An open-source agent for getting cited by AI search
The problem
Generative engine optimization was being discussed as an idea and sold as a service, but nobody had written down an operating procedure an agent could follow unattended, and a single prompt cannot hold a whole methodology.
What we built
The graph has 43 nodes across five clusters, and the agent loads each one only when it needs it. An index node routes the agent to the cluster its current task needs, so a citation-monitoring pass never pays for scraping architecture it is not using.
The loop runs through monitoring, scraping, paying, generating, publishing, and verifying, then starts again. Each stage feeds the next, and verification feeds back into monitoring, so the strategy adjusts on evidence.
Nine nodes cover x402 micropayments, so an agent can pay for metered or paywalled data mid-run instead of stalling at a signup wall.
A scheduled weekly pass runs the monitoring cluster headless and reports citation gaps with deltas against the previous week. The scheduled version stays report-only: it observes, and a human decides what to publish.
What this demonstrates
Agent-readable methodology: knowledge structured so a model loads only what the current task needs.
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