Most AI coding tools grep around the repo. Mulu maps the codebase first, ranks the files that matter, and knows what could break before it edits or verifies anything.
Without a codebase map, the verification pass is blind too. The model searches random files, misses connections, and ends up testing the wrong thing or missing the fallout entirely.
Without understanding your project structure, your AI has to search through dozens of files to answer a question about three. It often picks the wrong one.
Change one function and something breaks elsewhere because your AI had no idea those two things were linked. You find out after the fact.
Reviewing, correcting, and integrating AI suggestions takes more time than expected, especially as your codebase gets larger and more complex.
Mulu reads your entire project when you open it. Every file, every connection, every relationship. All mapped and ready before you ask your first question or run a verification pass.
So instead of dumping a huge context window at the AI and hoping it figures it out, Mulu sends the small, precise slice that actually matters. The build flow and the verification flow both start from knowledge, not guessing.
Most tools make a change and leave you to find the fallout. Mulu shows you the full picture first: every connected file, every place that calls the code you're editing, every test and path that should be part of verification.
You see the scope before a single line changes, and the agent knows what needs to be rechecked after the edit lands.
Other tools re-index on a schedule, which means the change you just made might not be reflected when you ask your next question. Stale context produces stale answers.
Mulu updates only the files that actually changed. The moment you save, the map is current. No refresh button. No waiting.
Mulu reads everything automatically: every file, every connection. You don't configure anything. It just works.
Your AI already has the relevant context loaded. No file-picking. No "please read this first." It knows what matters.
Before anything changes, review what else is connected. See every file that calls your code and every test that covers it.
No more broken surprises. No more "almost right." Your AI is working from the complete truth of your project, not a partial guess.
Not at all. Mulu does all of this in a completely separate background process, isolated from your editing experience. When you save a file, only that file is updated, not the whole project. It's fast enough that you'll never notice it running.
Most AI coding tools search for relevant files when you ask a question, like typing into a search engine and hoping the right result comes back. Mulu knows the structure of your project up front, so it doesn't need to search. It already understands how your files connect and what depends on what.
Yes. Mulu is built specifically for projects that have grown past the point where manually choosing context is practical. The larger your project, the more valuable automatic context becomes, and Mulu gets smarter about what to surface as your project grows.
Every token you send to an AI model costs money. Tools without good context management often over-send, dumping whole directories just to make sure the AI has what it needs. Mulu sends only what's actually relevant, so your credits go further and your answers come back better.
Mulu supports all major languages: JavaScript, TypeScript, Python, Go, Rust, Ruby, and more. It understands imports, exports, function calls, and test files regardless of the framework you're using.
Automatic context. Impact preview. Always current. Build without the guesswork.