a rail for guiding the learning experience.
First a companion. Then a place for it to live.
ARAIL began with Buddy — a local agent to learn alongside. Buddy needed an environment. That environment became a lab: pluggable, observable, and entirely owned by you.
It is not an app you log into. It is a blueprint you assemble — a runtime, a streaming inference engine, a router, and a swarm of agents — and at its center, a knowledge base that everything writes to and learns from. Nothing leaves your hardware.
Most tools forget the moment you close them. ARAIL keeps one growing knowledge base — and points it two ways at once. Your agents draw on it to act with real context. You draw on the very same base to learn faster. One memory, compounding for both of you.
Autoresearch agents curate and structure what matters into your base.
Frontier teachers, served locally, grounded in what you actually know.
Your base becomes a model that knows — not one that looks things up.
Specialist agents put that shared context to work on real tasks.
Context you own. Intelligence that compounds. That is wisdom per watt.
Each component clicks onto the same rail — and everything feeds the knowledge base. Swap any one without rebuilding the rest.
A local agent that learns with you and drives the lab in plain language.
The center of gravity — every part reads from it and writes back to it, so context compounds.
Streams 70B–500B+ teacher models layer by layer off your disk — frontier reasoning with no GPU farm to rent.
Fans one prompt across a tier of models — fast local drafts up to frontier teachers — so you can pull answers from several at once. Deterministic, observable, written in Rust.
Specialist agents interrogate, challenge, and refine — happily waiting on deep, disk-hosted models for the most careful answer.
Scores every answer and evolves the rubrics the swarm consults — what gets measured gets better, and it all flows back into your knowledge base.
If you can measure it, we can improve it.
AutoResearch scores every answer against rubrics that evolve themselves. What gets measured gets better — automatically, in the background, while you do something else entirely.
So the swarm reaches for the deepest models there are and runs them cheaply off your disk, layer by layer, instead of renting a rack of GPUs. Because it works in the background, it doesn't mind waiting minutes for a slow, careful chain of thought. It trades speed for depth — reasoning quality no model small enough to be fast can match.
parameter frontier models — up to 671B at 4-bit — running locally, streamed from your SSD. No cluster. No cloud.
Who else is getting local inference from a 500-billion-parameter model?
Clone the blueprint, pick your mode, and begin your journey into artificial intelligence.