Kelet – Root Cause Analysis agent for your LLM apps
I've spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions/day), and the hardest part was never building them — it was figuring out why they fail. AI agents don't crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern across hundreds of sessions. Kelet automates that investigation. Here's how it works: 1. You connect your traces and signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge, etc.) 2. Kelet processes those signals and extracts facts about each session 3. It forms hypotheses…
Kelet – Root Cause Analysis agent for your LLM apps launched on April 14, 2026 with 47 votes, #892 of 21,248 launches that month and more than 95% of that year's launches.
In plain words
Kelet is a root cause analysis tool designed for developers building LLM-based applications. It automates the investigation of agent failures by connecting traces and user signals, then clustering failure patterns across sessions to identify root causes. Rather than manually reviewing individual traces, teams can use Kelet to extract facts from sessions, form hypotheses about failures, and surface actionable fixes.
written from the facts on this page · September 2026
From the sources
Kelet continuously diagnoses why your LLM apps and AI agents fail in production, and generates the fix.
Kelet tracks down production failures in LLM apps and AI agents. It finds the root cause and hands you the fix. You just ship. No credit card required. Connect your first agent in 5 minutes. Open issues, agent health, and AI-generated brief at a glance. Every failure pattern classified, with evidence and a prompt patch ready to ship. Root Cause Analysis (RCA) + fix. No guesses. Evidence-backed. That's 30% of your engineering week. Another dashboard won't fix that. Kelet actually investigates. Constantly. Connect to your agent's stack in minutes. Every agent interaction, signal, and user feedback flows in automatically. Kelet reads every trace so you don't have to. Root causes surface in…from kelet.ai
In the maker’s words, at launch
I've spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions/day), and the hardest part was never building them — it was figuring out why they fail. AI agents don't crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern…
Does a similar job
all alternatives →
TrailsJan 2026 · trails-red.vercel.app · ▲81Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
Rudel – Claude Code Session AnalyticsMar 2026 · github.com · ▲144We built rudel.ai after realizing we had no visibility into our own Claude Code sessions. We were using it daily but had no idea which sessions were efficient, why some got abandoned, or whether we were actually improving over time. So we built an analytics layer for it. After connecting our own sessions, we ended up with a dataset of 1,573 real Claude Code sessions, 15M+ tokens, 270K+ interactions. Some things we found that surprised us: - Skills were only being used in 4% of our sessions - 26% of sessions are abandoned, most within the first 60 seconds - Session success rate varies…
Product Analytics for Agents and UsersAug 2026 · kubit.ai · ▲110Optimize Agent Actions with User Behavior.
20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
AI Agents for engineering use cases like debugging, LLD,testing etc.2024 · github.com · ▲26Hi I am Aditi and I co-founded Potpie AI with my college mate Dhiren. We are building an open-source infrastructure to create custom agents for engineering use-cases like debugging, system design, integration testing, PR review etc. The agents are powered by a knowledge graph built on your code base to provide better context and memory, leading to better planning and execution. Currently we offer 6 ready-to-use agents but you can also build your custom agents. You can tune agent parameters like purpose, goals, background etc. and they are also empowered by pre-built tooling like code…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 21d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, April 2026
the whole month →
- AG
Thought the resources for GPU arch were lacking, so here we are
Life & fun · Apr 2026 · jaso1024.com
Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
AI · Apr 2026 · github.com
I made a laptop stand out of solid concrete, in the style of brutalist architecture, with themes of urbex and urban decay
Life & fun · Apr 2026 · sam-burns.com
With social media and now AI, its important to keep the indie web alive. There are many people who write frequently. Blogosphere tries to highlight them by fetching the recent posts from personal blogs across many categories. There are two versions: Minimal (HN-inspired, fast, static): https://text.blogosphere.app/ Non-minimal: https://blogosphere.app/ If you don't find your blog (or your favorite ones), please add them. I will review and approve it.
AI · Apr 2026 · text.blogosphere.app