Reverse Engineer Anything: The GitHub Repo That Clones App Features With AI

Have you ever used an app and thought, “I wish I could build this feature in my own project”? Perhaps you like the way a desktop application searches files, how an image editor handles its tools, or how a website organizes its interface. You can see what the software does, but understanding how it works behind the scenes is a different challenge.

This is where Reverse Engineer Anything (REA) comes in. REA is an open-source GitHub project that connects AI coding agents with reverse-engineering tools. It helps developers investigate applications, inspect code and compiled binaries, understand how features work, and use those findings to build similar functionality in their own projects.

Instead of manually switching between different analysis tools, developers can ask a compatible AI agent to investigate a specific feature and explain the evidence behind its conclusions. REA supports analysis workflows for native binaries, JavaScript and Electron applications, .NET software, and other documented targets.

In this guide, you will learn what the REA GitHub repository does, how it works, how to install it, how to investigate an application, and how to use the results to develop your own software.

Official repository: https://github.com/morluto/rea

What Is REA?

REA stands for Reverse Engineer Anything. It is a developer toolkit designed to help AI agents understand software beyond what is visible in its user interface.

Normally, when you want to understand an unfamiliar application, you need to identify its technologies, inspect its files, search for relevant functions, and work out how different components communicate. If the application is compiled, you may need a disassembler or decompiler to examine its internal structure.

REA brings these investigation steps into a more connected workflow. It provides a command-line interface (CLI) and integrates with compatible AI agents through the Model Context Protocol (MCP).

For example, imagine that you want to understand the search functionality in a desktop notes application. You could ask your AI agent to investigate how the feature works, identify relevant functions, show the supporting evidence, and suggest how to build a similar search feature in your own application.

REA helps the agent gather technical information. The agent then uses those findings to explain the implementation and, where appropriate, write and test new code.

The important thing to understand is that REA helps developers investigate and recreate software features, but it does not guarantee a perfect copy of an existing application or recover all of its original source code.

Why Are Developers Interested in AI Reverse Engineering?

Software development often involves working with incomplete information. You might inherit an old project without documentation, need to understand a third-party application’s behavior, or want to make your software compatible with an existing file format.

Traditional reverse engineering can be time-consuming because developers must learn different tools and manually connect the results.

AI agents can make this process more approachable by helping with investigation, explaining technical findings, and suggesting the next steps. REA provides a way for these agents to access specialized analysis tools instead of relying only on general programming knowledge.

Here are some practical reasons developers might use it:

• Understand how an unfamiliar application implements a particular feature.

• Investigate compiled software when its source code is unavailable.

• Explore JavaScript and Electron application structures.

• Study function relationships in supported native binaries.

• Research software compatibility and interoperability.

• Understand legacy applications that lack useful documentation.

• Use investigation results to build an independent implementation.

The biggest advantage is the connection between investigation and development. You can study an existing feature and then move directly into designing and testing your own version.

How Does REA Work?

REA follows a straightforward process: investigate, understand, and recreate.

1. Investigate the application

The developer identifies an application, executable, package, or other supported target. The AI agent uses REA’s available tools to inspect the relevant files and collect information.

Depending on the target, this may include module relationships, strings, functions, assembly instructions, decompiled pseudocode, references, or other structural details.

2. Understand how the feature works

The agent reviews the evidence and follows relevant connections between components. It can explain which functions appear important, how the parts relate to each other, and what remains uncertain.

This step matters because finding a function name or a text string does not automatically explain the complete behavior of a feature.

3. Recreate the functionality

After understanding the relevant behavior, the developer can ask the coding agent to implement a similar feature in a separate project.

For example, you might investigate how an application searches through documents and then implement your own search system using JavaScript, TypeScript, Python, or another suitable technology.

The new implementation should be tested against your own requirements. The original software may contain undocumented edge cases, dependencies, or behavior that cannot be established from static analysis alone.

Key Features of the REA GitHub Repository

AI agent integration through MCP

REA uses the Model Context Protocol to connect supported AI coding agents to its investigation tools.

Once configured, an agent can request relevant analysis as part of a development task. This makes it easier to move between asking questions, examining evidence, and writing code.

The repository documents support for multiple coding agents. Check its current installation guide for the latest list of compatible clients and configuration instructions.

Native binary analysis

Many desktop applications are distributed as compiled executables instead of readable source files. REA can help investigate supported native binaries using compatible analysis providers such as Hopper, Ghidra, or IDA.

Depending on the provider and target, the results may include:

• Function information and available symbols.

• Embedded strings.

• Assembly instructions.

• Decompiled pseudocode.

• Function calls and references.

• Other information about the program’s structure.

These details can help developers investigate how a feature may be implemented inside compiled software. Native analysis requires the appropriate provider, and the available results depend on the binary and its configuration.

JavaScript and Electron analysis

REA also supports static investigation of JavaScript and Electron application artifacts.

JavaScript applications may contain bundled modules, imports, routes, and source maps. Electron applications introduce additional relationships between web-based interfaces and desktop processes.

Understanding these connections can help developers find the code associated with a screen or feature and learn how different parts of an application interact.

.NET software analysis

REA provides workflows for inspecting supported .NET artifacts. These can help developers understand managed assemblies and investigate application structure without requiring the original project source.

The exact analysis available depends on the target and supported tooling. Developers should consult the repository documentation before assuming a particular format or operation is supported.

Command-line access

REA can also be used directly from a terminal. Developers can run supported commands, check their environment, and investigate artifacts without conducting the entire process through an AI conversation.

This is especially useful when testing repeatable workflows or incorporating analysis into a developer’s existing process.

Evidence-based results

A good reverse-engineering workflow should explain why a conclusion was reached.

REA is designed to return findings with supporting evidence and relevant limitations. This helps developers evaluate the results rather than blindly trusting an AI-generated explanation.

How to Install REA on Your Computer

Before installing REA, check the official documentation for current operating-system requirements and supported tools.

The repository currently documents Node.js 22.x from version 22.19, Node.js 24.x from version 24.11, and Node.js 26 or newer, along with npm. Some analysis operations require additional software.

Step 1: Install Node.js

Visit the official Node.js website:

https://nodejs.org/

Install a compatible version of Node.js and npm. Then open your terminal, PowerShell, or command prompt and verify the installation:

node --version
npm --version

Both commands should display their installed versions.

Step 2: Run the REA setup command

Open your terminal and run:

npx rea-agents setup

Alternatively, explicitly request the latest published package:

npx --yes rea-agents@latest setup

The setup process helps configure REA for your chosen AI coding agent. Select the clients you want to connect, review the proposed changes, and approve the changes you intend to make.

REA setup can register the MCP server and install the matching workflow instructions. Follow the prompts carefully, particularly when configuring optional analysis providers.

Step 3: Restart your coding agent

Once setup is complete, restart or reconnect your AI coding agent so it can load the updated configuration.

If you use a client that supports MCP servers, make sure the REA server is registered correctly. Refer to the repository’s installation guide if your client requires manual configuration.

Step 4: Check the installation

Run the diagnostic command:

npx -y rea-agents@latest doctor

This helps identify missing prerequisites, configuration problems, and unavailable analysis tools.

If you plan to analyze native binaries, ensure that a compatible provider is installed and configured. Static JavaScript analysis does not require a native decompiler.

Official setup instructions: https://github.com/morluto/rea/blob/main/docs/installation.md

How to Use REA to Understand and Recreate an App Feature

Once REA is configured, you can describe the application or feature you want to investigate.

Let’s use a practical example. Suppose you are building a desktop notes application and want to understand how another application organizes its search functionality.

Step 1: Choose a specific feature

Start with a focused question instead of asking the agent to explain the entire application.

For example, you may want to investigate:

• How search results are generated.

• How the application finds files.

• How keyboard shortcuts trigger an action.

• How the application stores user preferences.

• How different modules work together.

A clear goal helps the agent focus its investigation.

Step 2: Ask your agent to investigate

After configuring REA, give your AI coding agent a prompt such as:

“Investigate how search works in this application. Identify the relevant modules or functions, show the evidence behind your findings, and explain what remains uncertain.”

The agent can use the available REA tools to inspect the target and collect relevant evidence.

Step 3: Review the findings

Read the explanation and check which conclusions are supported by actual results.

If the agent finds a potentially relevant function, ask it to trace the function’s references or examine its inputs and outputs, where supported.

You should not assume that a function name or a single code fragment reveals the entire feature. Additional analysis may be needed to understand the full behavior.

Step 4: Plan your own implementation

Once you understand the main behavior, define how your version should work.

For example, you might decide that your notes application needs keyword search, case-insensitive matching, and highlighted results.

Choose a suitable implementation for your own architecture, rather than copying internal details that are unnecessary for your requirements.

Step 5: Generate and test the code

Ask your coding agent to implement the feature using your chosen technology stack.

For example:

“Build an offline search feature for my notes application using TypeScript and SQLite. Include case-insensitive matching, useful error handling, and tests for empty searches and missing results.”

The coding agent can then use its normal development tools to write and test the implementation. REA supports the investigation stage, while the coding agent handles the new implementation.

Using REA from the Command Line

You do not always need an AI agent conversation to start working with REA.

Analyze a JavaScript or Electron application

The repository documents a command for static analysis of supported JavaScript application artifacts:

npx -y rea-agents@latest analyze-javascript-application "/absolute/path/to/app" --json

Replace the example path with the actual location of an extracted application directory or supported ASAR artifact.

On Windows, a path might look like this:

npx -y rea-agents@latest analyze-javascript-application "D:/apps/example" --json

The command returns structured findings that can include module relationships, imports, and Electron-related boundaries.

Install the CLI for regular use

If you expect to use REA frequently, you can install its npm package globally:

npm install --global rea-agents

Then inspect the available commands:

rea --help

For current options, native analysis commands, and provider-specific workflows, use the documentation and CLI help for your installed version.

Update REA

For a globally installed CLI, the repository documents this update command:

rea update

If you use npx for one-off commands, request the latest package with npx -y rea-agents@latest. Refresh your agent setup when required and restart the client after configuration changes.

What Can Developers Build With REA?

REA is not itself a replacement for a complete software development environment. Its value comes from helping developers understand existing software and apply that knowledge to their own projects.

Image and photo editing tools

A developer might investigate how a supported image editor organizes its tools or manages particular interface interactions. The findings could help inform an independently developed image editor.

REA does not automatically reproduce an entire photo editor, its proprietary effects, or its underlying image-processing algorithms. Those features must be implemented and tested separately.

Video editing applications

A developer interested in video editing may want to study how an application organizes timeline controls, project files, or editing workflows.

REA can assist with investigating supported software artifacts, but building a video editor still requires video-processing libraries, a suitable interface, and extensive testing.

Desktop productivity applications

Developers can investigate supported desktop applications to understand features such as search, file handling, preferences, and keyboard shortcuts.

The results can guide a new implementation in Electron, .NET, or another suitable framework, depending on the project’s needs.

Software compatibility tools

When building software that must work with an existing file format or application, reverse engineering can help reveal structures and behaviors that documentation does not explain.

Developers can use these findings to design compatibility tests and implement their own supported workflows.

Security research and debugging

Authorized researchers may use binary inspection and other supported analysis techniques to investigate suspicious behavior, understand software structure, or examine compatibility problems.

REA can support this work, but its findings do not replace a complete security audit, dynamic testing, or expert review.

Which Technologies and Tools Does REA Use?

REA brings together several parts of the development and reverse-engineering workflow.

You do not need every tool for every task. Developers investigating JavaScript application bundles may not need a native analysis engine. Native binary analysis, however, requires a compatible provider.

Hopper and IDA are separate products with their own licensing requirements. REA does not provide a license or authorization to use commercial analysis software.

Does REA Really Clone an Application?

The word “clone” can be misleading.

REA helps developers understand software behavior and use their findings to create similar functionality. It does not guarantee that an AI agent can recover the original source code, recreate every feature, or produce an identical application automatically.

For example, an agent might identify how a search feature interacts with several functions and then build a working search feature for your project. That is different from reproducing the original application’s complete architecture, visual design, proprietary assets, and hidden dependencies.

The results depend on the available evidence, the complexity of the target, the configured analysis tools, and the quality of the implementation and testing.

For this reason, it is better to think of REA as an AI-assisted reverse-engineering toolkit rather than a one-click application cloning tool.

Is REA Suitable for Beginners?

REA can be useful for beginners who already have some familiarity with programming and AI coding agents.

If you are new to reverse engineering, start with a small JavaScript application or a simple Electron project. Ask the agent to identify modules, explain their relationships, and show how a particular feature connects to the rest of the application.

Native binary analysis is more demanding. You may need to learn about executable formats, functions, assembly instructions, symbols, and decompilation before you can fully evaluate the results.

You do not need to master every technical detail before trying REA, but you should verify important findings rather than treating every AI-generated explanation as fact.

Is REA Free and Open Source?

The official REA repository identifies its own code as MIT-licensed open-source software. You can review the source code, examine the project documentation, and contribute improvements according to the applicable license terms.

However, some analysis workflows depend on separate tools. Hopper and IDA have their own licensing conditions, and other dependencies may carry separate licenses.

Before using the project commercially or in a confidential development environment, review the main license, third-party notices, and any applicable external-tool terms.

Privacy and Responsible Use

REA is designed to analyze target artifacts locally rather than uploading the application to a REA-hosted analysis service. That can be useful when investigating internal software or applications that are not publicly available.

However, the AI agent still receives the findings it needs to answer your questions. The model provider may have its own data-handling policies, so check those settings before analyzing confidential files.

Use REA only for software you own or are authorized to investigate. Respect copyright, licenses, privacy requirements, and applicable laws. Do not use reverse engineering to gain unauthorized access to systems or data.

How to Contribute to the REA GitHub Project

REA is an open-source project, and developers can help improve its code, documentation, and testing.

If you want to contribute, begin by reading the repository’s contribution guidelines:

https://github.com/morluto/rea/blob/main/CONTRIBUTING.md

A practical contribution workflow is:

1. Review the existing documentation and open issues.

2. Identify a bug, documentation gap, or useful improvement.

3. Set up the development environment according to the project’s instructions.

4. Make a focused change and run the relevant tests.

5. Submit a pull request describing the problem, your solution, and the checks you performed.

You can also report reproducible bugs or suggest new capabilities through the project’s issue tracker.

Frequently Asked Questions

What is the REA GitHub repository?

REA is an open-source reverse-engineering toolkit that connects compatible AI agents to tools for investigating software, including supported native binaries and application artifacts.

Can REA analyze apps without their source code?

Yes, for supported target types and analysis operations. The amount of information available depends on the application format, the analysis provider, and the evidence that can be recovered.

Can REA generate a similar app?

REA helps an agent investigate an existing application and understand its features. The agent can then use the findings to implement similar functionality in a separate project, but a complete application clone is not guaranteed.

Which AI coding agents work with REA?

REA supports multiple compatible agents through MCP and its setup workflow. Check the current repository documentation for the latest supported clients.

Do I need Ghidra or Hopper?

You need a compatible analysis provider for deep native binary analysis. Static JavaScript analysis does not require a native decompiler.

Does REA work on Windows?

Support depends on the particular analysis workflow and current release. Check the official installation guide for the operating systems and target types supported by your intended task.

Where can I download REA?

Start with the official GitHub repository:

https://github.com/morluto/rea

Follow its installation instructions rather than downloading a similarly named project from an unverified source.

Final Thoughts

REA is an interesting example of how AI agents can help developers investigate unfamiliar software and turn technical findings into practical development work.

Instead of manually moving between multiple analysis tools, you can use a compatible agent to examine a supported target, trace relevant components, review the evidence, and plan a new implementation. This can make reverse engineering more approachable while keeping the developer involved in important decisions.

If you want to understand how an existing feature works or build similar functionality for your own application, start with a small, well-defined question. Review the findings, test your implementation, and expand the investigation only when necessary.

Explore REA on GitHub: https://github.com/morluto/rea