Analyze Any Codebase in a Secure Cloud Sandbox

The problem with understanding unfamiliar code

You inherited a codebase. Or you’re evaluating an acquisition target. Or a contractor left and nobody knows how the backend actually works. Whatever the reason, you need to understand a large body of code you didn’t write — and you need to understand it fast.

The traditional approach: clone the repo to your local machine, open it in your IDE, and start reading. Grep for patterns. Trace function calls manually. Open dozens of files across multiple tabs. Build a mental model one file at a time. It takes hours. Sometimes days. And if the codebase contains malicious dependencies or hostile build scripts, you’ve just executed them on your own machine.

There is a better path. An AI agent that clones the repo into an isolated cloud sandbox, reads every file, maps the architecture, and answers your specific questions — with references to actual source code. That is what LikeClaw does for codebase analysis.

What real users are doing with codebase analysis

A team lead needed to understand how their iPad app handled API integrations, chat functionality, and tab filtering. They pointed the agent at their private repo and set a 5-minute timeout for deep analysis. The agent cloned via SSH, mapped the entire project structure, identified the tech stack, and explained how each subsystem worked — referencing specific files and functions. What would have taken an afternoon of manual code reading was finished before the coffee got cold.

A developer asked the agent to trace how labels and categories flow from a NestJS backend to the mobile frontend. The agent cloned the backend repo, found the relevant controller, read the ITag interface, and produced an API contract document showing exactly which routes serve which data shapes — all without the developer touching the codebase manually. Controllers, routes, interfaces, return types. Documented in minutes.

Another user asked bluntly: “Are your recommendations based on the actual repo, or did you make them up?” The agent responded with specific file paths, function signatures, and line numbers. Every recommendation was traceable to source code. No hallucinated file names. No invented function calls. The analysis was verifiable because the agent had actually read the code inside the sandbox.

Why the sandbox matters for codebase analysis

When you clone and run code locally, you trust that code with your entire machine. Your SSH keys. Your environment variables. Your file system. For your own projects, that risk is manageable. For unfamiliar codebases — open source dependencies, contractor deliverables, acquisition targets — it is not.

Snyk’s research found 341+ malicious skills in the OpenClaw marketplace, with 36% containing prompt injection attacks. That is the ecosystem people are granting repo access to. When an AI agent has permission to clone your private repository, the execution environment matters. If that agent runs on your local machine with raw system access, a compromised dependency in the analyzed repo could exfiltrate your credentials, SSH keys, or source code from other projects.

LikeClaw runs every analysis in an E2B sandboxed container. The repo is cloned inside the sandbox. The analysis runs inside the sandbox. When the task completes, the sandbox is destroyed. Your machine is never exposed. Your credentials are encrypted and scoped to the sandbox session. Zero lateral movement. Zero persistence.

What the agent actually produces

The output depends on what you ask for. Common analysis patterns from beta users include:

• Architecture maps: Tech stack identification, framework detection, directory structure breakdown, entry points, and dependency graphs
• API documentation: Route definitions, request/response shapes, authentication patterns, and middleware chains — extracted directly from controller and route files
• Code quality flags: TODO/FIXME markers, dead code, unused imports, circular dependencies, and configuration anti-patterns
• Security surface: Hardcoded credentials, exposed endpoints, missing input validation, and dependency vulnerabilities
• Onboarding docs: How-to-run guides, environment variable documentation, and key file explanations for new team members

You can ask follow-up questions in the same session. The agent maintains context from the cloned repo in your persistent workspace. Ask about one module, then drill into another. The code stays available until you end the session or the workspace resets.

How this compares to reading code manually

Manual code review is thorough but slow. You are limited by your own reading speed and working memory. A senior developer reviewing an unfamiliar 50,000-line codebase might need 2-3 full days to build a confident mental model. And that model lives only in their head — it is not documented, not searchable, not shareable.

The agent does not replace deep human understanding. But it compresses the first pass from days to minutes. You get a structured starting point: here is the tech stack, here are the key files, here is how data flows from endpoint to database. From there, you can focus your human attention on the parts that matter — architecture decisions, edge cases, business logic — instead of spending hours just figuring out which files to read first.

This pairs well with LikeClaw’s sandboxed code execution capabilities. Once you understand the codebase, you can run tests, execute scripts, and prototype changes — all in the same secure cloud environment. Analyze first, execute second, without ever exposing your local machine.

Who this is for

Engineering leads evaluating contractor deliverables. CTOs doing technical due diligence on acquisitions. Developers onboarding onto legacy projects. Security teams auditing third-party code. Anyone who needs to understand a codebase they did not write, without spending days reading it manually and without running unknown code on their own hardware.

Zero setup. Predictable pricing. Sandboxed from start to finish. Your code goes in, your analysis comes out, and the sandbox disappears.