Causari runs on your own machine — the client starts it, so there is no endpoint to ping. Last commit 21 Sep 2026.
Records what an AI agent did in a repo (prompt, model, files) and answers why a line exists.
What the code does
We read the source, 8 h ago · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
const child = spawn(bin, args, { stdio: 'inherit', windowsHide: true });
return path.join(cacheRoot(env, platform), 'causari', ver, tgt);
return subprocess.call([str(binary), *args])
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
This code can reach further than it looks
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
Alternatives to Causari
same job, measured the same wayAsk a repository what depends on what. Every answer cites the file, line and commit it came from.
Checks the context files a coding agent reads against the git index, and reports what drifted.
Persistent memory for AI coding agents — past bugs, decisions, and your corrections, in your repo.
Pack local or remote codebases into AI-friendly files that LLMs and coding agents can read or search
Presentations, slides, reports and one-pagers your agent designs and you edit. Exports to one file.
Persistent project memory for AI coding agents: one compact digest instead of re-reading the repo.
Persistent code memory for AI agents: notes pinned to files and symbols that survive refactors.
Linters check your code. Invigil checks whether your repo is legible — to newcomers and AI agents.
Causari — questions
Answers built from our own checks of this server.