A Linear twin for testing AI agents.

Test agents that triage and update issues. Teams, issues and states your agent can work through, without touching your real backlog.

Book a demo

Meet the Linear twin.

A stateful copy of Linear, built for testing agents. It accepts the same API and MCP calls, checks sign-in and input, and follows the same rules, but runs on its own data, so nothing reaches your real account.

What your agent can do

Issues, states and cycles
Projects and teams
Assignees, labels and priorities
Comments and relations
Permissions

Seeded state

Every run starts from a company that already has history. We seed:

Teams with their own workflows
A backlog and a cycle in progress
Projects with milestones
Users and labels

Use Linear in staging, without using production.

01Validate every actionThe twin checks what the agent sends the way Linear would: required fields, permissions, rate limits, malformed input. Mistakes fail here, not in your account.
02Start from a realistic stateDescribe the company and the situation in plain English. DrizzLabs seeds the twin to match, and resets it to that state before every run.
03Replay tasks across toolsThe Linear twin shares its people, customers and history with the other twins, so a task that touches three tools runs the way it would at work.

Point your agent at the twin.

Swap the live tool’s address for the twin’s. Your agent code stays the same.

Teams and projects
Issues and states
Cycles
Assignees and labels
One-call reset

{
  "mcpServers": {
    "linear": {
      "type": "http",
      "url": "<twin MCP endpoint>",
      "headers": { "Authorization": "Bearer <token>" }
    }
  }
}

REST works on the same data. Both are reset with one call.

Connected to the rest of the company.

A Linear issue links to the GitHub pull request that closes it and the Slack thread where it came up.

Gmail Google Calendar Jira Slack Gong Salesforce Google Drive HubSpot GitHub

Thirty minutes with a founder

Run your agent on the Linear twin.

Book a demo Request a twin