REA Explained: Explore Apps and Binaries with Reverse Engineer Anything

CODE LAB · PROJECT SPOTLIGHT

REA MCP · MIT license

Reverse Engineer Anything: investigate how applications work with an AI agent.

GitHub source ↗Releases ↗

Preview: morluto/rea on GitHub

You can see what an application does. Understanding how it does it takes a different set of tools. REA, short for Reverse Engineer Anything, connects AI agents to software inspection workflows. Its public GitHub project, morluto/rea, is a useful place to explore how an agent can investigate an application beyond its visible interface.

This Code Lab introduction reviews the project’s documentation and published examples, checked November 8, 2026. It does not report a new benchmark or a completed reconstruction.

What is REA?

REA provides an MCP server and a command-line interface. MCP is the connection through which an agent can discover and call tools. The agent asks a focused question, REA inspects a target, and the returned findings guide the next question or an implementation.

The project covers native binaries, JavaScript/Electron applications, managed .NET assemblies and other target types. Deep native analysis uses a compatible analysis engine such as Ghidra, Hopper, IDA or Kuna; requirements depend on the operation and host. Static JavaScript inspection does not require a native decompiler. Supported targets and requirements.

Inside the repository: the MCP connection in code

The following TypeScript excerpt is taken directly from src/main.ts, lines 30–39, at commit 5679ed6. It assembles the runtime dependencies used by REA’s entry point.

30const runtimeDependencies = (): RuntimeDependencies => ({31  env: process.env,32  stdin: process.stdin,33  serve: serveStdio,34  writeStderr: (text) => process.stderr.write(text),35  setExitCode: (code) => {36    process.exitCode = code;37  },38  registerShutdown: registerProcessShutdown,39});

• env and stdin pass the process environment and input stream into the runtime.
• serve: serveStdio supplies the standard-input/output MCP serving function.
• writeStderr provides a separate output path for diagnostic text; setExitCode updates the process exit status.
• registerShutdown supplies the shutdown-registration function.

This is dependency wiring, not the decompiler itself. It makes the connection described above concrete: the runtime receives functions and streams through one object. The excerpt shows the source structure; it is not a record of the program executing.

From a feature to an explanation

Consider a desktop application’s search box. A screenshot shows the input and results, but leaves several questions open: where is the query processed, which module produces results, and how does the interface receive them?

A useful investigation starts with one question and follows the relevant connections. For a JavaScript/Electron package, those connections might cross renderer code, a preload bridge, IPC messages and the main process. The JavaScript workflow documentation describes feature tracing and comparisons built on application evidence.

This sequence keeps the work reviewable. A clear explanation should let a reader follow the supporting locations instead of accepting a confident paragraph on its own.

What can you actually read?

Different targets expose different kinds of information. Native analysis can produce assembly and pseudocode; JavaScript analysis can expose modules and their relationships. These outputs need interpretation. A pseudocode variable name may be reconstructed, and a connection found in a file does not establish that a particular user action exercised it.

REA’s workflow documentation preserves distinctions between static observations, runtime observations, inferences and unavailable information. Its feature-tracing tools consume earlier application evidence rather than silently executing the target again. Tracing and evidence boundaries.

For the reader, a practical habit is to ask: which file or function supports this claim, what part of the application was inspected, and what would a real execution test still need to establish?

A concrete example: DX-Ball’s sound panning

The project’s DX-Ball case study follows a narrow question: how does a brick’s horizontal position become a left/right sound-pan value?

The initial pseudocode did not expose the full calculation. The investigation followed the caller, read the instructions and inspected numerical constants. The reconstruction then expressed the recovered calculation in C. The published example reports 3,205 comparisons against original x86 behavior and a compiler replay matching 63 function bytes.

Those numbers belong to that specific function and its published checks. The case study also says work toward a playable reconstruction continues. It is a useful example of measuring a limited result without treating it as proof that an entire application has been rebuilt.

Getting started without getting lost

The documented agent setup entry point is:

npx rea-agents setup

Setup lets you select supported clients, review configuration changes and approve them. It registers the MCP server and installs matching workflow instructions; the selected agent must then be restarted. Installing a skill alone does not establish that the server and analysis engines are connected. Follow the installation guide for your client and target.

For a first exercise, choose a small application or package you own or are authorized to inspect, and ask one concrete question. For example:

Inspect this local test application’s search feature. Show the entry point, the modules it calls, and the evidence for your explanation. Separate confirmed behavior from assumptions.

This is an example investigation prompt, not a result obtained for this article. Specify the actual target and scope before running it.

Check the release as well as the README

At this review, GitHub’s release list showed rea-agents 6.8.0, published October 25, 2026. Its highlights include local agent environments from captures, support for more shipped formats, and ways to keep large investigations focused.

The release also contains breaking changes, including function-analysis response defaults and JSON Lines output framing. When following an example, check that its options and expected output match your installed version. A live development README can describe changes beyond an older installation.

For direct terminal work, the CLI and Evidence guide explains how to save results and request a summary or selected modules. Large applications can produce large evidence records; reading the relevant view is usually more useful than dumping everything into a chat.

Why it belongs in Code Lab

REA offers a way to connect visible software behavior to inspectable implementation details. The most interesting follow-up is a small, reproducible investigation: choose one feature, preserve the evidence, explain the result and test any new implementation separately.

Start with the official repository, release history and project website. The repository uses the MIT license; analysis engines have their own requirements and licenses. Repository inspection happens locally, while tool results passed to an AI agent remain subject to that agent provider’s data handling.

For another look at reading a public project’s changes, see our Lichii GitHub introduction.