Ranger

Ranger is an always-on AI QA platform whose goal is to create a seamless QA experience so that teams are not blocked by testing issues during development, turning big ideas into real products.

Purpose

Ranger is mainly used to verify the work results of coding agents and help teams avoid releasing blindly. It allows agents to work autonomously for longer without frequently requesting human help.

Main Capabilities

• After running the ranger go command, Ranger launches a browser, tests the new feature, and returns a conclusion or demo.
• Coding agents can continue building while Ranger continuously checks.
• When Ranger finds a defect, the agent can catch the issue, fix it, and retest with Ranger, achieving a process with no human in the loop.
• Provides evidence for every change made by the agent, including screenshots, recordings, and traces, so teams understand whether the entire application is ready, not just a single feature.
• Users can leave feedback on any scenario, and the agent will receive the feedback, push a fix, and have Ranger re-verify it.
• Every review generates a shareable record containing screenshots, videos, and conclusions, which can be attached to a PR without needing a meeting.
• Performs intelligent test selection before each deployment; failures are immediately classified, real defects are surfaced, and the rest are handled automatically.

How to Use

Teams start the testing process by running ranger go . Ranger automatically completes browser launch, feature testing, and conclusion return. The agent and Ranger work together to form a closed loop of discovering defects, fixing them, and re-verifying. Users can also leave feedback on scenarios to drive the agent to fix them.

Applicable Scenarios

Ranger is suitable for teams that need to let coding agents work autonomously and continuously verify, especially in development environments with rapid iteration and frequent deployment. It supports parallel scaling, can launch hundreds of parallel browsers on demand, and is open to everyone, not just enterprises.

Limitations

Details such as specific pricing, cost, tech stack, and platform support are not yet provided.