Show HN: Tusk Drift – Turn production traffic into API tests

Show HN: Tusk Drift – Turn production traffic into API tests(github.com)

33 points by jy-tan 123 days ago | 8 comments

Hi HN! In the past few months my team and I have been working on Tusk Drift, a system that records real API traffic from your service, then replays those requests as deterministic tests. Outbound I/O (databases, HTTP calls, etc.) gets automatically mocked using the recorded data.

Problem we're trying to solve: Writing API tests is tedious, and hand-written mocks drift from reality. We wanted tests that stay realistic because they come from real traffic.

versus mocking libraries: Tools like VCR/Nock intercept HTTP within your tests. Tusk Drift records full request/response traces externally (HTTP, DB, Redis, etc.) and replays them against your running service, no test code or fixtures to write/maintain.

How it works:

1. Add a lightweight SDK (we currently support Python and Node.js)

2. Record traffic in any environment.

3. Run `tusk run`, the CLI sandboxes your service and serves mocks via Unix socket

We run this in CI on every PR. Also been using it as a test harness for AI coding agents, they can make changes, run `tusk run`, and get immediate feedback without needing live dependencies.

Source: https://github.com/Use-Tusk/tusk-drift-cli

Demo: https://github.com/Use-Tusk/drift-node-demo

Happy to answer questions!

jdmoreira 121 days ago |

I'm sold on this. Have been looking for something similar!

jy-tan 121 days ago | |

Give it a spin and let us know what you think! :)

dhruv3006 122 days ago |

Building something adjacent to this field :https://voiden.md/.

Also,I loved the approach here!

jy-tan 121 days ago | |

Thank you!

vishalb6 120 days ago |

Can this be used for performance testing as well or just functional testing?

jy-tan 120 days ago | |

Currently Tusk Drift focuses on functional/regression testing - we mock outbound dependencies (DBs, external APIs) for determinism, so we're not measuring real-world performance characteristics today.

That said, we're also exploring extending it for capacity modeling and resource estimation, which would be a differentiated approach from traditional load testing. Synthetic benchmarks fail to capture how traffic patterns (not just volume) affect resource usage. Since we already record real production traffic, we're uniquely positioned to:

1. Replay specific time periods (e.g., last year's Black Friday sale)

2. Preserve the natural distribution of request types

3. Control downstream latency via our mock system

4. Build models beyond linear regression for QPS -> CPU/mem prediction

What performance testing use case did you have in mind? We're actively exploring this space.

kxbnb 120 days ago |

This is a smart approach to API testing - capturing real production patterns is way more valuable than synthetic tests.

One question: how do you handle sensitive data in the captured traces? We've been working on API governance at toran.sh and found that policy enforcement during trace capture can be tricky - especially ensuring PII doesn't leak into test fixtures.

Great work on the trace replay mechanism!

jy-tan 118 days ago | |

Thanks! Great question, we have a Transforms system that lets you define redaction rules (redact, mask, replace, or drop) using matchers with JSONPath support. Transforms are applied at capture time, so sensitive data never leaves your service boundary.

Full docs here: https://docs.usetusk.ai/api-tests/pii-redaction/basic-concep...

Would love to hear what patterns you've found work well at Toran!