Tracellm is an observability platform for production AI applications. Monitor prompt execution, token consumption, latency, spans, errors, and model calls across your LLM workflows. Export traces using OpenTelemetry (OTLP) and quickly identify bottlenecks before they impact users
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.
please give it a "⭐" in GitHub if you find this helpful.
OTLP as the export path is the right bet, but it moves the hard problem rather than solving it. The moment a prompt body leaves for someone's existing stack it lands under retention and access rules it was never collected under, so the first thing I'd want to know is whether payload capture is opt in per span and whether I can strip it at the collector rather than in the SDK. Worth answering on the page, because that's the question that decides whether this gets past a security review or dies in one.
The redaction point further up is the one that decides whether we could even turn this on. Full request and response bodies leaving our infra to a third party is a hard no on anything finance adjacent unless PII gets scrubbed before export, not after. Is that per field configurable or a fixed blanket list?
This looks super useful — OpenTelemetry-based observability for LLM apps is a great idea, especially catching bottlenecks early. Congrats on shipping!
The thing I keep wanting from LLM tracing is MCP tool calls as first class spans, not just the model call with a blob of tool JSON hanging off it. Half my latency is the tools, not the model. Does the OTLP export break tool calls out as child spans?
Every question on that list is about a failure that announces itself. Failed request, slow call, token spike, all visible.
The one that costs most in production is the request that succeeded. No error, normal latency, sensible token count, and the answer was confidently wrong. To catch that afterwards the trace has to hold what the turn was grounded in, which retrieval actually landed and what the tool returned, attributed to that turn. Otherwise the worst incident of the year looks like a healthy span.
Does a span carry the retrieved content and tool results, or the call and the timing only?
About TraceLLM on Product Hunt
“OpenTelemetry for production AI applications”
TraceLLM launched on Product Hunt on July 31st, 2026 and earned 108 upvotes and 14 comments, placing #11 on the daily leaderboard. Tracellm is an observability platform for production AI applications. Monitor prompt execution, token consumption, latency, spans, errors, and model calls across your LLM workflows. Export traces using OpenTelemetry (OTLP) and quickly identify bottlenecks before they impact users
TraceLLM was featured in Open Source (68.7k followers), Developer Tools (517.2k followers), GitHub (41.4k followers) and Tech (629.6k followers) on Product Hunt. Together, these topics include over 286.5k products, making this a competitive space to launch in.
Who hunted TraceLLM?
TraceLLM was hunted by Jyotishmoy Deka. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Jyotishmoy Deka