A thousand experiments in parallel, on your compute, scored against your evals. State of the art with the team and budget you already have.
Write down what you want: beat this baseline, cut this latency, test these three ideas. Autolab reads your repo, plans the experiments, and spins up an agent for each one, across every machine you give it. They keep going until the goal is met.
Every experiment is a commit: code, environment, metrics, logs, checkpoints. The readout ranks what worked and why, so you always know what to try next.
Idle compute is the most expensive thing you own. Autolab keeps your cluster saturated with the next-most-valuable experiment, and scales the winner up when it’s proven.
Autolab optimizes whatever your eval measures. The agents push the number, and nothing ships unless it moves.
Architectures, data mixes, optimizers, schedules. Every promising variant gets its shot.
▲ +2.3% val_accSCORED AGAINST YOUR EVALQuantization, kernels, serving configs, tuned on the stack you actually serve from.
▼ −31% p99SCORED AGAINST YOUR EVALThe cheapest training and serving configuration that still clears your quality bar.
▼ −38% cost / runSCORED AGAINST YOUR EVALInstall, point it at your eval, go. Or meet Autolab inside the coding agent you already use.
$ curl -fsSL app.autolab.ai/install.sh | sh $ autolab init # point it at the eval script that prints your metric $ autolab start # the agents begin. watch or walk away.
$ autolab install claude-code then, inside Claude Code: > use /autolab to run experiments on this repo
$ autolab install codex then, inside Codex: > queue autolab experiments against eval.py
On your cluster or your cloud account: code, data, and weights never leave your network. On-prem installs available for pilots.
Autoresearch is autonomous AI agents running the machine learning research loop: proposing experiments, writing the code, training and evaluating models, and deciding what to try next — while humans set the goal. Instead of one researcher hand-running one experiment at a time, an autoresearch platform runs thousands in parallel and merges only what improves the metric. Read the full explanation →
Autolab is an autoresearch platform for AI model training. You give it a goal and an eval; its agents read your repo, plan experiments, launch them across your GPUs, score every run against your metric, and keep iterating until the goal is met. It was built by ML researchers from MIT, Harvard, Stanford, and Google DeepMind.
Agentic model training means AI agents, not humans, drive the training loop: they sweep learning rates, change data mixes, modify architectures, launch runs, read the training logs, and decide the next experiment. Autolab coordinates hundreds of these agents against a single goal, on your own compute.
AutoML and hyperparameter optimization search a fixed space with predefined strategies. Autoresearch agents write real code in your repository: new data pipelines, loss functions, kernels, quantization and serving configs — anything a researcher could try — and every change is scored against your own evals before it merges.
On your cluster or in your cloud account. Code, data, and weights never leave your network, and on-prem installs are available for pilots. Autolab keeps whatever GPUs you have — from a spare 3090 to a multi-node H100 cluster — saturated with the next-most-valuable experiment.
Whatever your eval measures: model accuracy, training cost, inference latency, throughput. Teams use it for pre-training and fine-tuning experiments, post-training recipes, and inference optimization — the agents push the number, and nothing ships unless it moves.