How to Automate Bug Reports and Screenshots Using Achu (2026)

The Problem Achu Solves

Documenting software defects often involves a tedious cycle of capturing screenshots, manually redacting sensitive user information, and writing up bug tickets in Jira or GitHub. For teams handling proprietary data, uploading these images to cloud-based AI tools poses a significant security risk. This manual process is not only time-consuming but also prone to human error, often resulting in inconsistent documentation.

Developers and QA engineers frequently struggle to find a balance between speed and security when reporting bugs. Designers, meanwhile, face the challenge of formatting raw captures into clean, presentable visual assets for documentation or design handoff. These workflows are fragmented, requiring multiple disparate tools just to get a single issue reported.

Achu addresses these pain points by consolidating the screenshot, redaction, and reporting stages into a single local application. By keeping the AI processing on the user's hardware, it mitigates data privacy concerns while automating the repetitive parts of the bug-reporting lifecycle. In this tutorial, you will learn exactly how to use Achu — step by step.

How to Get Started with Achu in 5 Minutes

1. Navigate to the official Achu website at achu.app and download the installer for your specific operating system.
2. Run the installer and grant the necessary permissions for the application to access your screen and local file system.
3. Open the application and verify that your hardware meets the local processing requirements for the AI OCR engine.
4. Configure your preferences to define how you want images wrapped, redacted, and formatted during the capture process.
5. Connect your GitHub account within the application settings to enable the automated bug ticketing feature.

How to Use Achu: Complete Tutorial

Step 1: Capturing and Processing Screenshots

Once Achu is running, you can trigger a screenshot capture using your designated hotkeys. The tool immediately passes the raw image into its local sandbox OCR engine, which identifies UI elements and text. Instead of a messy desktop capture, Achu automatically applies a professional wrap to the image, ensuring your assets look clean and consistent every time.

This process happens entirely offline, which is critical for teams working under strict data compliance. Once the image is processed, you can view the result in the Achu dashboard, where the AI has already segmented and highlighted relevant components. This visual enhancement turns standard captures into ready-to-use assets for design documentation or bug reports.

Step 2: Automating Redaction for Security

Privacy is the primary focus of the Achu workflow. When you capture a screen that contains sensitive data—such as API keys, personal user information, or proprietary financial figures—Achu allows you to apply automated redaction. Because the processing occurs within your local sandbox, no raw image data is sent to external servers for interpretation.

You can define redaction rules within the application settings, instructing the AI to target specific types of data for masking. This ensures that even if you accidentally capture sensitive information, it is stripped or blurred before you ever export the image or attach it to a ticket. It is an effective way to maintain security protocols without manually editing every single file.

Step 3: Generating Bug Tickets via the AI Bug Agent

After your image is prepped and redacted, you can invoke the AI Bug Agent to assist in creating your documentation. By analyzing the screenshot context, the agent generates a structured description of the bug, including relevant technical observations found in the UI. This draft can then be sent directly to your GitHub repository through the integrated link.

This integration bridges the gap between identification and resolution, allowing you to move from spotting a bug to filing a ticket in seconds. The AI agent includes the polished screenshot as an attachment, providing developers with clear, high-quality visual context without the need for manual image management. The result is a more standardized and communicative bug-tracking process.

Achu: Pros & Cons

Pros	Cons
Privacy-focused local-only processing.	Requires significant local hardware resources.
Automated redaction protects sensitive data.	Limited transparency on advanced features.
Direct GitHub integration for bug ticketing.	No mobile version available.
Professional visual asset generation.	No free tier explicitly documented.

Achu Pricing: Free vs Paid

At the time of writing, Achu does not explicitly list a free tier or an open-source option on its landing page. The nature of the tool—relying on local AI processing—suggests a model that may require a direct purchase or a subscription fee to maintain the development of the underlying AI models and features.

Given the niche, privacy-focused nature of the product, users should expect a professional-grade pricing model. If you are considering this for a team, verify if there are enterprise licenses available that might offer bulk discounts compared to individual user seats. Always confirm the current cost before installation to avoid any subscription surprises.

👉 Check the latest pricing on the official Achu website.

Who is Achu Best For?

For Developers: The GitHub integration saves substantial time when managing daily issues. It ensures that technical reports are always accompanied by clear, professional imagery, which helps in debugging and code review sessions.

For QA Testers: The ability to automatically redact sensitive information makes this an ideal tool for testing environments involving real user data. It allows for the rapid creation of bug tickets without the risk of exposing protected data points during the submission process.

For Product Designers: The tool acts as a utility to maintain visual consistency in design documentation. By using the automated wrapping and processing features, you can ensure that every screenshot captured during a design audit or review is perfectly framed and ready for presentation.

Alternatives to Achu

Tools like CleanShot X or Snagit offer advanced screenshot management but lack the specialized, privacy-focused local AI bug agent found in Achu. Another alternative, Shottr, is excellent for rapid capture and markup but does not include the automated GitHub ticketing or local sandbox AI processing that makes Achu distinct. Achu is the better choice for those who prioritize data privacy above all else and need a unified workflow for ticket management.

Final Verdict: Is Achu Worth It?

Achu succeeds in providing a secure, automated bridge between visual captures and project management. It is a highly focused tool for professionals who value privacy and need to streamline their documentation workflows. If your hardware can handle the local processing load, it is an efficient addition to your productivity stack.

📋 Disclosure: This is an independent tutorial based on Achu's publicly available documentation and website content as of June 7, 2026. GitNeural is not affiliated with, sponsored by, or endorsed by Achu or achu.app. Pricing and features may have changed — always verify on the official Achu website.