SEOagent is a tool designed to integrate with AI coding agents such as Claude Code, Cursor, and Codex to support search engine optimization (SEO) workflows. Its primary function is to help these agents create, audit, and update landing pages and blog articles within a local codebase, while also providing cloud-based performance analysis and content suggestions grounded in real search data. The tool aims to address the gap where coding agents can generate content and fix technical issues but lack built-in performance analysis capabilities. The system operates through a three-step workflow. First, it analyzes current page performance using data from Google Search Console, competitor research, and DataForSEO. Second, it generates suggested improvements, which the user can review and approve. Third, approved changes are implemented with a single command, keeping the local codebase and cloud insights in sync. SEOagent also supports the creation of a Google Open Knowledge Format bundle, intended to make site information more accessible to AI-driven search tools. Additional features include free utilities such as an OKF / AI-readiness checker, a Lovable TanStack checker, a Lovable SEO checklist, and comparisons against other SEO tools like Surfer SEO, Clearscope, and MarketMuse. Technical requirements are minimal for local use: the tool runs on the same AI model subscription the user already has for their coding agent (Claude Code, Cursor, or Codex), avoiding additional AI subscription costs or per-credit metering. The cloud component is optional and can be added when the user needs measurement, research, and review across an actual live site. SEOagent is positioned as a workflow tool that prioritizes user control, requiring approval before any changes are written into the codebase, and it emphasizes building on the user’s own expertise rather than generating generic, mass-produced content.