AI teammates that finish real work. Any model. Your data.
Every teammate runs on the model that fits its job, works from its own computer, and leaves a full record of what it did. You stay in control of the data the whole time.
What it actually does
Agents and channels
Message AI teammates like colleagues, one on one or in a channel with the whole team, and keep the full history in one thread.
Per-agent computers
Every agent gets its own persistent browser. Watch it work in real time, or take over the wheel yourself mid-task.
Skills with dry-run rehearsal
Teach a skill once by recording your screen. The agent drafts it, then rehearses it in dry-run before it ever touches production.
Routines and triggers
Schedule work on a cron, or fire it from a Slack message or a GitHub push. No cap on how many you run.
Full audit trail
Every run, tool call, model call, and approval is logged, filterable, and attributed to the person who kicked it off.
Workspaces
One deployment, many teams. Each workspace sees only its own agents, threads, skills, and connections.
Everywhere you work
Install BetterBots as a PWA on your phone. A desktop shell is available for Mac and Windows, and the iOS app is in testing.
Any model. No lock-in.
BetterBots routes through OpenRouter, so you pick the model for each job instead of being stuck with one vendor's model. Every run shows its exact cost in /usage, not a token estimate. The self-tuning engine watches spend and quality over time and suggests better tier assignments. You approve every change before it takes effect.
You always own your data.
Models process your requests. They do not store them. Every call routes through zero-retention providers, so a model sees a request, answers it, and forgets it. Everything else, your threads, your files, your skills, your audit log, lives on your own infrastructure. Switching models never means moving your data, because it never left in the first place.