Exabase
Context infrastructure for AI agents: memory, per-tenant bases, hybrid search and structured extraction
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exabase.ioAbout Exabase
Exabase is context infrastructure for teams building AI agents — the storage, retrieval and memory layer that otherwise gets rebuilt badly in every agent project. Six components make up the platform. Memory is a self-managing memory system the vendor states is first on both of the leading AI-memory benchmarks. Bases are isolated per-tenant instances with version rollback, which is the piece that matters if you are shipping an agent to customers and each tenant's knowledge must stay separated and restorable. Resources hold files, notes and links as a portable context server. Deep Search runs sub-document, multi-modal hybrid search rather than whole-document vector similarity. Extract pulls structured data out of PDFs, websites, images and other formats — the website-to-Markdown endpoint is exposed as a standalone tool. Workers are autonomous agents that enrich a knowledge base on their own schedule. Usage is credit-metered with the exchange rates published plainly: one credit buys roughly 18 PDF extractions, 18 website extractions, 30 audio extractions, 0.8 video extractions, 65 memories, or 10 memories with inference mode enabled, which makes cost modelling possible before you commit. The vendor reports over 100,000,000 pages processed and offers a zero-data-retention policy from the paid tier. Deployment is positioned as production-ready, private by design and security-first, and the vendor runs a demo booking route for teams evaluating whether the platform fits before committing to the credit model.
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