okwasniewski

Hey, Oskar here - creator of e2e thanks for posting it! Happy to answer any questions

We found gemini 3 flash to be the best model as of now, when it comes to bang for the buck, GPT 5.5 is also a bit more expensive than 5.4, if we run tests at scale it has to be affordable. Once a newer model that fits into the criteria is released, we will update it!

We've been doing quite a lot of context engineering and optimizations to make sure it's not as expensive. The subsequent runs are faster because we cache the trajectory of the agent (not the whole test run yet, as we want to keep the agent in the loop, more like a manual QA engineer, not a test script).

We currently do not have any benchmarks; much of the experience depends on the test plan. We've been mostly focusing on the customer experience not benchmarking.

Currently, only iOS, but we can add iPadOS too!

Happy to hear that!

Yeah, we have all of them. I saw it too where in bigger companies our emails were going straight to spam. Will migrate to it soon

Unfortunately from our experience tests don’t scale as well as code.

First of all, static tests are very brittle: you rely on selectors, need wait times, and can’t really test a lot of dynamic content (think AI chats/interactions). Then it’s all the infrastructure around it: solving captchas, handling auth, handling email OTP (each of our agents has access to its own inbox), spinning up simulators and handling video recording and screenshots.

To ensure stable results we do a lot of harness engineering, where we inject trajectories of previous tests to ensure the stability and also the split into smaller steps helps to prevent context overload and decision fatigue.

Regarding security part, the product can operate solely without any access to the codebase, you can just give us a URL or a mobile app build and we will do the testing.

Our approach is heavily focused on agents, both for executing tests and for managing the platform. We want to provide the best and simplest way to conduct agentic testing, with a strong focus on details. It looks like their platform also requires a sales call.

We support both web and mobile, which is what a lot of companies prefer, just one agent for both. Also, I'm pretty sure Revyl relies only on vision models, which tend to be slower. We built the platform around a hybrid approach that combines vision and accessibility APIs, which is much faster.

Would love to hear your feedback after you try it out!

Unfortunately from our experience tests don’t scale as well as code. First of all, static tests are very brittle: you rely on selectors, need wait times, and can’t really test a lot of dynamic content (think AI chats/interactions). Then it’s all the infrastructure around it: solving captchas, handling auth, handling email OTP (each of our agents has access to its own inbox) and handling video recording and screenshots.

To ensure stable results we do a lot of harness engineering, where we inject trajectories of previous tests to ensure the stability and also the split into smaller steps helps to prevent context overload and decision fatigue.

Regarding test case management, our customers have used our CLI to migrate their existing test cases from whatever system they were using before.

I feel like new AI model releases will only allow our agents to do more in-depth testing; the space still has a lot of room to grow. Quality assurance is way more complicated than just clicking around a UI.

Regarding the other question: not yet. For now, we have Chromium, iOS, and Android (latest versions of each), but we are working on adding more. Regarding network connectivity, it's coming soon (I have an open PR).

Thank you! That's the goal

We use agents to navigate the app, making real-time decisions based on its state. I prefer to compare it more to a manual QA engineer than to static e2e tests. We spent a lot of time on the harness to make sure the results are reliable. This allows you to assert on dynamic content like AI-generated content. We also support validation of email flows since the agent can read its own email.