Helicone

Helicone – LLM-Observability for Developers is an all-in-one platform to monitor, debug, and improve production-ready LLM applications. It provides a comprehensive suite to observe prompts, segments, sessions, datasets, and model interactions across integrations, enabling developers to optimize performance, reliability, and safety. The platform emphasizes visibility into AI apps, fast iteration, and collaboration, with support for OpenAI, Anthropic, Azure, LiteLLM, Anyscale, Together AI, OpenRouter, and more.

How Helicone Helps

• End-to-end observability for LLM apps: capture prompts, responses, routes, and system signals to diagnose issues and improve prompts.
• Prompt optimization and experimentation: run experiments, compare prompts, and tune system and user messages for better outcomes.
• Debugging and tracing: segment sessions, track properties, and inspect evaluation results to identify bottlenecks and failures.
• Data governance and safety: manage datasets and evaluators to ensure responsible use and reproducibility.
• Developer-friendly: integrates with major LLM providers and supports scalable, production-ready deployments.

How to Use Helicone

1. Connect your LLM provider (OpenAI, Anthropic, Azure, etc.) to start capturing data from your applications.
2. Instrument prompts and responses: log prompts, responses, model choices, and evaluation signals for each session.
3. Create experiments and evaluators: define prompts, templates, and evaluation criteria to test improvements and compare results.
4. Inspect dashboards: review Segments, Sessions, Properties, and Datasets to understand performance and behavior.
5. Iterate and deploy: refine prompts and workflows based on insights, then monitor in production.

Integrations & Ecosystem

• OpenAI
• Anthropic
• Azure
• LiteLLM
• Anyscale
• Together AI
• OpenRouter
• Other providers

Core Features

• Comprehensive LLM observability across prompts, responses, and sessions
• Prompt experimentation tooling with templates and evaluators
• Session and segment-level debugging for root-cause analysis
• Datasets and evaluators to support safe and reproducible AI workflows
• Multi-provider integrations for broad compatibility
• Experimentation playgrounds for rapid iteration
• Production-grade monitoring to improve reliability and performance
• Deployment-ready observability with quick setup and onboarding