Dimension scores are derived from public data and fields; weighted into the composite. Reference only.
CommerceTXT is an e-commerce transaction data standard designed for AI agents. It emphasizes that llms.txt solves “content discovery,” while CommerceTXT solves “transaction understanding”: information such as pricing, inventory, shipping, returns, payments, reviews, subscriptions, and age restrictions can all be expressed through commerce.txt and subordinate category and product text files.
The protocol uses a Root → Category → Product hierarchy. The root file describes store identity, regions, payments, shipping, policies, and catalog structure; category files provide filters, promotions, and product indexes; product files contain SKUs, GTINs, prices, inventory, specifications, package contents, reviews, compatibility, and more. Directives such as @INVENTORY, @SUBSCRIPTION, @REVIEWS, @IMAGES, and @AGE_RESTRICTION cover common needs in AI shopping Q&A. Its goal is to reduce HTML scraping and DOM parsing, providing deterministic data with smaller payloads and fewer tokens.
The documentation describes the standard as CC0 Public Domain, maintained by an open working group and not owned by any single company. v1.0 recommends keeping it read-only and exposing only public commercial data. Future @ACTIONS may support actions such as checking inventory or adding items to cart, but this is still in experimental discussion, with an emphasis on requiring user confirmation, OAuth2, security audits, and fraud prevention. On the ecosystem side, it maps to Schema.org and envisions being read by AI shopping scenarios such as ChatGPT, Claude, and Gemini, but there is no indication of official integration with these platforms yet.
No pricing, commercial edition, or hosted service is mentioned, so the open protocol itself should be free to adopt. The documentation is fairly strong: it includes a TL;DR, a comparison with llms.txt, complete field examples, a trust verification model, and an FAQ, and it also points to RFC v1.0. However, the crawled content does not show a validator, SDK, plugin, or deployment tool, so real-world implementation still requires developers to generate and maintain the files themselves.
Its strengths are a clear structure, transaction-oriented fields, low token cost, and consideration for multi-region support, compliance, and trust verification. Its weaknesses are uncertain adoption, limited tooling and platform support, and the fact that inventory and pricing accuracy depends on merchants updating data in a timely manner. It is suitable for e-commerce sites, cross-border merchants, SaaS subscription products, and AI agent developers who want their offerings to be accurately understood by AI shopping assistants.
No information is provided about access from mainland China, ICP filing, mirrors, or payment options, so its availability cannot be determined. If it cannot be used reliably, alternatives include Schema.org, product feeds, sitemaps, platform APIs, or custom llms.txt extensions.
⚠ This review is compiled from public sources and does not constitute a purchase recommendation. Verify all facts on the vendor's official site. Verify on commercetxt.org official site.
commercetxt.org is an United States AI Apps provider. TG4G tracks its product information, an overall rating of 8.0/10, and a China-accessibility score of China direct-connect friendly. Click "Visit Official Site" to reach commercetxt.org directly.