Trayn makes real work trainable
Record any workflow on any app. Train, evaluate, and verify your AI agents against hyper-realistic RL playgrounds.
Why agents fail at real work
Real workflows span multiple apps and steps. Context gets lost across handoffs. UI changes, hidden state, and edge cases make agents fail silently.
Trayn captures those failure points and turns them into training data for the next run.
From real workflow to trained agent.
Capture. Train. Verify
RL Playgrounds and Training infrastructure
Captured workflows become practice environments. Agents run, get feedback, and improve over repeated reps.
Forwarded the Acme Corp████ ████ contacts from Gmail████. Can someone update the CRM before the 3pm call?
On it. Pulling the data into Salesforce██████████ now. @Sarah████ heads up — two of those emails bounced.
CRM sync: 3 contacts updated (sarah@acme.com████@████.com, j.doe@acme.com█.███@████.com, m.lee@acme.com█.███@████.com)
Thanks Mike████! I'll prep the deck for Acme████ — meeting link is in the Google Cal████ ███ invite.
Privacy-first capture
All personal data is detected and replaced offline in your browser before anything is stored. Names, emails, and org data are anonymized while preserving structural context.
SDK + CLI
Point the SDK at any playground URL. Your agent attempts the task, gets graded on each step, and stores memories for the next repetition.
Training feedback loop
Agents practice real workflows in interactive playgrounds and get clear feedback on what to improve next.
Graded memories
Trayn records what worked and what failed, then uses those corrections in the next rep so agent behavior improves over time.