Introduction

What Trevize is, how teams build software with AI agents, and why every change lands as a reviewable pull request.

Trevize is a shared workspace where your team and AI agents build software together. You describe what you want, agents do the work inside isolated cloud sandboxes, and every change comes back as a pull request on your own GitHub repository — so nothing ships without your review.

Two ways to work

Trevize gives you two complementary modes, backed by the same agents and the same repositories:

Live sessions

A real-time, multi-player working session. You and your teammates chat with an agent as it works, steering it turn by turn — like pairing, but the agent writes the code. See Sessions.

Automations

Autonomous loops that run on a schedule and keep improving your product between sessions, opening pull requests as they make progress. See Automations.

For one-off work you don’t need to sit through, you can also start a task — a single unit of work that runs autonomously from a short prompt and a blueprint (a bug fix, a feature, or a quick change) and produces a pull request.

How it works

You connect a GitHub repository to a workspace.
You start a session, task, or automation and describe the goal.
Trevize spins up an isolated sandbox, checks out your repo, and runs a Claude Code agent against it.
The agent implements the change on a branch, optionally serves a live preview, and opens a pull request.
You review, leave feedback, and merge — on your terms.

What it connects to

Trevize meets your team where it already works:

GitHub — repositories, branches, and pull requests.
Linear — turn issues into work and track it across tools.
Claude Code — the coding agent that powers sessions, tasks, and automations.
MCP, Neon, Sentry, and Supabase — extend agents with tools, databases, error context, and backend services.

See Integrations for the full list.

Who it’s for

Trevize is built for product teams who want to move faster with AI agents without giving up code review, previews, or ownership of their repositories. Work stays in your GitHub, agents carry knowledge forward across runs, and insights show you exactly how much they’re contributing.