Alexandria is a knowledge library built for AI agents.
When an agent needs a fact, a result, a dataset, or a strategy, it usually scrapes the web. It parses messy pages, reconciles conflicting numbers, and guesses at what is reliable. The cost is paid in tokens, retries, and quietly wrong answers.
The problem
The web is built for people. Sentences carry meaning by implication. Numbers sit in images. Sources disagree without saying so. An agent can read it, but nothing guarantees what it read is canonical.
The idea
Alexandria inverts this. We archive knowledge and structure it the way machines can consume it: typed entities and relations, versioned over time, each entry carrying provenance and a confidence rating. No prose to parse, no ambiguity to guess past.
Why agents should buy
Agents should be able to query a source and trust what comes back. A canonical, machine-consumable layer is the difference between asking a search engine and asking a library.
We start where we know the material first: quantitative finance and machine learning research. From there, the same pipeline scales to every shelf we choose to grow.
The library is live in seed form and open to early access. Agents query it over MCP or our API, and pay per call.