🔍 Read the full analysis: How AI Agents Can Help You Make Sense Of Existing Software on ThorstenMeyerAI.com
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TL;DR
A report published by ThorstenMeyerAI.com on October 19 describes how Reverse Engineer Anything (REA) lets AI coding agents inspect existing software through local analysis tools. Its examples show how developers can use an agent to investigate specific behaviors, but findings need verification, and the source does not establish that the approach works reliably across all programs.
A report published by ThorstenMeyerAI.com on October 19 describes how developers can use Reverse Engineer Anything (REA) to connect AI coding agents to tools that inspect existing software. The approach is intended to help developers identify how a program handles particular data and business rules before building or changing another application; a reported QuickBooks project offers one example, but does not establish that the method works for every program.
REA is an MIT-licensed open-source project 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 targets such as native applications, JavaScript and Electron apps, .NET and Android software, firmware, databases and browser-based sites. The source identifies version 6.7.0 as released October 18, 2026.
The report recommends asking an agent to investigate one defined behavior, rather than asking it to explain an entire program. A useful question specifies what decision the investigation will inform and asks for the data involved, the calculation and the conditions that change the result. The report also advises asking the agent to label conclusions as observed, inferred or unknown and to demonstrate its explanation with a concrete example.
In the cited Ledger Reborn project, the developer used REA and Ghidra to study QuickBooks Desktop for Mac 2024, focusing on inventory arithmetic, budget sign conventions, reconciliation balances and cash-basis reports. The report says the developer’s local accounting app matched profit-and-loss, balance-sheet and trial-balance figures for Intuit’s official sample company account by account, to the cent. That result is an account from the project, not an independently established measure of REA’s performance across other software.
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 Business Rules
For developers maintaining older or poorly documented software, a working screen may not explain the rules behind the result. Understanding how records are stored, how figures are calculated and what changes an outcome can help teams assess whether a replacement or integration will behave as intended. REA’s proposed role is to give an AI agent access to inspection tools and evidence, rather than relying only on descriptions in a prompt.
The QuickBooks example matters because accounting outputs depend on rules that can be easy to miss in a visual imitation. The report says the recreated app’s statements matched a sample company’s figures, but a single reported case cannot show that every edge case, version or business workflow was captured. Developers still need to check important findings against actual outputs and make clear which conclusions are observed and which are inferred.
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How REA Connects Agents to Analysis Tools
According to the report, REA combines an MCP server that coding agents can call, a command-line utility for setup and analysis, and an agent skill that lays out investigation steps. Some target types require separate tools: native-program analysis may use Ghidra, Hopper or IDA, while website investigation can use a browser the user controls. The report says REA requires Node.js 22.19 or newer and that native analysis can involve additional dependencies.
The source describes a guided setup process and a diagnostic command, while warning that diagnostics check files and prerequisites rather than proving a live agent connection. It recommends checking inside the agent that REA tools are actually available before interpreting an answer. This distinction matters: an agent that never calls an analysis tool may be responding from its model’s prior knowledge rather than evidence about the target program.
REA’s analysis runs locally, the source says, but results are passed to the agent and may also reach the model provider under that provider’s data policy. The project warns, in the report’s wording, “This is not a sandbox.” Inspection tools run with the user’s permissions, so analyzing an untrusted program carries risks similar to opening other untrusted files.
“This is not a sandbox.”
— The REA project, as quoted in the ThorstenMeyerAI.com report
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Limits of the Reported Evidence
The source describes one accounting-app case study and gives no independent evaluation of REA’s accuracy across software types. It does not establish how often an agent’s analysis is correct, how results hold up across program versions, or how much human review is needed for different investigations. The reported match to QuickBooks sample-company statements is attributed to the project account; the material provided does not include an independent audit of the recreation.
The report also does not quantify the privacy exposure of sending analysis results to model providers or set out a full security assessment. Developers should check the relevant provider’s data policy and treat unknown software cautiously. Whether REA’s popularity or the described case will lead to broader adoption is also unclear.
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Verify the Setup and Findings
The report’s suggested next step is to confirm that the chosen agent can call REA tools before opening a target, then begin with a harmless application the developer owns. For each investigation, developers can test the explanation against one concrete input and output and compare consequential results with the original program. The source gives no announced product milestone beyond the release of REA 6.7.0 on October 18, 2026, so broader validation and future development remain to be seen.
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Key Questions
What is REA?
Reverse Engineer Anything is an open-source project that connects AI coding agents to software analysis tools through an MCP server, command-line utility and agent skill, according to the report.
What did the QuickBooks example show?
The report says Ledger Reborn’s developer used REA to investigate selected QuickBooks Desktop for Mac 2024 behaviors and reported matching several accounting statements for Intuit’s sample company to the cent. This is a reported case study, not proof of general accuracy.
Can REA inspect any application?
The source lists support for several types of software and data, but it does not establish that every application can be analyzed successfully. The tools and dependencies needed vary by target.
Does local analysis mean the information stays private?
No. The report says analysis runs locally, but results are sent to the agent and may reach its model provider under that provider’s data policy. REA is also not a sandbox, according to the project warning quoted in the report.
Source: ThorstenMeyerAI.com
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