graph LR
subgraph DE ["Application Environment"]
A[Your App] --> B[LangFuse SDK]
end
subgraph SH ["Self-Hosted in AKS"]
C[LangFuse Server]
D[Database]
E[Web UI]
C --> D
C --> E
end
B --> C
What is LangFuse?
Concepts
LangFuse is an open-source observability platform specifically designed for Large Language Model (LLM) applications. It provides comprehensive tracing, monitoring, and analytics capabilities that help developers understand and optimize their AI-powered systems.
Core Capabilities
🔍 Tracing & Observability
- Detailed Execution Traces - See exactly how your AI workflows execute
- Nested Span Tracking - Monitor complex, multi-step processes
- Real-time Monitoring - Live visibility into application performance
- Error Tracking - Identify and diagnose issues quickly
📊 Analytics & Insights
- Performance Metrics - Latency, throughput, and success rates
- Cost Tracking - Monitor token usage and API costs
- Usage Patterns - Understand how your applications are being used
- Quality Metrics - Track model performance and output quality
🛠️ Developer Experience
- Easy Integration - Simple Python SDK with decorator-based tracing
- Flexible Deployment - Cloud-hosted or self-hosted options
- Rich Dashboard - Intuitive web interface for exploring traces
- Open Source - Full transparency and community-driven development
Why LangFuse at Justice AI?
Regulatory Compliance
- Audit Trails - Complete records of AI decision-making processes
- Data Sovereignty - Self-hosted deployment keeps data within MoJ infrastructure
- Transparency - Clear visibility into how AI systems operate
Operational Excellence
- Proactive Monitoring - Catch issues before they impact users
- Performance Optimization - Identify bottlenecks and optimization opportunities
- Cost Management - Track and optimize AI-related expenses
Quality Assurance
- Model Behavior Analysis - Understand how models perform across different scenarios
- Prompt Engineering - Iterate and improve prompts based on real usage data
- A/B Testing - Compare different approaches and configurations
Common Use Cases
Application Development
- RAG Systems - Monitor retrieval accuracy and generation quality
- Chatbots - Track conversation flows and response quality
- Document Processing - Trace multi-stage document analysis workflows
- Decision Support - Monitor AI-assisted decision-making processes
DevOps & MLOps
- CI/CD Integration - Automated testing and validation of AI components
- Model Deployment - Monitor model performance in production
- Incident Response - Quick diagnosis and resolution of AI-related issues
- Capacity Planning - Understand resource requirements and scaling needs
Getting Started
Ready to add observability to your AI applications?
- Quick Start Guide - Learn by doing (Tutorial)
- Basic Python Tracing - Step-by-step learning (Tutorial)
Where to Go Next
Based on your experience level and goals:
New to LangFuse? 1. Quick Start - Get tracing working in 5 minutes (Tutorial) 2. Basic Python Patterns - Learn by building (Tutorial)
Ready to Implement? 1. Python SDK Guide - Full SDK capabilities (How-to guide) 2. OpenTelemetry Guide - Standardized tracing (How-to guide) 3. Raw Requests Guide - Troubleshooting with HTTP (How-to guide)
Deploy Your Own Instance? 1. Azure Deployment - Deploy LangFuse on Azure (How-to guide) 2. SSL Certificates - Configure trusted certificates (How-to guide) 3. Email Notifications - Enable user invitations (How-to guide)
Need Help? - Deployment Troubleshooting - Common issues and solutions
Architecture Overview
LangFuse operates as a separate service that receives trace data from your applications, providing a centralized platform for monitoring and analysis while keeping your core application logic unchanged.