Mitos MCP server

Mitos

<!-- mcp-name: io.github.dovahkiin-v/mitos --> <!-- ^ MCP Registry ownership proof. The registry reads this out of the PyPI longdescription, so it must survive into the published package and must byte-match name in server.json. Enforced server-side: a publish without it is rejected 400. tests/testpackaging.py locks the pair. -->

🔧 Early release — actively developed

When you build software with AI assistants over months, the reasoning behind your decisions gets lost. The assistant forgets why you chose one approach, re-suggests options you already rejected, and your design notes drift out of sync with what was actually decided. Mitos is a memory layer for those decisions: it records each decision, the alternatives you ruled out, and how later decisions replace earlier ones — then feeds that history back to your AI assistant in a compact, trustworthy form.

The result: your AI collaborator stays consistent with the calls you've actually made — it stops contradicting a past decision or re-opening a settled question, and your decision record never silently rots.

Under the hood: markdown for humans (decisions.md and its quarterly archives under decisions/archive/ are the source of truth you can always read and grep), a typed graph for the agents (SQLite + a local Qdrant for semantic recall), and an MCP server so agents check precedent before deciding and record decisions as they make them.

Available on PyPI and the MCP Registry.

Fastest install: hand it to your agent

If you work with an AI coding agent (Claude Code, Cursor, Gemini CLI, …), the easiest path is to let it do the setup. In the project you want mitos in, give your agent:

Read https://github.com/dovahkiin-v/mitos/blob/main/SETUP.md and set up mitos
for this project. When done, run `mitos status .` from the project directory
and report the result.

What your agent will end up doing — the same steps a human follows, all in SETUP.md where you can read them first:

• install the mitos CLI via pipx (from PyPI or this repository);
• start a local Qdrant container (qdrant/qdrant on port 7333, isolated from any Qdrant you already run);
• register the MCP server once for the whole machine, if it isn't registered already;
• initialize the project workspace, which also registers the project by name;
• ask you to set your API keys yourself (mitos set-key) — a Gemini key (required), and an Anthropic key for the conflict-audit layer (strongly recommended); the setup guide tells agents not to handle key values.

How much your agent asks along the way is governed by your own agent's settings, not by this prompt.

Manual setup

The same steps by hand — full detail in SETUP.md:

Shortened. The full README is on GitHub.