Use Case
An experiment loop that runs around the clock
Form a hypothesis, write code, run it on GPUs, read the results, decide what's next. AI agents iterate this loop autonomously on Seyval.
The challenge
Every experiment needs GPU setup and teardown, capping how many attempts you can make.
AI agents can write code, but have no safe way to access the compute needed to run it.
Logs and artifacts end up scattered across local machines, making experiments hard to reproduce or compare.
How Seyval solves it
Connect your repository
Register your experiment repo with Seyval and run any branch or commit as-is.
Agents run over MCP
Agents like Claude launch experiment jobs through MCP tools. Seyval handles GPU provisioning and environment setup.
Results drive the next step
Logs and artifacts are saved and organized automatically. The agent reads results, adjusts parameters, and runs the next experiment.
Why Seyval
An order of magnitude more attempts
No environment-setup wait, and the loop keeps running while you sleep.
Every run is recorded
Which commit, on which compute, at what cost — every run is traceable and reproducible.
Nothing carries over between runs
Each run builds and executes its own image, then the environment is destroyed — so one experiment's side effects never leak into the next.