Tell Autolab what to improve.
Choose the metric that matters, such as accuracy, cost, or latency.
metric + constraints + evaluationAI model optimization, automated.
Supercharge your
Autolab optimizes the accuracy and speed of your AI models. Agents run experiments on your GPUs and learn from every result: less development time, lower cost, a more effective ML team.
$ curl -fsSL app.autolab.ai/install.sh | sh ↓
$ autolab start --goal "beat baseline"
reading repo · locating evaluation
connecting available GPUs · experiments queued
exp/021 · evaluating
exp/084 · stopped · evidence retained
comparing experiment results
proposing the next experiments
winner / exp-941c2 · winning change identified
checks passed · change ready for review
ready to inspect
an Autolab run, compressed
Autolab / model improvement loop
01 / 06
Turn ideas into experiments.
Agents read your code and create concrete changes they can test.
▸ one vector = one experimentRun them across your GPUs.
Autolab sends each experiment to the next available machine.
every available GPU joins the queueStop wasted work early.
Runs stop when they have plateaued or are clearly going to fail.
weak runs stop · every result improves the nextChoose what to try next.
Every result helps Autolab propose a better next experiment.
result → next experimentReturn the best change.
You get the winning code, its results, and the history behind it.
winning change → human reviewwinner / exp-0138ready for approval
reasoning-frontier ↗
EVALIMPROVEDCHECKSPASSEDSTATUSREVIEW
- rope_theta = 10_000+ rope_theta = 1_000_000+ data_mix = "reasoning_v2"
experiment history attachedhuman approval required
Compute / continuously scheduled
More insights. Same GPUs.
Autolab watches every job. When a run stops improving or is clearly failing, it ends the job and gives that GPU to the next experiment.
More useful experiments finish without adding more compute.
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autolab / initialize
$ 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
From GPU time to better models
Every experiment improves the next one.
Autolab keeps the code, metrics, and history behind every run. You review the winning change and decide what ships.
Get early access ↗