Langfuse SDK Patterns

Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

Overview

Best practices and idiomatic patterns for using the Langfuse SDK, covering singleton clients, trace lifecycle, nested spans, decorators, sessions, and scoring.

Prerequisites

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

Instructions

Step 1: Use Singleton Client

Create a single Langfuse instance reused everywhere. Register shutdownAsync() for clean exit.

Step 2: Manage Trace Lifecycle

Create trace at operation start. Update with output on success. Update with error level on failure. Never await flush in hot paths.

Step 3: Nest Spans for Complex Operations

Use parent/child spans: validation span, retrieval span, generation for LLM calls. Always call .end().

Step 4: Use Python Decorators

Use @observe() for automatic span creation. Use @observe(as_type="generation") for LLM calls. Access context via langfuse_context.

Step 5: Track Sessions and Users

Link traces across conversation turns using sessionId. Set userId for user-level analytics.

Step 6: Add Evaluation Scores

Score traces with numeric values (0-1) for accuracy, relevance, and user feedback.

See detailed implementation for advanced patterns.

Output

• Singleton client pattern
• Proper trace lifecycle management
• Nested span hierarchy
• Python decorator-based tracing
• Session tracking and scoring

Error Handling

Examples

Basic usage: Apply langfuse sdk patterns to a standard project setup with default configuration options.

Advanced scenario: Customize langfuse sdk patterns for production environments with multiple constraints and team-specific requirements.

Resources

• Langfuse Tracing Guide
• Langfuse Python Decorators
• Langfuse Scores