🔍 Read the full analysis: What Should Your AI Agent Inspect First In An Unknown Codebase? on ThorstenMeyerAI.com
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TL;DR
Thorsten Meyer published a guide on Oct. 19, 2026, explaining how developers can use the open-source Reverse Engineer Anything (REA) toolkit with AI coding agents to investigate unfamiliar software. The guide recommends starting with one testable behavior, checking that REA is connected, and treating agent conclusions as evidence to verify—not as confirmed facts.
Thorsten Meyer published a guide on October 19, 2026, outlining how developers can use the open-source Reverse Engineer Anything (REA) toolkit with AI coding agents to inspect unfamiliar programs. Its central advice is to start with one specific behavior and a decision it informs, then check the agent’s evidence before relying on its explanation—a method that can help developers understand existing software before building or changing systems that depend on it.
REA is described as a local MCP server, command-line tool and agent skill that lets coding agents call software-analysis tools. The source says it supports several kinds of targets, including native programs, JavaScript and Electron applications, .NET and Android apps, firmware, saved network traffic, WebAssembly and SQLite databases. For websites, it can observe activity in a browser controlled by the user. The article reports that REA version 6.7.0 was released October 18.
The guide’s first recommendation is to avoid open-ended instructions such as “figure out how this program works.” Instead, it advises asking about a bounded operation—for example, how a partial invoice payment changes a balance and which account receives the posting—and stating what design decision the answer will support. The agent should explain stored data, calculations and conditions that alter the result, while marking conclusions as observed, inferred or unknown. A useful check is whether it can work through a concrete example with actual inputs and outputs.
Before an investigation, developers should install REA, restart their agent and verify that the tools are available. The source gives setup instructions for multiple clients and says Node.js 22.19 or newer is required. It also cautions that REA’s diagnostic command checks installation files and prerequisites, not whether the agent’s live connection works. The guide recommends confirming that connection from within the agent and testing with a harmless application the user owns.
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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Why a Small Test Beats a Screen
An interface can show what a program looks like without revealing the rules behind its results. For teams maintaining legacy systems or creating compatible replacements, those rules may determine how records are stored, how values are calculated and what happens when a user changes something. An agent that describes screens but cannot trace one example may offer a plausible account without uncovering the mechanism.
The article presents REA as a way to gather evidence from software and turn a narrow investigation into a written specification. That could help developers identify what they need to reproduce or test before committing to an implementation. But the guide does not establish that using REA will reliably recover a program’s full behavior; the quality of the result still depends on the investigation, available analysis tools and human verification.
There is also a data and security trade-off. REA runs analysis locally, according to the source, but the agent receives the results, and the model provider may also receive them under its own data policy. The project’s warning that REA is not a sandbox matters: opening an untrusted program passes it to analysis tools running with the user’s permissions.
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Ledger Reborn as a Case Study
The guide points to Ledger Reborn, a local accounting application its author says was developed after investigating QuickBooks Desktop. The source reports that the developer used REA to examine the official QuickBooks Desktop for Mac 2024 trial, focusing on inventory arithmetic, how account types affect budget signs, reconciliation balances and cash-basis report logic. It says the replacement’s profit-and-loss statement, balance sheet and trial balance matched the original’s figures account by account, to the cent, for Intuit’s official sample company.
That result is an account by the source, not an independent evaluation included in the material. The case study illustrates the guide’s broader point: investigating a limited set of business rules may be more actionable than attempting to reconstruct an entire application at once. The source also says REA is an MIT-licensed project hosted on GitHub and reports roughly 325,000 stars and 82,000 forks as of October 19. Those are dated repository counts, not evidence by themselves that the tool’s findings are accurate.
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What the Investigation Cannot Prove
The source is a practical guide and case study, not an independent comparison of REA’s accuracy across software types. It does not provide a systematic success rate, error rate or benchmark showing how often agents correctly identify program behavior. The reported QuickBooks matching result is attributed to the Ledger Reborn project account; the supplied material does not describe independent replication or testing beyond the sample company and statements listed.
It is also not clear from the source how results vary across agents, model providers, target applications or analysis engines, or how much manual effort the investigations required. Local analysis does not mean all information stays on the user’s machine: the source says findings go to the agent and may be handled by its provider under that provider’s data policy. Developers should check those policies and assess the risks of the target and tools before proceeding.
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Verify Before Building on Findings
The guide’s next step for a developer is to choose a behavior, connect REA, and verify the connection before opening a target. The agent should then produce a traceable explanation of one example, label what it observed versus inferred, and identify anything it could not establish. Developers can compare that account with known inputs and outputs before using it to shape a specification or implementation.
For REA itself, the source identifies version 6.7.0 as current on October 19, 2026, but does not give a later release schedule or report an upcoming milestone. The practical follow-up is therefore evidence-based: test the workflow on software the user owns, review the findings, and keep unresolved behavior marked as unknown rather than treating an agent’s explanation as verified.
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Key Questions
What should an AI agent inspect first in an unknown codebase?
Start with one behavior tied to a concrete decision, such as how a payment changes an invoice balance. Ask the agent to explain the stored data, calculation and conditions that change the result, and to work through an example.
What is REA?
Reverse Engineer Anything is described in the source as an open-source toolkit with a local MCP server, command-line tool and agent skill. It connects coding agents to software-analysis tools for inspecting supported applications and other targets.
How can I tell whether REA is connected to my agent?
Run the setup and diagnostic checks, then confirm the live connection from inside the agent by asking it to call an REA tool. The guide says the diagnostic checks files and prerequisites, not the live agent connection.
Does local analysis mean the results stay private?
No. The source says REA analyzes targets locally, but the agent receives the results and the model provider may receive them under its own data policy. Review the provider’s terms before sharing sensitive findings.
Is REA a safe way to open an unknown program?
The source explicitly warns that REA is not a sandbox. Analysis tools run with the user’s permissions, so unknown programs should be treated as untrusted files; the guide recommends starting with a harmless application you own.
Source: ThorstenMeyerAI.com
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