AI · alternatives · 2026
24 alternatives to DebugAI: Diagnose failed LLM outputs
Debug failed LLM calls with root-cause diagnoses
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.
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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…
2024 · github.com · its alternatives →
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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 →
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Debugging-friendly exceptions for Python. Contribute to cknd/stackprinter development by creating an account on GitHub.
2019 · github.com · its alternatives →
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BetterBugs.io▲669Capture bugs, record sessions, and fix with AI
2024 · betterbugs.io · its alternatives →
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debugaifree tier, from $12/mo▲5Every tool sees errors. Only debugai knows your codebase.
Jun 2026 · debugai.io · its alternatives →
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Testim's root cause analysis helps save time troubleshooting failed tests. Tag failures by error type to see trends.
2020 · testim.io · its alternatives →
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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 →
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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 →
- 9LO
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 →
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Interactive, node-by-node debugging and visualization for TensorFlow - ericjang/tdb
2015 · github.com · its alternatives →
- 11
debugcode.ai▲103Debug like never before with the power of AI
2023 · debugcode.ai · its alternatives →
- 12DL
2024 · monitor.transluce.org · its alternatives →
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NetworkSpy▲89HTTP(s) proxy debugger with custom viewer
Jun 2026 · networkspy.app · its alternatives →
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A simple Python debugger and profiler that generates animated visualizations of program flow, useful for algorithm learning. - CCExtractor/vardbg
2020 · github.com · its alternatives →
- 16
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…
Apr 2026 · kelet.ai · its alternatives →
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VizPy▲27Turn prompt failures into executable rules — for AI agents
Mar 2026 · vizpy.vizops.ai · its alternatives →
- 18
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 →
- 19
Distributed cross-network remote debugging for Python - tarockey/azure-debug-relay
2021 · github.com · its alternatives →
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2023 · useadrenaline.com · its alternatives →
- 23
AI that diagnoses and fixes code errors like a pro
2025 · share.streamlit.io · its alternatives →
- 24
Debugging is hard for LLMs, because they primarily depend on source code, and they don't have access to runtime state. I spent countless hours debugging code, and the only way I found LLMs useful for that, is to ask them to add log lines. That's annoying, because it pollutes my code and adds unnecessary diffs. So we made an MCP server that solve this problem. It gives MCP clients (like Claude Code) access to a NodeJS inspector, so they can: 1. set breakpoints 2. step in, step out, continue 3. fetch the current execution location 4. read console output 5. run JS using eval To try: 1. run a…
2025 · 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 →