REA: Reverse Engineering With AI Coding Agents

REA gives a coding agent a way to investigate how software works, even when you do not have its source project. It connects the agent to application inspection, binary analysis, and runtime observation tools, then returns evidence the agent can use to explain a feature or help build an implementation.

That could be useful when you maintain an old internal application, investigate a desktop integration, or need to understand a behavior before replacing a system. The useful outcome is a supported explanation and a tested implementation—not simply an impressive-looking generated interface.

This guide draws on REA’s official repository, its installation documentation, and Nick Saraev’s demonstration. The project facts below come from the documentation. The evaluation advice is FindMilan’s interpretation; we have not independently benchmarked the toolkit.

What REA actually does

REA exposes investigation tools through the Model Context Protocol, or MCP. A compatible coding agent can call those tools, receive their findings, ask a more specific question, and use the evidence while writing code.

The underlying analysis still depends on the target. A native executable needs different tools from an Electron application or a website. REA provides a common way for the agent to reach those capabilities; it does not make every application equally easy to understand.

The current README describes support for native binaries, JavaScript and Electron applications, .NET assemblies, websites, and several other artifact and runtime workflows. Its CLI can perform investigations without an interactive agent session.

An ordinary coding agent already has useful tools for reading files and inspecting a browser. REA’s value is the additional analysis it makes accessible, particularly when the question requires tracing compiled behavior or relationships that a screenshot cannot reveal.

What the video shows—and what it cannot prove

In the supplied video, Nick Saraev investigates the viral repository and demonstrates an agent workflow around recreating aspects of a Mac Preview-style application. He also tries a comparison without REA and questions how much of the result comes from the toolkit versus the agent’s existing abilities.

That is a useful distinction for developers. An agent may recreate a familiar interface from visible behavior and general knowledge. Producing a plausible application does not, by itself, show that it recovered the original implementation.

The demonstration is one creator’s task and comparison. It does not establish a general speed advantage, reliable reconstruction of arbitrary applications, or production readiness across every supported target.

When assessing a similar result, ask: Which finding came from REA, which part was inferred by the model, and which behavior was actually tested? A traced function or observed IPC relationship can be valuable even if the final interface could have been generated without it.

Match the tool to the question

Different investigations produce different kinds of evidence. Start with a narrow question rather than a request to clone an entire product.

See the project’s target and prerequisite table before choosing a workflow. Support and host requirements vary, and repository documentation can describe functionality that has not yet reached the npm release.

For example, an Electron clipboard problem may be best investigated through the relationship between the renderer, preload layer, IPC, and main process. A native calculation may require examining instructions and comparing outputs. A visual page redesign may need ordinary browser inspection instead.

Installation: review the setup plan first

The official setup guide currently supports Node.js 22.x from 22.19, 24.x from 24.11, or 26 and later, plus npm. Check the current requirements before running it.

To request the latest published package explicitly:

npx rea-agents@latest setup

The setup flow lets you select clients, inspect the proposed changes, and approve them. It can register the MCP server and install matching workflow instructions. Native analysis may require an existing Hopper, Ghidra, or IDA installation; the setup documentation explains the separate provider requirements.

After setup, restart or reconnect your agent and inspect its available REA tools. A registration file on disk is useful evidence, but it does not show that the current session has loaded the server.

A skill and a working MCP connection are different

The documented skill-only command is:

npx skills add morluto/rea --skill reverse-engineer-anything

That supplies investigation guidance and references. It does not register REA’s MCP server or install native analysis engines. If the agent has instructions but no corresponding tools, follow the official connection and readiness guidance.

The setup documentation also distinguishes repository main from the package selected by @latest. Use the connected server’s actual tool list and schemas when deciding which feature is available.

A useful first investigation

Use a small application or artifact you own or are authorized to inspect. Pick one behavior whose result you can check independently.

Here is an illustrative prompt for an agent with a working REA connection:

Investigate the clipboard behavior in this application that I am authorized
to inspect. Start with static analysis of the supplied artifact.

Identify the relevant modules and any renderer, preload, IPC, or native
boundaries. For each conclusion, show the supporting evidence and label
inferences and unknowns.

Do not run the application, install dependencies, or change its files yet.
Return a short investigation report and a proposed test plan.

This gives the agent a clear target and a concrete report to produce. Once you understand the findings, request the specific runtime observation or implementation step that the task needs.

A useful report should tell you what was inspected, where the evidence came from, what remains uncertain, and what test would confirm the explanation. An unexplained block of pseudocode is much less useful than a trace that answers your original question.

From findings to an implementation you can trust

Treat the analysis as input to engineering work. A recovered code path may explain an algorithm or interface, but the replacement still needs to work in your application.

For a legacy import workflow, for example, record a small set of input files and expected outputs. Include empty input, malformed data, unusual characters, and a representative large file. Then compare the replacement against those behaviors.

For a desktop integration, inspect both the visible interaction and the application boundary that handles it. A button can appear to work while writing the wrong format, ignoring an error, or failing after restart.

Keep three things distinct during review:

• Observed facts: a module import, an instruction, a captured request, or a measured output.
• Interpretations: the agent’s explanation of how those observations fit together.
• Implementation choices: the code your team writes to reproduce a permitted behavior.

This makes errors easier to locate. If a replacement fails, you can check whether the original observation was incomplete, the interpretation was wrong, or the new code introduced a defect.

Local analysis still has data and execution boundaries

REA says analysis runs locally. Its documentation also explains that the agent receives tool results, and the model provider has its own data policy.

If a hosted model is involved, inspect what evidence will enter its context. Source fragments, file paths, strings, captures, or business data may be sensitive even though the initial analysis process runs on your computer.

The README distinguishes static JavaScript and .NET inspection, which read supplied files, from runtime capture that runs or interacts with a target under your user permissions. Choose the mode deliberately, particularly for unfamiliar applications.

For business use, establish your permission to inspect the target and reuse the relevant behavior. REA’s MIT license covers the toolkit; it does not automatically give you rights to third-party code, assets, or applications. Obtain advice appropriate to the project where rights are unclear.

When REA is worth evaluating

REA is worth a focused trial when your question depends on details that ordinary source access or visual inspection cannot answer, and you can verify the findings against the target.

Good starting points include investigating your own legacy software, tracing an authorized integration, or understanding a specific compiled routine. The same approach is less convincing when the only goal is to produce an interface that looks familiar.

My practical recommendation is to test one question, retain the evidence, and compare the result with the tools your agent already has. Evaluate the value of the investigation, not the breadth of the repository’s name.

If your team wants help building this into a useful workflow, FindMilan offers AI consulting and custom development, workflow automation, and SaaS product engineering. The Hermes and OpenClaw setup project shows how agent tools and infrastructure fit into an operational system.

Book a free strategy call to scope one investigation or migration with a clear result to verify.

Sources and further reading

• REA official repository and README
• REA installation, package availability, and client setup
• REA official guides
• REA MCP result contracts
• Nick Saraev: I Tested The Viral Github Repo That Reverse Engineers Anything