Tracium.ai
Hi everyone, thanks for checking out tracium. At work I needed some clarity on how much each agent costs us and how they perform on production so I started looking at existing solutions for this such as LangSmith and LangFuse. Honestly, they felt like they are overcomplicated to integrate and that there should be a simpler solution so I decided to build Tracium. Tracium keeps track of every single AI API call that you make to an LLM provider, with this you can see traces, spans, I/O, tokens in/out, latency. You can compare during A/B testing, see usage from each customer and…
Tracium.ai launched on March 19, 2026 with 70 votes, #665 of 23,380 launches that month and more than 95% of that year's launches.
What it does
Tracium is a developer-first observability layer for AI systems. It lets you track costs, trace requests, debug failures, compare prompts and models, and detect drift in real time, all across models, tools, and agents, in one place. Useful AI observability without complex setup, availabile in minutes and built for developers.
AI Evaluation Platform
Useful AI observability without complex setup, available in minutes and built for developers. "One line of code gave us real visibility into AI costs, performance, and errors. I might be biased, but it’s a great tool." Tracium is a developer-first observability layer for AI systems. It lets you track costs, trace requests, debug failures, compare prompts and models, and detect drift in real time, all across models, tools, and agents, in one place. Track token spend and infra cost across models, agents, and workflows in real time. Capture, classify, and replay failures across model calls, tools, and agent steps. Trace every request end-to-end, including tool hops, agent steps, and model…from tracium.ai
In the maker’s words, at launch
Hi everyone, thanks for checking out tracium. At work I needed some clarity on how much each agent costs us and how they perform on production so I started looking at existing solutions for this such as LangSmith and LangFuse. Honestly, they felt like they are overcomplicated to integrate and that there should be a…
Does a similar job
all alternatives →
TracciaAug 2026 · traccia.ai · ▲197AI applications are becoming agents, which has started to take autonomous decisions. There are plenty of tools and platform available to trace, and observe what an agent or llms calls does. They are good in what they do, but tracing and observability isnt enough for AI agents era. We need a solution that can help you observe, evaluate, create run time policies to govern and finally audit the actions of the agent. We built Traccia to solve this problem. The good part, all of these can be achieved by just writing few lines of code. Traccia has an open-sourced sdk that can work with your…
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
AI Observability by OpenObserveAug 2026 · openobserve.ai · ▲317OpenTelemetry-native observability for agents and LLMs
Atomic2023 · github.com · ▲140Hey HN, I’ve been building Atom (https://github.com/rush86999/atom), an open-source, self-hosted AI automation platform. I built this because while tools like OpenClaw are excellent for one-off scripts and personal tasks, I found them difficult to use for complex business workflows (e.g., managing invoices or SaaS ops). The main issue was State Blindness: the agent would fire a command and assume it worked, without "seeing" if the UI or state actually updated. I just shipped a new architecture to solve this called Canvas AI Accessibility. The Technical Concept: Instead of…
- CGChecksum – generate and maintain end-to-end tests using AI2023 · ▲78
Hey HN! I’m Gal, co-founder at Checksum (https://checksum.ai). Checksum is a tool for automatically generating and maintaining end-to-end tests using AI. I cut my teeth in applied ML in 2016 at a maritime tech company called TSG, based in Israel. When I was there, I worked on a cool product that used machine learning to detect suspicious vehicles. Radar data is pretty tough for humans to parse, but a great fit for AI – and it worked very well for detecting smugglers, terrorist activity, and that sort of thing. In 2021, after a few years working in big tech (Lyft, Google), I joined…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Product Hunt launch ↗ · launch text quoted in part
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
Littlebird▲709Littlebird remembers everything across your meetings, messages, docs, and apps, then takes action so you can recall any detail and never lose context.
AI · Mar 2026 · littlebird.ai
