Hey Product Hunt! I'm Jyotishmoy, the maker of Tracellm. Like many developers, I've been building AI applications using different LLMs and frameworks. One thing quickly became obvious: once an AI app reaches production, it's surprisingly difficult to understand what's actually happening. Questions like: Why did this request fail? Which prompt caused the issue? How many tokens did this interaction consume? Where is the latency coming from? Which model call is slowing everything down? There are great observability tools for traditional applications, but I wanted something purpose-built for AI workloads. That's why I built Tracellm. Tracellm gives developers complete visibility into their AI applications by tracing prompts, spans, token usage, latency, model calls, and errors in one place. It also supports OpenTelemetry (OTLP), making it easy to integrate with your existing observability stack. This is just the beginning. I have a lot more planned, including richer analytics, cost optimization insights, and support for more AI frameworks and providers. I'd genuinely love your feedback: What features would make this indispensable for your workflow? Which AI framework or model provider should I support next? What would you like to see improved? Thanks so much for checking out Tracellm! I'm excited to answer your questions throughout the launch.

TraceLLM

OpenTelemetry for production AI applications

OpenTelemetry for production AI applications