🔍 Read the full analysis: The Practical First Steps For Understanding Software With AI on ThorstenMeyerAI.com
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TL;DR
An October 19 article describes how Reverse Engineer Anything (REA), an open-source toolkit for AI coding agents, can help developers investigate how existing software behaves. Its examples emphasize narrow questions, checking that the tools are connected, and validating the agent’s explanations against concrete evidence. REA’s findings are not automatically correct, and local analysis does not mean data stays private.
ThorstenMeyerAI.com published a guide on October 19 showing how developers can use Reverse Engineer Anything (REA) with AI coding agents to investigate what existing software does behind its screens. The report argues that focused investigations—followed by checks against concrete examples—can turn observed behavior into requirements for a new application, while warning that an agent’s answer may be a guess if REA is not connected or its evidence is not checked.
REA is an open-source project on GitHub that provides a local Model Context Protocol (MCP) server, a command-line tool and an agent skill for guiding investigations. The source says it can connect agents including Claude Code, Codex, Cursor and Gemini CLI to analysis tools for several kinds of software, from JavaScript and Electron applications to native programs, databases and firmware. Its current version was listed as 6.7.0, released October 18.
The guide recommends asking about one specific behavior and naming the decision the answer will inform. For example, instead of asking an agent to reverse engineer an entire accounting application, a developer might ask how a partial invoice payment changes the balance and which account receives the posting. The agent should report stored data, calculations and conditions that affect the result, while distinguishing what it observed from what it inferred or could not establish.
Before investigating a target, developers should confirm that REA is installed and available to the agent. The article describes a setup command and a diagnostic check, but says the diagnostic checks files and prerequisites rather than confirming a live agent connection. It recommends verifying the connection inside the agent and testing first on software the user owns and considers harmless. A useful test of the final answer is whether the agent can explain a complete example using actual inputs and outputs, rather than merely describing the interface.
Infographic 01 · Reverse Engineer Anything
Five places to point REA first, and how to tell when the answer is wrong.
REA gives coding agents such as Claude Code and Codex tools to inspect programs, apps and browser sessions. Each investigation needs a finishable question and a check.
npx rea-agents@latest setupThe five investigations
What to ask, and the check that catches a wrong answer.
- 1
Ask a question you can finish
One behaviour, one decision it informs. Example: how a partial payment changes an invoice balance.
It’s wrong ifThe agent can't walk through one concrete example with real inputs and outputs. - 2
Get REA connected and confirmed
Run the guided setup, then doctor, then call binary_session inside the agent before any real target.
It’s wrong ifAnswers arrive without a single REA tool call: you're reading the model's guesses. - 3
Turn a business rule into a spec
Inputs, stored fields, formula, edge cases, plus test cases checked against the original's own output.
It’s wrong ifYour version and the original disagree on a test case, even by one cent. - 4
See what has to survive a copy
Find what an app writes to the clipboard and what it keeps locally, then design your own copy and paste.
It’s wrong ifThe explanation doesn't predict what lands in a plain text editor, an email and your app. - 5
Your own app, your equipment, an old program
Start with software you own or rely on. Ask what it sends, stores or calculates when you do one thing.
It’s wrong ifIt only works for the example you watched. Test with a new file or a read-only command first.
Sources: REA on GitHub (stars via GitHub API, 19 Oct 2026); REA installation guide; REA security policy.
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From Software Behavior to Requirements
The method addresses a common gap in AI-assisted development: an application can look convincing while its calculations, data model and business rules remain wrong. Understanding those rules matters when replacing an older program, building an integration or trying to reproduce a workflow users already rely on. A narrow investigation can give a developer something more actionable than a screen mock-up: a written account of what the software stores and how its results change under different conditions.
The source presents a QuickBooks Desktop project, Ledger Reborn, as an example. Its author reportedly used REA to investigate accounting behavior and built a local application whose profit-and-loss statement, balance sheet and trial balance for Intuit’s sample company matched the original account by account, to the cent. That is a claim described in the source, not an independent test in this article. The example illustrates the potential value of checking behavior against a known reference; it does not establish that REA will produce accurate results for other programs or projects.
There are also privacy and security limits. REA performs analysis locally, but the article says results are passed to the AI agent and the model provider under that provider’s data policy. It also warns that REA is not a sandbox: opening an untrusted program can expose it to analysis tools running with the user’s permissions. Developers should consider what data they send and avoid treating unfamiliar binaries as safe.
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How REA Fits Into Agent Work
The article describes REA as a way for an AI coding agent to call specialist analysis tools, rather than as a system that independently proves how a program works. Its listed targets include native applications analyzed with tools such as Ghidra, Hopper or IDA, as well as JavaScript applications, .NET and Android software, WebAssembly, SQLite databases and firmware. For websites, it can observe activity in a Chrome-family browser controlled by the user. Available capabilities depend on the target and on having the necessary analysis software installed.
The source says REA had about 325,000 GitHub stars and 82,000 forks on October 19, after appearing on GitHub’s trending page in early October. Those figures are a dated snapshot, not evidence that the tool’s analyses are accurate. The article also identifies the project’s maintainer as anonymous. Developers evaluating the toolkit may want to review its documentation, dependencies and security model rather than using popularity as a substitute for technical review.
The guide’s central workflow is to define a bounded question, confirm the agent can call REA, inspect a target, and test the resulting explanation against a concrete case. The source notes that native analysis may require a disassembler such as Ghidra, which requires Java, or a licensed product such as Hopper or IDA. It lists Node.js 22.19 or newer as a setup requirement and advises restarting the agent after installation.
“This is not a sandbox.”
— REA project documentation, as quoted by ThorstenMeyerAI.com
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Accuracy, Access and Data Risks
The source does not provide an independent evaluation of REA’s accuracy across software types, nor does it establish how often an agent’s findings are incomplete or wrong. The QuickBooks example is a case study reported by the article; the supplied material does not include independent verification of its matching results. Findings may also depend on the target version, available analysis engines and the scope of the question.
It is not clear from the source which model providers receive results in every setup, what data each provider retains, or how REA’s capabilities perform across all listed targets. Users need to check the applicable provider policies and tool documentation. The article also says the project maintainer is anonymous, but the supplied material does not explain the project’s governance or review process. No evidence is provided that a local analysis prevents information from being shared with an agent or its provider.
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Verify Before Building Further
Developers following the guide can begin by choosing a single behavior and a real decision, then checking that their agent has loaded REA and can invoke its tools. They should test on software they own or are authorized to inspect, and compare the agent’s account with a known input-and-output example. If the explanation cannot account for a full example, the behavior remains unresolved rather than established.
Before using REA on sensitive or unfamiliar software, users should review the project’s current setup instructions, identify which analysis tools will run, and check the AI provider’s data policy. The source provides no announced next release or future milestone. The next practical step is validation: document what was observed, separate it from inference, and test any resulting implementation against the original program where authorized.
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Key Questions
What is Reverse Engineer Anything?
REA is an open-source toolkit that lets supported AI coding agents call software-analysis tools through an MCP server. It also includes a command-line tool and guidance for running investigations.
How should a developer start an investigation?
Ask about one specific behavior, explain what decision the answer will inform, and request the stored data, calculation and conditions that affect the result. Ask the agent to label statements as observed, inferred or unknown.
How can users tell whether an agent’s answer is grounded?
Check that the agent actually called an REA tool, then ask it to explain a complete example with real inputs and outputs. A description of the screen alone does not establish how the underlying software behaves.
Does local analysis mean information stays private?
No. The article says REA runs locally, but its results are also passed to the AI agent and the model provider under that provider’s data policy. Users should review those terms before sending sensitive material.
Is REA a safe place to open unfamiliar software?
The project’s quoted warning says REA is not a sandbox. The article advises treating unknown binaries as untrusted because analysis tools run with the user’s permissions.
Source: ThorstenMeyerAI.com
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