1.10.10117
Daily-driver build with the safest release cadence.
- Updated
- 2 days ago
- File Size
- 536.7 MB
- Release Notes
- Open notes
Stable is the recommended track for most users. Alpha gets you the newest capabilities first.
Daily-driver build with the safest release cadence.
Fastest release track with the newest features and experiments.
Every release stays linked here so you can inspect what changed before you switch versions.
The last three weeks have been very productive. Most of the work went into performance improvements: startup time, memory usage during startup and runtime, CPU and GPU usage during search, and overall configuration processing efficiency have all been improved. In some areas, performance is up by several dozen percent.
Just as importantly, several new subsystems were added that will help me identify and fix problem areas more effectively going forward.
At the moment, there are still two noticeable issues: startup memory usage (usually around 1 to 2 GB, though it drops to about 500 MB after some time) and overall UI responsiveness. The causes of both are understood, but there is no quick and painless fix, so we will be addressing them gradually over the next six months.
Nothing special, just another input method. It is only useful if the other methods are not working for some reason.
Three new options have been added in Settings: Property Caching, Filesystem Storage, and File References. All of them are intended to improve performance in different ways.
These options are still being tested, so if you decide to enable them, be prepared for possible issues. If you prefer, you can also wait until these systems are officially released and enabled by default. Your participation and feedback would be very valuable.
ReplaceId NullReferenceException).Here’s a summary of the changes made to the app over the last 4 months.
A lot of work went into this release: improved C# scripting (for Triggers/Actions and Overlays), an upgraded Send Sequence system (which now fully replaces Send Input in terms of functionality), a better configuration subsystem, and much faster, more efficient Image Effects that can now downscale images for better performance.
To support the needs of the new and rapidly growing machine learning community, I also developed a separate app to make it easier to create and train Yolo models, which are fully supported by the ML Search feature. In my experience, this is much faster than preparing data manually, even though the app is still in an early alpha stage. For more information, see: Getting Started | EyeAuras Wiki.
One of the main ongoing initiatives is the move to a new UI technology. This change is intended to solve current responsiveness issues in the software, such as switching between auras or creating new triggers/actions.
Although this is a slow transition and may take another 6–12 months, the expected result should bring major improvements. You can already see this in the new Import/Export and Bindings dialogs, which now use this technology. Over the coming months, more parts of the app will be replaced and rewritten one by one (Aura Tree, I’m looking at you).
Performance: The main focus of the next update is performance. I’ve completely redesigned the configuration system; it is currently in testing and should make app startup and overall operation much faster, especially on lower-end PCs. As mentioned above, I’ll also continue rewriting slower parts of the app, such as the Aura Tree, to improve the overall user experience.
Behavior Trees: Currently in development and testing, Behavior Trees are the next major feature to look forward to. They fundamentally change how you work with the app. They also use the new UI technology, so they are much more responsive :)
Stay tuned for updates, and thank you for your continued support and for using the software!
For more detailed information, please refer to the individual update changelogs available on the website.
They’re now more responsive, more informative, easier to use, and hopefully less error-prone :)
P.S. The sync dialog is not ready yet — it will be included in a separate set of changes related to the publishing/sync system.


Over the next few days I’ll publish more details, but in short: the C# engine that was already used in WebUI to take advantage of Blazor has now been ported and is also available in C# actions and triggers.
This means that everything you could previously use in WebUI is now also available in triggers/actions, including:
SendInputScriptingApi, to provide easy access to image analysis, input sending, and all other functionality currently available in EyeAuras.Autocomplete is currently disabled. I haven’t decided yet whether to switch directly to the Monaco editor or improve the existing text editor first. I’ll share an update as soon as possible.
One of the biggest advantages of this change is that now, with a unified scripting system, any improvement will instantly apply everywhere — WebUI / C# actions / triggers and Behavior Trees.
P.S. Behavior Trees will most likely be released in December, and they’ll be based on the same engine. Stay tuned — it’s going to be very, very cool, I promise :)
The new website hosting the EyeAuras API documentation is now available at https://docs.eyeauras.net.
This will be especially useful for users who use EyeAuras as a scripting platform. However, the Wiki remains the main resource for non-developers.
At the moment, the documentation is still missing a demos and examples section, which is an essential part of any documentation site. In December, linqse and I will be focusing on improving that area. In addition, most public interfaces and classes now include XML comments.
If any important information is missing, please let us know.
Understanding exactly what went wrong is critical when problems occur. Our new error reporting system collects all errors within an Action/Trigger or across the entire Aura.
These errors are hierarchical, meaning Aura-level errors include all errors from their actions/triggers, similar to folders. Users can also mark errors as read whenever they want.

A new feature has been introduced to significantly reduce disk reads/writes and slightly lower CPU usage. This feature is still being tested and can be enabled in settings via the Property Caching option.
A collection of fixes and quality-of-life improvements. The biggest change is support for working with multiple projects at the same time — you can now save, load, and share configuration files, and train multiple models.
Oh, and yes — I renamed it to better reflect what the tool actually does.
Bindings | EyeAuras Wiki is a mechanism that lets you connect multiple action/trigger properties together. For example, you can take the coordinates of a detected image from Image Search and move the mouse there using Send Sequence.
I completely rewrote the Bindings Editor, where all of this is configured. It is now faster, more responsive, and generally easier to use.
There are a LOT of different improvements and fixes here: performance, UX, and bug fixes. Listing every single one would not be very useful, so it is better to try it yourself: Getting Started | EyeAuras Wiki
I know there are already some successful use cases, so the technology does work, and training through CVATAAT is definitely easier than doing everything manually.
Welcome to the improved Send Sequence action. It now combines all Send action types into one: it can send clicks, type text, move the mouse, and click the mouse, including support for links and anchors.
For about 1.5 years, there were 3 separate Send action types: Send Input, Send Text, and Send Sequence.
Each one covered its own specific set of features:
That is now a thing of the past. The redesigned version has the same capabilities as all previous Send action types, and it is much easier to extend with new features and new blocks with any kind of functionality, for example:
The main benefit is that building input sequences is now much easier. For example, if you want to click a specific image, you no longer need to alternate between Send Sequence and Send Input.
I also plan to focus on improving Send Sequence going forward, making it more flexible instead of splitting time across 3 different Send action types.
From now on, Send Text and Send Input are effectively in maintenance mode. I will not be adding new options to them or actively working on them anymore.
At some point in the future, they will be fully deprecated and automatically converted into Send Sequence.
You can convert them manually at any time by clicking the Convert to Send Sequence button shown at the top of each Send action. For now, the conversion does not remove the original action, so you can verify that the new one behaves exactly as expected.
As the title suggests, I added a new option to all of these triggers that allows capturing any window without its border.
In addition, all actions that work by linking/anchoring to the position of a found image will take the missing border into account and adjust coordinates accordingly.
This makes tracking objects in applications that are not running in fullscreen mode easier than ever.
A new option has been added that allows searching for specific pixel blocks of a given color. There is a limited set of supported block sizes, such as 2x2, 4x4, and so on, and the code has been optimized for better overall performance.
Based on my tests, 4x4 blocks give the best results.
Keep in mind that under the hood, this mechanism is very similar to how image search works, so in some scenarios it may still be better to stick with image search. In other cases, though, this new color search mode really shines.
And of course, you can configure anchors/links to the found blocks as well.
A fairly detailed documentation page has been added, and it should answer most of your configuration-related questions.
The tool is working and can already be very useful even with its current feature set.
Originally, the tool only allowed training models locally on your own PC using your own hardware. That has obvious limitations: you need to actually have the hardware.
Cloud training makes it possible to use enterprise-grade GPUs such as the A100 for faster and more efficient training in some cases.
It does come with its own limitations, but it can be very useful in many situations. Support is still somewhat limited for now—for example, auto-annotation is not yet available in this mode—but I will continue expanding it.
Machine learning—especially object detection, classification, and segmentation—can dramatically expand what game bots are capable of. Imagine bots that can accurately identify in-game items, enemies, and terrain, or classify player behavior to make smarter and more effective decisions. The hard part is getting started: there is often a ton of information to sort through, and setup can be complicated.
Meet the CVAT auto-annotation tool — your new go-to tool for training ML models.
CVATAAT is here to make that process easier. Built for people who are new to botting, it helps you prepare and train your game data with minimal hassle. It’s free, runs on your own computer, and is focused specifically on gaming use cases. Just feed it your game images, label them, and CVATAAT does the heavy lifting by quickly training and delivering optimized models that you can then use in EyeAuras with the ML-Search trigger.
This is still an early alpha, so things may go wrong. Please report any issues so we can make the model preparation process as simple as possible together. I genuinely believe this is the future, and with this tool I want to clear some of the obstacles out of the way.