Tutorials

Note

Learning-oriented documentation

These tutorials are designed to help you learn by doing. They take you through a series of steps to complete a meaningful project, helping you understand how the HoneyHive SDK works in practice.

Welcome to the HoneyHive Python SDK tutorials! If you’re new to HoneyHive or LLM observability, start here.

What you’ll learn:

• How to set up and configure the HoneyHive SDK

• How to trace your first LLM application

• How to integrate with popular LLM providers

• How to enrich traces with metrics, feedback, and metadata

• How to run experiments and evaluate your LLM outputs

• How to implement distributed tracing across microservices

Prerequisites:

• Python 3.11 or higher

• Basic familiarity with Python programming

• A HoneyHive account (sign up at honeyhive.ai)

Getting Started Path

Follow these tutorials in order for the best learning experience:

• 1. Set Up Your First Tracer
• 2. Add LLM Tracing in 5 Minutes
• 3. Enable Span Enrichment
• 4. Configure Multi-Instance Tracers
• 5. Tutorial 5: Run Your First Experiment
• 6. End-to-End Distributed Tracing

Additional Learning

Once you’ve completed the core tutorials, explore these additional topics:

• Advanced Configuration Guide

Next Steps

After completing these tutorials:

• Check out How-to Guides for solving specific problems

• Explore API Reference for detailed API documentation

• Read Explanation to understand concepts and design decisions

Need Help?

• Join our Discord community

• Check the How-to Guides guide (Troubleshooting section)

• Email support@honeyhive.ai