nowfound

AI · alternatives · 2026

24 alternatives to 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 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… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1
    Trails▲81

    Hey 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…

    Jan 2026 · trails-red.vercel.app · its alternatives →

  2. 2

    I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…

    Apr 2026 · github.com · its alternatives →

  3. 3

    We 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…

    Mar 2026 · github.com · its alternatives →

  4. 4

    Optimize Agent Actions with User Behavior.

    Aug 2026 · kubit.ai · its alternatives →

  5. 5

    Never ship broken AI code again

    2025 · its alternatives →

  6. 6

    We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…

    Mar 2026 · github.com · its alternatives →

  7. 7

    Open-source LLM tracing for agent visibility

    Mar 2026 · breadcrumb.sh · its alternatives →

  8. 8
    Kadabra▲168

    Build data solutions/automate workflows with noCode/lowCode

    Nov 2025 · getkadabra.com · its alternatives →

  9. 9
    Kodus▲145

    Hey everyone, Just wanted to share a quick update we just launched at Kodus. For those who don’t know it yet, Kodus is a code review agent that runs directly in your team’s Git workflow (GitHub, GitLab, Bitbucket… and now Azure DevOps as well). It helps maintain code quality and consistency by analyzing each PR based on your team’s own rules and repository standards. Support for Azure had been a common request — so if your team uses it, you can now plug Kodus right into your workflow and give it a try. Docs: https://docs.kodus.io/how_to_use/en/overview Repo:…

    Oct 2025 · kodus.io · its alternatives →

  10. 10
    Rudel▲94

    Claude Code & Codex session analytics for dev teams

    Apr 2026 · rudel.ai · its alternatives →

  11. 11

    Single-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…

    Feb 2026 · github.com · its alternatives →

  12. 12

    Hi 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…

    2024 · github.com · its alternatives →

  13. 13
    Velane▲95

    n8n is built for humans dragging nodes on a canvas. That breaks down at B2B scale (embedding in a product, multi-tenant scalability, etc). n8n does have an MCP server so agents can create workflows too, but it outputs raw JSON. That's fine for n8n's engine, but painful for a coding agent (or a human reviewing its output) to read, write, diff, or debug. I'm building an alternative where workflows are authored by a coding agent in [a more dev-legible format] instead of JSON blobs, and execute it at scale.

    Jul 2026 · velane.sh · its alternatives →

  14. 14
    Flare▲120

    The graph-first IDE and interactive map for agentic coding

    Aug 2026 · github.com · its alternatives →

  15. 15

    Hi HN, I’m Michael, I built AgentGrid with my friend Souren because we were tired of losing our coding sessions and couldn't keep track of what was actually being built across our many projects. Our approach was to use an infinite canvas desktop app for the TUIs we already know and love, mainly claude + codex. We drew inspiration from Figma and Railway. Overtime we added image nodes, notes, terminals, coding editors, source control, etc. I wasn't sure about it at first. It took me a month to give the first version Souren built a real try. What got me was an Apple System Update I've been…

    Jul 2026 · agentgrid.sh · its alternatives →

  16. 16

    Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…

    2023 · github.com · its alternatives →

  17. 17
    Tracea▲80

    Datadog for AI agents with traces, RCA, and team memory

    May 2026 · tracea.dev · its alternatives →

  18. 18

    See what breaks your AI agent and fix it automatically

    Jan 2026 · docs.futureagi.com · its alternatives →

  19. 19
    klanex▲4

    Reliability layer for AI agent tool calls

    Aug 2026 · klanexai.com · its alternatives →

  20. 20

    Hi HN, I built this open-source LLM red teaming tool based on my experience scaling LLMs at a big co to millions of users... and seeing all the bad things people did. How it works: - Uses an unaligned model to create toxic inputs - Runs these inputs through your app using different techniques: raw, prompt injection, and a chain-of-thought jailbreak that tries to re-frame the request to trick the LLM. - Probes a bunch of other failure cases (e.g. will your customer support bot recommend a competitor? Does it think it can process a refund when it can't? Will it leak your user's address?) -…

    2024 · promptfoo.dev · its alternatives →

  21. 21

    Testim's root cause analysis helps save time troubleshooting failed tests. Tag failures by error type to see trends.

    2020 · testim.io · its alternatives →

  22. 22

    Visual debugging, tracing, and replay for agent workflows

    Apr 2026 · agenticlens.in · its alternatives →

  23. 23

    In this video, I demonstrate how our DevOps teams can efficiently handle errors using CloudShip without the hassle of copying, pasting, or switching contexts. I walk through a specific example involving Grafana dashboards and memory leak analysis, showcasing how we can automate these workflows. If y

    2025 · loom.com · its alternatives →

  24. 24
    Kayba▲17

    Make your agents self‑improve from experience

    Mar 2026 · kayba.ai · its alternatives →

Also compare

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →