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AI · alternatives · 2026

24 alternatives to Leaping – Debug Python tests instantly with an LLM debugger

Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - https://news.ycombinator.com/item?id=39528087 As we were building the root-cause phase of our automated debugger, we realized that we developed…

Leaping is a pytest plugin that uses an LLM to debug Python tests by analyzing execution logs in natural language. Developers can interact with it conversationally to understand variable states and behavior over time… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Leaping – Debug Python tests instantly with an LLM debugger launched in 2024; newer entries below may have overtaken it.

  1. 1LO

    Show HN: Leaping - Open-source debugging with LLMs Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our life combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We started by capturing information from running applications to then ‘replay’ relevant sessions later. Our approach for Python involved extensive monkey patching: we’d use OpenTelemetry-style instrumentation to hook into the request/response lifecycle, and capture anything non-deterministic (random,…

    2024 · its alternatives →

  2. 2

    Hey HN, I just built an experimental VSCode extension called LLM Debugger. It’s a proof-of-concept that lets a large language model take charge of debugging. Instead of only looking at the static code, the LLM also gets to see the live runtime state—actual variable values, function calls, branch decisions, and more. The idea is to give it enough context to help diagnose issues faster and even generate synthetic data from running programs. Here’s what it does: * Active Debugging: It integrates with Node.js debug sessions to gather runtime info (like variable states and stack traces). *…

    2025 · github.com · its alternatives →

  3. 3
    Zipy▲339

    Debug instantly with session playback and monitoring in one

    2022 · zipy.ai · its alternatives →

  4. 4

    A simple Python debugger and profiler that generates animated visualizations of program flow, useful for algorithm learning. - CCExtractor/vardbg

    2020 · github.com · its alternatives →

  5. 5

    Automated Penetration Testing for AI-Built Apps

    2021 · replay.io · its alternatives →

  6. 6

    Capture bugs, record sessions, and fix with AI

    2024 · betterbugs.io · its alternatives →

  7. 7

    Hey HN my name is Thassilo, I'm a student and passionate programmer from Germany. I want to showcase Spray, a small C debugger I've been working on for a few months now. Spray has a very simple and approachable interface. Its feature set is limited at this point, but it's already enough to tackle some basic problems. I stared to work on Spray because I was curious about how debuggers work. I am also trying to design Spray in such a way that it's easy to grasp and has a small mental overhead. I'd love to get your feedback on Spray. Email: d4kd (at) proton (dot) me PS: I'm generally interested…

    2023 · github.com · its alternatives →

  8. 8

    Hey HN, we're developing Burr (github.com/dagworks-inc/burr), an open-source python framework that makes it easier to build and debug GenAI applications. Burr is a lightweight library that can integrate with your favorite tools and comes with a debugging UI. If you prefer a video introduction, you can watch me build a chatbot here: https://www.youtube.com/watch?v=rEZ4oDN0GdU. Common friction points we’ve seen with GenAI applications include logically modeling application flow, debugging and recreating error cases, and curating data for testing/evaluation (see…

    2024 · github.com · its alternatives →

  9. 9

    An experimental code editor for writing algorithms - ChrisKnott/Algojammer

    2018 · github.com · its alternatives →

  10. 10

    Debugging-friendly exceptions for Python. Contribute to cknd/stackprinter development by creating an account on GitHub.

    2019 · github.com · its alternatives →

  11. 11

    Hey all, I've been working on this side project to get a Cypress.io-like experience, but for Ruby developers. It's plug-n-play with Capybara system tests, with the following features: - visualize assertions/commands as they happen - view all API requests, errors, and logs in the timeline - pause/continue on any step - rewind through history with a recorded video This is brand new, so looking for people to start trying it out and leave feedback.

    2024 · github.com · its alternatives →

  12. 12

    Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them. For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior. After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x…

    2023 · github.com · its alternatives →

  13. 13

    Debug like never before with the power of AI

    2023 · debugcode.ai · its alternatives →

  14. 14

    Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)

    2023 · chainforge.ai · its alternatives →

  15. 15

    Hey HN commmunity, Karthik here! Super stoked to announce the launch of Zipy today. Launching the product that you've been so dearly working on for months is like sending your newborn to school for the first time. Excitement to nervousness, anxiety to thrill, all sorts of emotions hit you at the same time. But the entire team of Zipy is confidently looking forward to the feedback you guys have in store for the beautiful product we’ve built. In community we trust! Over the past decade, from being a Web Development Intern to a UX Designer and a Product Manager, I have seen the struggles that…

    2022 · zipy.ai · its alternatives →

  16. 16

    Hi HN, I'm the CEO at https:&#x2F;&#x2F;replay.io. We've been building a time travel debugger for web apps for several years now (previous HN post: https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=28539247) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand…

    2025 · nut.new · its alternatives →

  17. 17

    Bringing the simplicity of print() to production debugging!

    2022 · its alternatives →

  18. 18

    I got sick of the old software development loop: Change code -> Run tests -> wait -> wait some more -> look at failures. I decided to build a tool that will enable you to: Change code -> look at failures. No wait time, no explicit test running. Under the hood: - Runs the whole test suite and collects code coverage per test. - For each auto file save, analyzes the changes on the tests. - Runs changed tests in the background. - Display results, the loop time from change to test results is approx 250ms. Instead of: code -> alt+tab -> arrow up -> rerun all the tests -> wait ... -> test results…

    2022 · github.com · its alternatives →

  19. 19

    HTTP(s) proxy debugger with custom viewer

    Jun 2026 · networkspy.app · its alternatives →

  20. 20

    Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…

    2025 · infinitcode.ai · its alternatives →

  21. 21

    Hi Hacker News, As a dev extensively using GPT-4 for coding, I've realized its effectiveness significantly increases with richer context (e.g., code samples, execution state - props to DevinAI for famously console.logging itself). This inspired me to push the idea further and create CaptureFlow. This tool equips your coding LLM with a debugger-level view into your Python apps, via a simple one-line decorator. Such detailed tracing improves LLM coding capabilities and opens new use cases, such as auto-bug fix and test case generation. CaptureFlow-py offers an extensible end-to-end pipeline…

    2024 · github.com · its alternatives →

  22. 22

    Hi Hacker News! We launched an autonomous agent that helps debug production issues, and we’re curious to get your feedback. Today's GenAI devtools, such as Copilot, are limited: they are great for writing code, but we all know that programming is only 20% coding, and 80% debugging. So how can GenAI be used for debugging? As opposed to code completion or test automation, production debugging is not about generating text. Debugging is mostly about root-cause analysis. We realized two things: 1) Generative AI is drastically changing the way we work with data, thanks to its ability to not only…

    2023 · wildmoose.ai · its alternatives →

  23. 23

    Testim&#039;s root cause analysis helps save time troubleshooting failed tests. Tag failures by error type to see trends.

    2020 · testim.io · its alternatives →

  24. 24

    Python stream processing for humans. Contribute to omegaml/minibatch development by creating an account on GitHub.

    2020 · github.com · its alternatives →

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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 →