AI · alternatives · 2026
24 alternatives to A static scanner for LLM app code
Code scanner to check for issues in prompts and LLM calls - kereva-dev/kereva-scanner
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Code scanner to check for issues in prompts and LLM calls - kereva-dev/kereva-scanner
2025 · github.com · its alternatives →
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · 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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Hey HN! We’re Kevin and Steve. We’re building PromptTools (https://github.com/hegelai/prompttools): open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. Evaluating prompts, LLMs, and vector databases is a painful, time-consuming but necessary part of the product engineering process. Our tools allow engineers to do this in a lot less time. By “evaluating” we mean checking the quality of a model's response for a given use case, which is a combination of testing and benchmarking. As examples: - For generated…
2023 · github.com · its alternatives →
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Breadcrumb▲163Open-source LLM tracing for agent visibility
Mar 2026 · breadcrumb.sh · its alternatives →
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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 →
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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 →
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Dabarqus▲92Add PDF chat to your LLM app in less than 9 lines of code
2024 · dabarqus.com · its alternatives →
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Promptotype▲18Develop, test, and monitor your LLM structured tasks
2024 · promptotype.io · its alternatives →
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Spelltest framework simulates conversations between AI ‘synthetic users' in an environment to test and refine LLM-based applications. It ensures your app converse with utmost accuracy and relevance. Post-chat, Spelltest assesses responses, providing qualitative and quantitative feedback on performance. Suitable for both chat and completion modes. When to use: - After modifying your prompt. - When your LLM provider updates. - As a CI step for you repo. All feedback and collaborations appreciated!
2023 · github.com · its alternatives →
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I was working on a project that involved indexing GitHub repos that used really long prompts. Iterating over each section and figuring out which parts of the prompt led to which parts of the output was a quite painful. As a frontend dev, I kept thinking it would be nice if I could just 'inspect element' on particular sections of the prompt. So I built this prompt debugger with visual mapping that shows exactly which parts generate which outputs. Now that context windows are getting huge, working with massive prompts should not be as difficult. Planning to open source this soon, but I'd love…
2025 · inspectmyprompt.com · its alternatives →
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The HN community may find the context of the prompts, organized by each turn in each session, the most useful. See the website/docs/prompts.md and session-X.md files. I also started exploring some workflows for the LLM to execute, organized in the website/docs/tasks/ folder. I found it pretty handy to have the LLM document our work as we went and simply embedded the static site into the executable, along with all the music and logic. The whole project took me about a day for the backend. The C++ controller itself took only a few turns. I enjoyed focusing on my son's…
2025 · github.com · its alternatives →
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ContextCheck▲11Framework for testing and evaluating LLMs, RAG & chatbots.
2024 · github.com · its alternatives →
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An automated security check for apps built with Bolt, Cursor or v0. Plain-English findings and a copy-paste fix. No jargon, no terminal.
Aug 2026 · sentrint.com · its alternatives →
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Experiment with, and evaluate, prompts and LLMs
2023 · its alternatives →
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2023 · github.com · its alternatives →
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LLMDog▲22Your code's best friend for seamless LLM conversations
2025 · llmdog.arif.sh · its alternatives →
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Hi all, I'm Ivan, and together with Alex, we're building a diagram visualization tool for codebases. Alex and I are devs, and we've noticed that recently we've been super productive at writing code (prompting :D). But when it comes to understanding big systems, prompting doesn't work that well — for that, diagrams are best imo. Most tools out there don't scale to big projects (e.g. PyTorch), so we're building CodeBoarding — a recursive visualizer for codebases. It starts from the highest level of abstractions and lets you dive deeper. We use static analysis and LLM agents. The control-flow…
2025 · github.com · its alternatives →
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Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com · its alternatives →
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Also compare
- An open-source code scanner to find issues in prompts and LLM calls alternatives
- Why write code if the LLM can just do the thing? (web app experiment) alternatives
- Letting LLMs Run a Debugger alternatives
- PromptTools – open-source tools for evaluating LLMs and vector DBs alternatives
- Breadcrumb alternatives
- ChainForge, a visual tool for prompt engineering and LLM evaluation alternatives
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 →