🔍 Read the full analysis: What An AI Agent Can Do When You Point It At A Codebase on ThorstenMeyerAI.com
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TL;DR
A report published October 19 describes how Reverse Engineer Anything (REA), an open-source toolkit, lets AI coding agents inspect existing programs and trace specific behaviors. A case study reports that a developer used it to build a local accounting app whose reports matched QuickBooks Desktop’s official sample company to the cent; that result is attributed to the case study, not independently verified here.
Reverse Engineer Anything (REA), an open-source toolkit for AI coding agents, is designed to help developers inspect how existing software behaves rather than infer its workings from screens alone. In a report published October 19, a developer’s case study describes using REA to study QuickBooks Desktop and build a local accounting app whose financial statements for the software’s official sample company reportedly matched account by account, to the cent.
The report presents Ledger Reborn as an example of using software analysis to turn observed behavior into an implementation specification. Its author says the investigation examined how QuickBooks handled inventory arithmetic, account types in budgets, reconciliation balances and cash-basis reporting. The stated goal was to understand the records and rules behind the results, not simply reproduce the application’s screens. The reported matching results come from the project’s case study; the supplied material does not include an independent audit of them.
REA combines a local MCP server, a command-line tool and an agent skill that guides investigations. MCP is a way for agents to connect to tools. The report says REA can work with several kinds of targets, including native applications, Electron and JavaScript software, .NET and Android apps, firmware, databases and WebAssembly. For websites, it can observe activity in a browser under the user’s control. The project’s version 6.7.0 was released October 18, according to the report.
The reporting recommends asking one bounded question at a time: identify a behavior, explain the decision the answer will inform, and ask for the relevant stored data, calculations and conditions. Findings should be marked as observed, inferred or unknown. A useful check, the report says, is whether the agent can explain one complete example using concrete inputs and outputs. If it describes only a screen, or answers without calling REA, its explanation may be an unsupported model guess rather than an analysis result.
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 Working Rules
For developers maintaining or replacing older software, knowing what a program does can be as important as knowing how it is built. A visible interface may not reveal how a partial payment changes a balance, which account receives a posting or how a report chooses between accounting methods. Errors in those rules can make a new application appear usable while producing incorrect records.
REA’s reported value is that it gives an agent tools to gather evidence from an existing program and organize it around a practical engineering question. That can help developers write and test a specification before building a replacement or integrating with software that has limited documentation. The QuickBooks example illustrates the potential in a financially sensitive domain, but one case study does not establish that REA will produce accurate results on other programs, or that every behavior can be recovered.
The approach also changes what a developer needs to verify. The agent’s answer should be treated as an account of evidence, not as proof by itself. Users need to check whether the right tool ran, examine the basis for each conclusion, and compare important outputs against known examples. That is particularly relevant when software affects accounting, security or other high-consequence decisions.
software reverse engineering tools
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How REA Connects to Coding Agents
REA is described in the report as an MIT-licensed, open-source project hosted on GitHub. Its tools are intended to be called by agents such as Claude Code, Codex, Cursor and Gemini CLI. The project offers a guided setup command; the report says users need Node.js 22.19 or newer, while native program analysis may require a disassembler such as Ghidra, Hopper or IDA.
Installation checks are not the same as confirming that an agent can use the tools. According to the report, REA’s diagnostic command checks files and prerequisites but does not confirm a live agent connection. The recommended follow-up is to verify tool access from inside the agent, then start with a harmless application the user owns. This distinction matters: without a working connection, an agent might still answer a question from its own prior knowledge without having inspected the target.
The Ledger Reborn investigation used REA 6.4.0 and Ghidra on a QuickBooks Desktop for Mac 2024 trial, the report says. The current version cited in that report is 6.7.0. The supplied material also says the project had about 325,000 GitHub stars and 82,000 forks on October 19, 2026. Those are dated figures and do not independently establish adoption or the accuracy of its analyses.
“This is not a sandbox.”
— REA project documentation, as quoted in the report
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Accuracy, Safety and Scope
The supplied report does not provide an independent replication of the cent-for-cent QuickBooks comparison, or enough detail to assess every test case and edge condition. The claim should therefore be understood as a result reported by the Ledger Reborn case study. It is not evidence that REA can fully reconstruct any application, or that an agent’s interpretation will be correct without human checks.
Security and privacy also depend on how the tools and agent are used. REA analyzes targets locally, according to the report, but the agent receives results and the model provider handles them under its own data policy. The project’s sandbox warning means users should not assume analysis isolates an unknown program from their machine. The supplied material does not specify the data-retention terms of individual model providers or establish that every analysis workflow is safe for confidential code.
It is also unclear from the material how well the toolkit performs across the many listed formats, which behaviors it cannot observe, and how findings vary by target or analysis engine. Tool access, available disassemblers, licensing and the complexity of the program can all shape what an investigation can establish. Those limits make explicit labels for observed, inferred and unknown results useful, but do not remove the need to test conclusions.
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Verify Before Building on Findings
The immediate next step for anyone trying the method is to confirm that the agent has a working REA connection and begin with a narrow question about software they own or are authorized to inspect. The report recommends checking tool availability inside the agent, then validating the explanation against a concrete example with known inputs and outputs before using it as a specification.
For the Ledger Reborn example, the relevant follow-up is whether the reported matches hold across more transactions, accounting scenarios and edge cases, and whether others can reproduce the results using the published case study. The supplied material does not identify a planned independent evaluation or a next release date for REA. Until further evidence is available, the practical conclusion is limited: REA offers a way to investigate program behavior, while the reliability and completeness of each result still need to be established for the software in question.
debugging and code investigation software
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Key Questions
What is Reverse Engineer Anything?
REA is an open-source toolkit that connects coding agents to software-analysis tools. It includes a local MCP server, a command-line utility and an agent skill intended to guide investigations.
Did REA independently prove it can reproduce QuickBooks?
No independent verification is included in the supplied material. The report attributes the account-by-account, cent-level match to the Ledger Reborn case study.
Does REA run its analysis in a sandbox?
The project’s quoted warning says, “This is not a sandbox.” The report says analysis runs locally with the user’s permissions, so users should treat unknown programs as untrusted files.
Can a setup check confirm the agent is connected?
Not by itself. The report says REA’s diagnostic checks files and prerequisites, but not the live agent connection. Users are advised to verify tool access from within the agent.
How should developers check an agent’s findings?
Ask for one specific behavior and a concrete example with inputs and outputs. Separate observed, inferred and unknown points, then compare important results with known behavior before building on them.
Source: ThorstenMeyerAI.com
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