Tusk Drift: Turning Real Traffic into Deterministic API Tests

API testing has long been a tedious chore for developers, often requiring hand-written mocks that quickly drift from production reality. A new system aims to change that by turning live traffic directly into tests.

Tusk Drift records real API traffic from a service and replays those requests as deterministic tests. The approach eliminates the need to write and maintain test code or fixtures, offering a more realistic testing environment grounded in actual usage patterns.

Traditional API testing involves writing tests and creating mock dependencies that simulate external services. This process is often manual, time-consuming, and prone to error.

Hand-written mocks frequently drift from the actual behavior of the services they are meant to simulate. This discrepancy can lead to tests that pass in isolation but fail in production, creating a false sense of security.

The fundamental challenge is maintaining test fidelity. When the real services change, the mocks must be updated, but this often falls behind, causing tests to become outdated.

Tusk Drift offers a different methodology by recording full request/response traces externally. Instead of intercepting HTTP calls within the test itself, it captures the entire interaction.

The system records traffic for HTTP, databases, Redis, and other dependencies. This comprehensive trace is then used to automatically mock outbound I/O when the tests are replayed.

Key features of the approach include:

• Recording traffic in any environment
• Automatically mocking all outbound I/O
• Replaying requests against a running service
• Eliminating the need for test code or fixtures

The implementation involves a straightforward, three-step process designed for integration into existing development workflows.

First, developers add a lightweight SDK to their codebase. Currently, the system supports Python and Node.js environments.

Second, traffic is recorded in any environment, capturing real user interactions and system behavior.

Third, the tusk run command is executed. This CLI tool sandboxes the service and serves the recorded mocks via a Unix socket, creating a self-contained testing environment.

The system is designed for continuous integration, running on every pull request to ensure code changes don't break existing functionality. This provides immediate feedback to developers.

Beyond standard CI, it has proven valuable as a test harness for AI coding agents. These agents can make changes, run the test suite, and receive immediate feedback without requiring live dependencies or complex setup.

We wanted tests that stay realistic because they come from real traffic.

This approach ensures that tests remain grounded in actual usage, reducing the gap between development and production environments.

The introduction of traffic-driven testing represents a significant shift in how API reliability is approached. By leveraging real usage data, teams can build more robust and accurate test suites.

As development cycles accelerate and AI-assisted coding becomes more prevalent, tools that provide fast, reliable feedback will be increasingly critical. Systems like this one offer a path toward more automated and realistic testing practices.