REA
NexusAi Summary: REA inspects executables, browser scripts, and Electron apps so your coding agent can explain, reproduce, and rebuild software behaviour from binaries to runtime traces, including handlers, branches, and call relationships.

NexusAi Overview
Workflows start with a targeted inspection: point REA at an executable, an Electron app or a browser endpoint and capture the code paths and runtime values that matter. The agent receives labelled snippets and traces, then answers in human terms and produces reconstruction code or tests.
Capabilities and outputs
The toolkit suits engineers, security researchers, product teams and AI-first developers who need to understand legacy behaviour or closed binaries quickly. Typical scenarios include rebuilding a calculator rule, extracting a game’s acceleration formula, tracing an unexpected UI handler, or verifying a patch. REA reduces time spent decoding assembly and chasing call graphs by delivering labelled findings that integrate into an agent-driven development loop.
- Inspect native binaries and return selected assembly and called helpers.
- Map JavaScript and Electron modules to reveal runtime flow and IPC.
- Trace browser activity and capture live script updates and configuration.
- Produce readable handler summaries and short reconstruction-ready code snippets.
- Validate reconstructions with simple checks that reproduce observed results.

Practical reverse engineering for agent-driven development
Who it’s for
Install the agent connector and follow the guided setup; the recommended starter command is npx rea-agents@latest setup. The process returns a setup plan for approval and then verifies the installation. For browser inspection, open a local debugging endpoint and give the agent the target page URL. Documentation and example labs show end-to-end cases such as Calculator and the dinosaur game to illustrate inspection, rule recovery and reconstruction.
NexusAi: REA turns low-level traces into actionable code and concise explanations your agent can use immediately.
Getting started
REA’s strength is pragmatic output: concise, agent-friendly findings that reduce friction between low-level traces and working prototypes. It avoids exhaustive dumps in favour of labelled handlers, operand mapping and simple checks, letting teams reproduce behaviour and iterate rapidly without deep manual decoding.
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