  comment: "High accuracy response",
});

langfuse.score({
  traceId: trace.id,
  name: "user-feedback",
  value: 1, // Boolean as 0/1
  comment: "User thumbs up",
});

langfuse.score({
  traceId: trace.id,
  observationId: generation.id, // Score specific generation
  name: "relevance",
  value: 0.8,
});

Output

• Singleton client pattern for consistent tracing
• Proper trace lifecycle management
• Nested spans for complex operations
• Clean decorator-based tracing (Python)
• Session and user tracking
• Evaluation and scoring integration

Error Handling

Pattern	Issue	Best Practice
Client creation	Multiple instances	Use singleton pattern
Trace updates	Missing outputs	Always update on success/error
Span nesting	Orphaned spans	Always call .end()
Flush timing	Lost data	Use shutdownAsync() on exit
Scoring	Invalid values	Use 0-1 range for consistency

Examples

Complete TypeScript Pattern

import { Langfuse } from "langfuse";

const langfuse = new Langfuse();

interface ChatRequest {
  userId: string;
  sessionId: string;
  message: string;
}

async function chat(request: ChatRequest) {
  const trace = langfuse.trace({
    name: "chat",
    userId: request.userId,
    sessionId: request.sessionId,
    input: { message: request.message },
  });

  const span = trace.span({ name: "process" });

  try {
    const generation = span.generation({
      name: "llm-call",
      model: "gpt-4",
      input: [{ role: "user", content: request.message }],
    });

    const response = await callOpenAI(request.message);

    generation.({
      : response.,
      : response.,
    });

    span.({ : { : response. } });
    trace.({ : { : response. } });

     response.;
  }  (error) {
    span.({ : , : (error) });
    trace.({ : , : { : (error) } });
     error;
  }
}

Resources

• Langfuse Tracing Guide
• Langfuse Python Decorators
• Langfuse Scores

Next Steps

For core tracing workflows, see langfuse-core-workflow-a.