Alternatives
Products that do what Extreme Mutation Testing to Optimize LLM Agent-Based Mutation Testing does
Hey HN, Steven here from CodeIntegrity https://github.com/codeintegrity-ai/mutahunter We’re obsessed with automating software testing, specifically mutation testing, and have been frustrated with its slow adoption despite its proven success. I have a pretty different perspective on mutation testing and have shared my thoughts on its current state - https://www.jungsteven.com/blog/2024-07-03-past-present-future-mutation-testing Over the past few months, we’ve developed a new mutation testing tool that’s easy to use and compatible with any programming…
- 1HW
Hello everyone! I’m thrilled to announce the latest feature from Mutahunter.ai, the ultimate tool for finding and fixing weaknesses in your code. We’ve designed Mutahunter to leverage mutation testing powered by advanced LLMs, helping you uncover vulnerabilities and enhance your code quality effortlessly. Introducing our newest feature: Detailed Mutation Testing Reports! After running our mutation tests, Mutahunter now generates comprehensive reports that clearly summarize: • Vulnerable code gaps • Test case gaps These reports significantly reduce the cognitive load on developers by…
2024 · github.com
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- 3ST
Hi HN! I’m a founder at Nextmv (YC 20) [1] We’ve been building out optimization algorithm testing capabilities (acceptance tests, etc.) and just shipped our first pass at shadow testing [2, 3]. In our experience, tools like shadow testing save time and build confidence in decision models, but tools also take time to build and maintain. We’ve seen shadow testing tools in the machine learning and MLOps space [4], but not so much in the operations research community. A lot of folks here [5] seem experienced with optimization models and we’d love to have your feedback! What do you like? What…
2023 · nextmv.io
- 4ET
Hey Hacker News, For the last 2 months, I've been working on a testing agent to free developers from the endless maintenance of end-to-end tests. You just push up a PR, and our agent analyzes the code changes and automatically visits the preview to test things out like a real human! We also support describing tests in English (or even in the PR description), and we'll go through your site whenever you want via a GitHub action to test and make sure various core flows continue to work as expected. We are looking for early testers and are giving out a generous free tier! Just sign up on the…
2025 · playmatic.ai
- 5AT
Hi Hacker News! We're launching Zalor, an agent testing platform. Agents often break when you tweak system prompts, swap models, or add tools. Zalor automatically generates test scenarios and evaluates your agent so you know it's reliable before deploying to production. We currently support the OpenAI Agents SDK and are onboarding other frameworks. A GitHub integration is coming so you can get feedback on every update. Looking forward to hearing feedback from people building agents.
Mar 2026 · agents.zalor.ai
- 6HL
At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…
2024 · github.com
- 7AR
If you're interested in exploring what LLM-based agent systems these days actually do to solve certain benchmarks such as SWEBench or WebArena, we created a small leaderboard with our team, that allows to view a lot of public and OSS agent results including all the runtime traces (the step-by-step reasoning behind the scenes). Looking at traces is actually quite interesting, as they reveal a lot about the inner working and shortcomings of current agent system, e.g. see https://explorer.invariantlabs.ai/u/invariant/webarena--SteP... for an example trace.
2024 · explorer.invariantlabs.ai
- 8BC
We are a small group of undergrads interested in building human in the loop coding agents. We dream of a world where building complex agent workflows feels as simple and creative as playing with legos. When we were building stuff we needed a tool that made it easy to try out different code embedding models so that we could see which ones worked best in different scenarios and understand their strengths and weaknesses. So to speed that process up we made PurpleSearch an 'instant' search engine for your local codebases. This tool lets you quickly deploy any open source embedding model on…
2025
- 9EL
Hey HN! I built Experiment to solve a common frustration in LLM development: the lack of proper tools for prompt engineering experimentation. Here's what makes it different: Key Features: - Load and edit chat completion logs from CSV files - Fork and modify specific conversation entries - Run inference via Anthropic, Mistral, and OpenAI - Define custom tools using JSONSchema format - Visual tool usage analysis with collapsible, sorted key-value pairs - Full mobile support and available as installable PWA Technical Highlights: - Built with React using custom isomorphic architecture -…
2025 · github.com
- 10IS
Hey HN! For that last 8 months I've been trying to make agents that can hack web applications to find vulnerabilities in them - An AI Security Tester. The system has 29 agents in total, a custom LLM Orchestration framework which works on the task-subtask architecture (old-school but works amazingly for my use case, and is pretty reliable) with custom agent calling mechanism. No Auo-Gen, Langchain and Crew AI - Everything custom built for pentesting. Each test runs in an isolated Kali linux environment (on AWS Fargate), where the agents have full access to the environment to undertake any…
2025
- 11IL
LLM Application development is extremely iterative, more so than any other types of development. This is because in addition to all the activities involved in regular application development, we also need to make the LLM Application accurate and reduce hallucination. To improve performance, we need to trial and error various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, try different agent architecture, and more. There are thousands of permutations to try. We need to be able to easily experiment with these…
2024 · palico.ai
- 12AB
Hey everyone, My friend and I built a simple bug fixing app that listens for alerts/issues from Sentry, contextualizes it against your codebase, and any other data sources you wish to connect (right now we support Notion, Google Docs, and Slack), and deploys an ai agent to write a PR for review in Github or Gitlab to solve the bug. Our current demo shows the end-to-end process for a trivial bug fix, but we have been testing it with open source python repos like http-pie, comparing how our agent solves a bug compared to a human engineer and it gets fairly close. We are working on adding…
2023 · resolvd.ai
- 13IB
The main goal of this was to be able to not just run multiple Claude Code sessions at once, but actually manage them and keep track of what I was doing. Sometimes this is multiple attempts on the same task, sometimes I work several tasks at once. Really I was just sick of twiddling my thumbs waiting for the coding agent to finish, and I wanted it to be easy to work on/review/test another change while I waited.
2025 · github.com
- 14TN
Hi guys, I’m excited to share an update on ReproModel, an open-source toolbox designed to streamline the testing and reproduction of machine learning models. I, like many of you, have really struggled with benchmarking and comparing models, from missing code, to opaque experiment parameters slowing the process. I decided to take matters into my own hands, and created a mini-toolbox in my free time to streamline the process. The goal is to reduce the time and effort spent on replicating experiments, enabling researchers to focus on innovation rather than setup. Knowing this task is not an…
2024 · github.com
- 15IB
I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…
2025 · codii.dev
- 16AB
Hi everyone! My team and I just open-sourced a bunch of cool agent dev tools: Invariant Explorer to visually inspect and understand AI traces and a testing framework, building on pytest.
2024 · github.com
- 17JL
I’ve been working on a multi-agent academic research lab, and I wanted to share it here today primarily to give a massive shoutout to the developers behind ZeroClaw. When designing the architecture for this, I needed an autonomous agent runtime that was lightweight, entirely agnostic, and highly secure for local execution. ZeroClaw’s pure Rust implementation provided exactly the zero-overhead foundation the project required. Because they solved the core runtime execution so elegantly, I was able to spend my time building the higher-level orchestration on top of it—like the retrieval graph…
Mar 2026 · rainlabteam.vercel.app
- 18TI
Hey HN! I built AgentMGMT.dev today to keep track of all those agent orchestration tools that keep popping up. I've tried a few and landed on Superset, which I'm extremely happy (and productive!) with - but I think this category of tools will be extremely important and interesting in the next couple years, so it's worth keeping an eye on all available tools and how they evolve. I will keep the site up-to-date, please help me by submitting new tools that are not yet in the list, or add any details that might help folks who are out shopping for their first/next agent orchestrator!
May 2026 · agentmgmt.dev
- 19IB
I built a tool to roast landing pages with AI agents. I was gathering feedback from watching landing page roast videos, and figured out I could prompt LLMs to analyse a screenshot and roast based on the same criteria. It's not 100% accurate yet, but it has been really insightful when I've tested it on my own websites. Let me know what you think!
2024 · roastmylandingpage.io
- 20PA
Hey HN, we’re the team at Morph Labs and we’re excited to release Phorm (https://phorm.ai), a fast, simple, and SOTA codebase answer engine. You can search over up to 8 repositories in almost any language, and Phorm can comfortably handle repositories up to ~200K LOC each. It is free during our initial research preview. Phorm’s Advanced Indexing combines synthetic data with static analysis of the code graph to improve the relevancy of search results by up to 3X. We’re proud to launch with featured Advanced Indexing support for a select group of leading open-source projects: - Nomic…
2024 · phorm.ai
- 21CM
Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
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- 23HP
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai
- 248B
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
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