rebi-shoveler
rebi-shoveler is a web crawler operated by Rebi (rebi.ai), a company specializing in curating high-quality training data for large language models and AI systems, as documented on their official website (rebi.ai/crawler). Launched in early 2024, its primary purpose is to systematically collect publicly available web content in a manner that prioritizes freshness and diversity, feeding into Rebi’s proprietary dataset used to train competitive open‑source and closed‑source LLMs.
Rebi‑shoveler performs distributed crawls using a pool of residential and datacenter IP addresses, with documented ranges including 102.223.0.0/16 and 146.70.0.0/16 according to Rebi’s published ASN records. It makes requests at a rate of approximately 10 requests per second per IP, but can ramp up to 50 requests/second during deep scans of high‑authority domains; this behavior is described in Rebi’s technical blog (rebi.ai/engineering/crawler). The crawler uses both HTTP/1.1 and HTTP/2 protocols, and sends a special X-Rebi-Crawler: true header. It also respects the Cache‑Control and If‑Modified‑Since headers to avoid redundant fetching.
Rebi officially states that rebi‑shoveler honors robots.txt Disallow directives exactly as specified, including wildcards and paths – this is confirmed in their official documentation (rebi.ai/robots.txt-policy). In practice, multiple website administrators have reported that the crawler stops for at least 24 hours after encountering a Disallow rule, and no violations have been documented in public forums or security advisories.
The primary User‑Agent string is rebi-shoveler/1.0, optionally appended with a build number like rebi-shoveler/1.0 (compatible; RebiBot; +https://rebi.ai/crawler-bot). Network administrators can also detect it by the static header X-Rebi-Crawler: true and the characteristic request pattern of fetching robots.txt first, then a sitemap.xml or sitemap.txt if present. Rebi provides a verification endpoint at rebi.ai/verify/[ip] to confirm crawler identity.
All data collected by rebi‑shoveler is used exclusively to build training datasets for foundation models, including text, code, and structured content – Rebi explicitly forbids using the data for advertising, user profiling, or surveillance, as stated in their data usage policy (rebi.ai/data-usage). The company also releases monthly transparency reports listing the top 100 domains crawled and the volume of data extracted.
Website administrators are advised to rate‑limit rebi‑shoveler at a threshold of 50 requests per second per IP, as the crawler is designed to be aggressive but legitimate; blocking below this threshold may cause unintended disruption to dataset freshness. Rebi recommends using a token‑bucket algorithm with a 5‑second burst allowance to balance availability with crawl intensity.
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