FaultFixer is a tool for websites and apps that aims to help them self-heal. It catches front-end and back-end errors, diagnoses the root cause with AI, and sends the result to an AI coding tool over MCP. The setup is presented as a single snippet that can be pasted once, with no rewrites required.
Its described features focus on error diagnosis and response. FaultFixer says it provides root cause analysis, a fix-ready hypothesis, and the impact of an issue rather than only a stack trace. It also shows the sequence of click, route, and request activity before each error, tags errors as coming from the user’s code, a dependency, or an outside script, and links an error to the release that introduced it and the last good release for comparison. Other listed functions include automatic regression detection, alerts when error rates spike, prioritizing issues by severity, impact, and confidence, and filtering duplicates, bots, and benign events.
The service is meant for teams working on websites and apps across a range of environments. The page names JavaScript, React, Next.js, Vue, Svelte, Node.js, Python, Ruby, PHP, Go, .NET, Java, Vercel Edge, Cloudflare Workers, AWS Lambda, Bun, Deno, Nuxt, SvelteKit, Astro, static HTML, and no-code or vibe-coded sites, and says it integrates with all platforms. It also says issues and fixes can move through MCP into Claude Code and Cursor, and notifications or issue filing can go to GitHub, GitLab, Bitbucket, Slack, Discord, Teams, Telegram, email, and webhooks.
FaultFixer is described as totally free, with every account receiving the full Pro experience from the start. The page says there are no plans to introduce paid tiers, no card to enter, and no servers to run.
In the AI space, FaultFixer takes a focused approach. Automatically detecting and resolving front-end and back-end errors in web and app projects using AI. It is built as a B2B product for web developers and engineering teams. There is a free tier. FaultFixer is available on the web.
FaultFixer first shipped in 2026. Key capabilities include error detection, AI diagnosis, and root cause analysis. It exposes integrations via an MCP server.
Summary written by a language model from the project’s public pages.
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