LangFuse: LLM Observability, Tracing, Evaluation, Monitoring

A warm welcome to LangFuse for LLMOps: Observability, Tracing, Evaluation & Monitoring course by Uplatz.

LangFuse is an open-source LLM observability and evaluation platform that helps AI engineers trace, monitor, debug, evaluate, and optimize production-grade AI, RAG, and agentic applications.

Large Language Models (LLMs) have transformed the way modern applications are built, but deploying AI systems into production introduces new challenges around visibility, debugging, monitoring, evaluation, cost control, and reliability. Traditional monitoring tools are not designed to understand prompts, model responses, retrieval pipelines, agent workflows, or AI-specific performance metrics.

This course provides a comprehensive, hands-on introduction to LangFuse, one of the leading open-source platforms for LLM observability, tracing, prompt management, evaluation, and production monitoring. You will learn how to instrument AI applications, capture traces, analyze model behavior, monitor token usage and costs, evaluate response quality, and build reliable AI systems that can be confidently deployed at scale.

Starting from the fundamentals, you will explore LangFuse architecture, traces, spans, observations, sessions, and observability concepts before moving into practical implementation using the LangFuse SDK and OpenAI-compatible APIs. Through real-world examples, you will learn how to monitor LLM interactions, manage prompt versions, enrich traces with metadata, collect feedback, perform evaluations, and identify performance bottlenecks.

The course also covers advanced topics including observability for Retrieval-Augmented Generation (RAG) applications, agentic AI systems, production readiness, self-hosting strategies, CI/CD integration, testing workflows, and common implementation mistakes. Finally, you will build a complete mini-project where you instrument a real AI application and apply industry best practices for monitoring, debugging, and optimization.

Whether you are building chatbots, AI copilots, RAG systems, AI agents, or enterprise-grade GenAI applications, this course will equip you with the practical skills needed to monitor, evaluate, and improve AI systems in production.

What You Will Learn

• Understand LangFuse architecture and core observability concepts.

• Deploy and configure LangFuse using Docker Compose.

• Instrument LLM applications using the LangFuse SDK.

• Capture and analyze traces, spans, and observations.

• Monitor token consumption, latency, and operational costs.

• Implement prompt versioning and prompt management workflows.

• Add metadata and structured context to AI interactions.

• Build evaluation pipelines using scores, feedback, and quality metrics.

• Debug failed, incomplete, and underperforming AI workflows.

• Monitor RAG pipelines and agentic AI systems.

• Integrate LangFuse into testing and CI/CD workflows.

• Apply production-grade monitoring and observability practices.

Why Learn LangFuse?

As organizations increasingly deploy AI applications into production, observability and monitoring have become critical requirements. LangFuse has emerged as a leading platform for understanding how AI systems behave, helping teams improve reliability, reduce costs, accelerate debugging, and continuously enhance application quality. Learning LangFuse provides valuable skills for AI Engineering, GenAI Engineering, LLMOps, MLOps, Platform Engineering, and AI Architecture roles.

By the end of this course, you will have the knowledge and practical experience required to implement enterprise-grade observability and monitoring for modern AI applications.

This course is ideal for professionals building, monitoring, and optimizing production AI applications. LangFuse skills are particularly valuable in roles focused on LLMs, GenAI, observability, evaluation, and AI operations.

• AI Engineer – Build and deploy AI-powered applications using LLMs and modern AI frameworks.

• Generative AI Engineer – Develop chatbots, copilots, RAG systems, and agentic AI solutions.

• LLM Engineer – Design, optimize, evaluate, and monitor Large Language Model applications.

• LLMOps Engineer – Manage observability, tracing, evaluation, monitoring, and AI operations in production.

• Machine Learning Engineer – Deploy and maintain ML and AI systems with production-grade monitoring.

• AI Platform Engineer – Build and support enterprise AI platforms, tooling, and infrastructure.

• AI Solutions Architect – Design scalable, reliable, and observable AI solutions for organizations.

LangFuse for LLMOps: Observability, Tracing, Evaluation & Monitoring - Course Curriculum

Module 1: Course Foundation and Environment Setup

Lesson 1: Course Orientation and Environment Validation

• Course Overview and Learning Outcomes

• Understanding the LangFuse Ecosystem

• Prerequisites and Development Requirements

• Environment Validation Checklist

Lesson 2: LangFuse Architecture Overview (Mental Model First)

• What is LangFuse?

• Core Components and Architecture

• Data Flow in LLM Observability

• Mental Models for Tracing and Monitoring

Lesson 3: Preparing the Local Workspace (WSL2-Only)

• Setting up WSL2 Environment

• Installing Required Dependencies

• Development Environment Preparation

• Workspace Validation

Lesson 4: Deploying LangFuse with Docker Compose (Local)

• LangFuse Deployment Architecture

• Docker Compose Configuration

• Running LangFuse Locally

• Verifying Deployment and Services

Lesson 5: First Login, Organization, and Project Setup

• Initial Login and Configuration

• Organizations and Projects

• API Keys and Authentication

• Basic Platform Navigation

Module 2: Core Observability and Instrumentation

Lesson 6: Understanding Traces, Spans, and Observations

• Introduction to Traces

• Understanding Spans

• Observations and Event Tracking

• Observability Fundamentals

Lesson 7: LangFuse SDK Basics (Python Example)

• LangFuse Python SDK Introduction

• SDK Installation and Configuration

• Creating Basic Traces

• Logging and Monitoring Examples

Lesson 8: Instrumenting LLM Calls (OpenAI-Style APIs)

• Capturing LLM Requests and Responses

• OpenAI-Compatible Integrations

• Monitoring Token Usage

• Tracking Model Performance

Module 3: Prompt Management and Metadata

Lesson 9: Prompt Versioning and Prompt Management

• Prompt Lifecycle Management

• Prompt Version Control

• Managing Prompt Iterations

• Best Practices for Production Prompts

Lesson 10: Metadata and Structured Context

• Working with Metadata

• Custom Attributes and Tags

• Context Enrichment

• Structured Observability Patterns

Module 4: Evaluation, Feedback, and Analytics

Lesson 11: Scores, Feedback, and Evaluation

• Evaluation Frameworks

• Human Feedback Collection

• Quality Scoring Mechanisms

• Building Evaluation Pipelines

Lesson 12: Cost, Latency, and Performance Analysis

• Cost Tracking and Optimization

• Latency Monitoring

• Throughput and Performance Metrics

• Production Performance Analysis

Lesson 13: Debugging Failed or Partial Traces

• Common Trace Failures

• Root Cause Analysis

• Debugging Techniques

• Observability Troubleshooting Workflows

Module 5: LangFuse for Advanced AI Architectures

Lesson 14: LangFuse in Agentic Architectures

• Observability for AI Agents

• Multi-Step Agent Tracing

• Tool Calling Visibility

• Agent Workflow Monitoring

Lesson 15: Production Readiness and Safety

• Production Monitoring Strategies

• Reliability and Governance

• Safety and Risk Monitoring

• Operational Best Practices

Lesson 16: Self-Hosting Considerations

• Cloud vs Self-Hosted Deployment

• Infrastructure Requirements

• Scalability Considerations

• Security and Compliance

Module 6: Enterprise Integration and Operations

Lesson 17: LangFuse with CI/CD and Testing

• Observability in Development Pipelines

• Automated Testing Workflows

• CI/CD Integration Patterns

• Quality Gates and Monitoring

Lesson 18: Common Anti-Patterns and Mistakes

• Frequent Implementation Errors

• Observability Pitfalls

• Monitoring Blind Spots

• Lessons Learned from Production Systems

Module 7: Hands-On Industry Project

Lesson 19: Mini Project – Instrumenting a Real LLM Application

• Designing an Observable LLM Application

• End-to-End Instrumentation

• Monitoring User Interactions

• Performance and Evaluation Analysis

Module 8: Course Wrap-Up and Next Steps

Lesson 20: Course Conclusion

• Operational Maturity Model for LLM Applications

• Long-Term Observability Strategy

• Scaling Monitoring Practices

• Professional Deployment Discipline

• Next Learning Pathways and Resources