Langfuse is a comprehensive observability platform built specifically for Large Language Model (LLM) applications. It provides developers with powerful tools to monitor, analyze, and optimize their AI models and applications in production environments.

Key Features

• LLM Monitoring: Track and analyze your LLM applications’ performance, usage, and behavior in real-time.
• Cost Analytics: Monitor and optimize spending across different LLM providers and models.
• Prompt Management: Version control and analyze the effectiveness of different prompts.
• Performance Metrics: Track latency, token usage, and other key performance indicators.
• A/B Testing: Compare different models and prompts to optimize performance.
• Error Tracking: Identify and debug issues in your LLM applications quickly.
• API Integration: Easy integration with popular LLM frameworks and languages.
• Self-Hosted Option: Deploy Langfuse on your own infrastructure for complete control.

Why Choose Langfuse?

• Specialized for LLMs: Built specifically for the unique challenges of LLM applications.
• Open Source: Transparent development and community-driven improvements.
• Developer-Friendly: Easy integration with existing workflows and tools.
• Comprehensive Analytics: Deep insights into your LLM applications’ performance.
• Cost Optimization: Tools to manage and optimize LLM-related expenses.

Use Cases

• Production Monitoring: Track the performance and reliability of LLM applications in production.
• Prompt Optimization: Test and refine prompts to improve model outputs.
• Cost Management: Monitor and optimize spending across different LLM providers.
• Quality Assurance: Ensure consistent and high-quality model outputs.
• Performance Analysis: Analyze and improve response times and efficiency.

Getting Started

To begin using Langfuse, visit their official website and follow the setup instructions. Whether you’re managing a production LLM application or developing new AI features, Langfuse provides the tools needed to understand and improve your AI systems.

Docker Compose File

services:
  langfuse-db:
    image: postgres:16-alpine
    container_name: LangFuse-DB
    hostname: langfuse-db
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]
      interval: 5s
      timeout: 5s
      retries: 5
    volumes:
      - ./langfuse-db:/var/lib/postgresql/data:rw
    environment:
      POSTGRES_DB: ${POSTGRES_DB}
      POSTGRES_USER: ${POSTGRES_USER}
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
    restart: on-failure:5

  langfuse:
    image: langfuse/langfuse:latest
    container_name: LangFuse
    ports:
      - 5061:3000
    restart: unless-stopped
    depends_on:
      - langfuse-db
    environment:
      DATABASE_URL: postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@langfuse-db:5432/${POSTGRES_DB}?sslmode=disable
      NEXTAUTH_URL: https://longfuse.bitdoze.com
      NEXTAUTH_SECRET: ${NEXTAUTH_SECRET}
      SALT: ${SALT}
      LANGFUSE_CSP_ENFORCE_HTTPS: true
      LANGFUSE_ENABLE_EXPERIMENTAL_FEATURES: false
      TELEMETRY_ENABLED: false
      AUTH_DISABLE_SIGNUP: ${AUTH_DISABLE_SIGNUP}

Langfuse is more than just a monitoring tool; it’s a complete platform for understanding and optimizing your LLM applications. Its open-source nature and specialized features make it an essential tool for any team working with AI and language models in production.