#4 · Primary category: AI Usage & Cost Monitoring

helicone

agent-monitoring analytics evaluation gpt langchain large-language-models llama-index llm llm-cost llm-evaluation llm-observability llmops monitoring open-source openai playground prompt-engineering prompt-management ycombinator

🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓

Project last updated:08/26/26

GitHub Stars

6.1K

Forks

661

Contributors

101

License

Apache-2.0

Why we included this project

Most teams only discover how much a model-backed feature costs when the provider invoice lands. Helicone fixes that blind spot by letting you point your existing OpenAI, Anthropic, or LangChain client at its dashboard: change the base URL and every request, response, token count, and latency figure starts flowing into a readable interface. You can trace individual agent sessions, watch spend and response times per provider or prompt template, and run evaluations on the traffic you've already logged rather than a separate test set. It also works as a thin gateway, so a single endpoint can reach more than a hundred models with automatic fallbacks if a provider degrades. For a small team that wants production visibility and predictable cost control without assembling its own telemetry stack, this self-hostable project covers most of that work in one deploy.

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