Helicone Guides

Practical guides for building production-ready LLM applications with Helicone’s observability platform

What You’ll Learn

These guides provide step-by-step instructions for implementing key observability patterns in your LLM applications. Each guide focuses on practical implementation with real code examples.

Core Monitoring Patterns

Agent Tracing

Track complex agent workflows with tool calls, decision paths, and multi-step reasoning

Cost Tracking

Monitor spending, optimize costs, and understand unit economics across your AI stack

Debugging

Identify errors, diagnose issues, and optimize LLM application performance

Experiments

A/B test prompts and models with production data to improve response quality

Fine-Tuning

Prepare datasets and track fine-tuning workflows with OpenPipe integration

Step-by-Step Tutorials

Complete implementations showing how to integrate Helicone into real applications.

Vercel AI Gateway

Build a multi-model assistant with intelligent routing and cost optimization

RAGAS Evaluations

Implement comprehensive evaluation pipelines with Ragas metrics

Structured Outputs

Use OpenAI function calling and structured outputs with monitoring

Quick Navigation

Getting Started with Monitoring

Start with Agent Tracing to understand session-based monitoring, then move to Cost Tracking to optimize spending.

Improving Quality

Use Experiments to test prompt changes, and Fine-Tuning for specialized model behavior.

Production Optimization

Implement Debugging workflows and use the Vercel AI Gateway tutorial for production patterns.

Integration Patterns

All guides show integration with:

• OpenAI SDK (Python & TypeScript)
• Anthropic Claude
• Vercel AI SDK
• Helicone Manual Logger for custom integrations

Need Help?

Discord Community

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API Reference

Complete reference for Helicone’s REST API