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Reverse Engineer Anything (REA) is an open-source MCP server and command-line tool that connects AI coding agents to reverse-engineering software. It lets an agent inspect an application or binary, collect evidence from analysis tools, explain how a feature works, and help implement an adapted version for a different stack or interface.
REA is not an automatic source-code recovery or cloning system. Its documented workflow is to decompile, understand, and recreate, with the agent showing the observations and limitations behind its conclusions.
What Reverse Engineer Anything does
REA provides an MCP layer between an AI agent and reverse-engineering providers. Instead of asking a model to guess from a binary, the agent calls analysis tools, receives structured findings, and uses those findings to answer follow-up questions or produce implementation work.
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The project describes its scope as “Reverse engineer anything with agents, from app behavior down to native binaries.” In practice, the useful output is an evidence-backed explanation: recovered pseudocode, strings, symbols, call relationships, decoded resources, packet summaries, or runtime observations, depending on the target and provider.
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How the three-stage investigation works
1. Decompile
REA opens the selected app, file, project, or capture and asks the underlying provider to recover readable representations. These can include code-like pseudocode, assembly, strings, names, symbols, imports, and references. Decompilation exposes clues; it does not recreate the developer’s original source files or comments.
2. Understand
The agent follows control flow and data flow, checks callers and cross-references, and compares multiple pieces of evidence before explaining a behavior. A documented feature-investigation sequence starts with open_binary and binary_overview, then uses search_strings, search_procedures, and list_names. Cross-reference and caller tools help locate relationships, while call-graph and procedure tools trace execution. The agent can then request procedure_pseudo_code, procedure_assembly, or batch_decompile for deeper inspection.
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3. Recreate
Once the behavior is understood, the agent can help implement an equivalent or adapted feature in the reader’s chosen language, framework, interface, or architecture. “Adapted” matters: REA does not promise a byte-for-byte clone, recovered original source, or a complete replica of a commercial application.
Installing REA and connecting an agent
Quick-start command
- Install a supported Node.js release: 22.x with at least 22.19, 24.x with at least 24.11, or 26 and newer. npm is also required.
- Run
npx rea-agents setup. The setup process registers REA with the selected coding agents, installs matching workflow instructions, backs up existing configuration, and presents proposed changes for review before approval. - If you prefer a global command, install the CLI with
npm install --global rea-agents, then use the installed command according to the project’s current instructions. - Connect an MCP-capable client. The project lists Claude Code, Codex, Cursor, Gemini CLI, and other compatible clients as supported integrations; availability can vary by client and operating system.
Provider requirements
Static JavaScript and .NET inspection can work without a native reverse-engineering engine. Deeper native analysis depends on a provider such as Hopper, Ghidra, or IDA Pro/Hex-Rays. JEB is relevant for workflows involving JEB projects and Android analysis. These are separate products: REA does not grant their licenses, activation rights, trial permissions, or redistribution rights.
On Linux, some workflows require a procps-compatible ps command. Provider support and operating-system restrictions differ, so the current project documentation and the provider’s own requirements should be checked before installation.
Targets REA can analyze
The project’s target list extends well beyond conventional native executables. Returned artifacts depend on the target and the provider used.
| Target | Examples of returned evidence |
|---|---|
| Native binaries and offline ELF layouts | Pseudocode, assembly, strings, symbols, calls, references, and module or import relationships |
| Go and Rust binaries | Recovered procedures, names where available, control-flow relationships, and decompiled views |
| WebAssembly and EVM bytecode | Procedure-level code views, instructions, names, and call relationships |
| SQLite databases | Database structures and extracted records or metadata available to the connected tools |
| Recorded Linux crashes | Crash and process observations useful for tracing failure behavior |
| JavaScript and Electron apps | Static code and application structure |
| Websites and saved network captures | Application resources, captured requests, and protocol or packet summaries |
| .NET assemblies | Managed-code inspection and recovered program structures |
| Android APKs and devices | Application code, resources, device observations, and runtime behavior |
| JEB projects and Android resources | Project-level analysis and decoded resource information |
| Flutter apps | Application artifacts and provider-specific code or resource views |
| Firmware | Binary structure, strings, procedures, and other provider-supported findings |
| Packet captures and logic signals | Packet summaries, protocol observations, and signal-level findings |
| Apple packages | Package contents, resources, and supported code-level observations |
| Process behavior | Runtime observations from the supported host |
REA versus a conventional analyst-led workflow
| Question | Conventional workflow | REA workflow |
|---|---|---|
| Who drives the investigation? | An analyst manually switches among reverse-engineering tools and records findings. | An AI agent calls MCP tools, keeps the investigation context, and answers follow-up questions from returned evidence. |
| How is evidence exposed? | The analyst gathers pseudocode, strings, graphs, and runtime observations separately. | Tool results are returned to the agent for explanation, cross-checking, and implementation planning. |
| Where does analysis run? | On the analyst’s configured machine and installed providers. | On the supported local host; the model receives the resulting tool output. |
| What is the deliverable? | Usually notes, diagrams, or manually written replacement code. | An evidence-based explanation plus assistance creating an adapted implementation. |
| What remains necessary? | Tool expertise, licensing, and human interpretation. | The same provider and licensing requirements, with the agent helping orchestrate and explain the work. |
Does REA upload an app or binary?
REA’s analysis is performed locally on the supported host, which means the connected reverse-engineering tools operate against local files or local runtime targets. That statement does not define what happens to every result afterward. The agent receives tool results, and the model provider has its own data-handling policy. Review that provider’s terms before analyzing confidential software, customer data, credentials, proprietary algorithms, or regulated information.
Legal and practical limits
REA is positioned for lawful research, education, interoperability, software preservation, and analysis of software that you own or are authorized to examine. Authorization for the file is not the only consideration: commercial-tool trials, demonstrations, automation, and sharing generated output may also be restricted by vendor terms.
- Obtain and maintain the required licenses for native-analysis providers independently of REA.
- Confirm that automated inspection and any redistribution of findings are permitted for the target software.
- Keep sensitive inputs and model-provider policies in scope even when local analysis is enabled.
- Treat decompiler output as an interpretation that requires validation, especially around optimized code, obfuscation, packed binaries, stripped symbols, and runtime-only behavior.
When REA is the right fit
REA is most useful when an engineer needs to move from “what does this feature appear to do?” to a defensible explanation and a practical reimplementation plan. It can reduce the friction of searching large binaries, following callers, comparing procedures, and turning scattered tool output into a coherent narrative.
It is less suitable as a promise of instant cloning or as a substitute for provider expertise. Native targets still depend on the quality and availability of Hopper, Ghidra, IDA Pro/Hex-Rays, or another appropriate engine; unusual formats may expose only the artifacts that the selected provider supports. The strongest result comes from treating the agent as an investigator and implementation partner while a human verifies evidence, permissions, and behavioral equivalence.
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