Tracecore: Benchmark AI Agents on Deterministic Coding Tasks

Hacker News

I'm sharing Tracecore, an OS tool I'm building to evaluate AI agents' ability to handle deterministic software tasks like log triage, config remediation, and incident recovery. It started as a way to test whether agents could reliably perform structured operations without free-form guessing, inspired by frustrations with brittle automation in ops workflows. What sets it apart: Unlike benchmarks like SWE-Bench (which tests code generation on open-ended GitHub issues) or general agent evaluation suites (that mix diverse reasoning, coding, and interaction tasks), Tracecore focuses on deterministic episodes where agents must use constrained actions (e.g., file operations, ops triage) to achieve exact outcomes, with strict validation. It includes 15+ tasks across suites like operations and games, and supports running agents via adapters for frameworks like OpenClaw and Autogen, or custom scripts. You can try it out by installing through pip/uv, or by cloning the repo and installing the optional dev dependencies, and running the dashboard, the cli wizard or the cli commands. It outputs structured results with success/failure, steps used, traces for analysis, diffs, bundles and more. I've been iterating on this over the past few weeks, adding new tasks and improving the harness. Previous discussions on AI eval tools were helpful in shaping the design. Feedback welcome, especially on expanding task suites or integration ideas.

Visit product View on Hacker NewsFeb 26, 2026

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Cua-Bench – a benchmark for AI agents in GUI environments57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cua-Bench – a benchmark for AI agents in GUI environments

Hacker News40

WebGL Sprites Benchmark58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

WebGL Sprites Benchmark

Hacker News38

NAB – The Numenta Anomaly Benchmark42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

NAB – The Numenta Anomaly Benchmark

Hacker News17

NAB – The Numenta Anomaly Benchmark42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

NAB – The Numenta Anomaly Benchmark

Hacker News15

AgentMafia – A Social Deduction Benchmark38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AgentMafia – A Social Deduction Benchmark

Hacker News3

Manage coding norms across your AI agents31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Manage coding norms across your AI agents

Hacker News1

A New Implementation of the Seven GUIs Benchmark59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A New Implementation of the Seven GUIs Benchmark

Hacker News4

Webbench, a WASM Based Benchmark52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Webbench, a WASM Based Benchmark

Hacker News2

APIEval-2082%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An open benchmark for AI agents that test APIs

Product Hunt+121API

LLM Deceptiveness and Gullibility Benchmark43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LLM Deceptiveness and Gullibility Benchmark

Hacker News7

Browse all products Analyze your launch