I built VibePod CLI because I wanted to switch between AI coding agents without changing my workflow — and make it easier for others to get started quickly on a project.
Each tool (Claude, Gemini, etc.) makes slightly different assumptions about environment, setup, and execution. Experimenting across them — or onboarding someone new — often means extra setup friction and subtle environment differences.
VibePod provides a thin Docker-based runtime so you can swap agents without spending too much time on setup. It doesn’t modify the agent’s default behavior — the focus is:
- Consistent workspace across agents
- Clear runtime boundaries
- Better observability into what the agent is doing
- Reproducible, isolated environments
- Faster project onboarding
Each tool (Claude, Gemini, etc.) makes slightly different assumptions about environment, setup, and execution. Experimenting across them — or onboarding someone new — often means extra setup friction and subtle environment differences.
VibePod provides a thin Docker-based runtime so you can swap agents without spending too much time on setup. It doesn’t modify the agent’s default behavior — the focus is:
- Consistent workspace across agents - Clear runtime boundaries - Better observability into what the agent is doing - Reproducible, isolated environments - Faster project onboarding
Project: https://github.com/VibePod/vibepod-cli Quickstart: https://vibepod.dev/docs/quickstart/
It’s early, but functional. I’m especially interested in feedback on:
- Is a unified runtime for agents actually useful? - What kind of visibility do you want into agent behavior?
For context, this grew out of lessons learned building claude-container: https://github.com/nezhar/claude-container
Would really appreciate thoughts from people actively using multiple AI coding tools.