Canonry – Open-source AEO monitor (track how AI engines cite you)

Name: Canonry – Open-source AEO monitor (track how AI engines cite you)
Availability: InStock
Author: arberx

by arberx·Mar 11, 2026·1 point·2 comments

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AI Analysis

●●SolidSolve My ProblemSlickNiche Gem

Self-hosted AEO monitoring with YAML config when enterprise SaaS costs thousands.

Strengths

•Config-as-code YAML workflow fits modern DevOps pipelines better than SaaS dashboards.
•SQLite backend means zero external dependencies beyond your own LLM API keys.
•Parity between CLI, API, and dashboard prevents vendor lock-in for data access.

Weaknesses

•Non-deterministic LLM responses make consistent citation tracking scientifically fragile.
•Running frequent queries against paid APIs like Claude will get expensive quickly.

Post Description

We couldn't find a good open source solution for this so we built Canonry to monitor how ChatGPT, Gemini, and Claude represent and cite our sites.

It tracks visibility scores, citation-readiness, and brand accuracy across providers over time. Self-hosted on SQLite, config-as-code in YAML, CLI and API are first-class.

Would love some feedback!