Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud
Mitos
Overview
Description from the maker (original English)
Architectural decision memory for LLM-native workflows — markdown for humans, a graph for agents.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 88/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"mitos": {
"command": "uvx",
"args": [
"mitos-adr"
]
}
}
}Run this in your terminal
claude mcp add mitos -- uvx mitos-adrOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"mitos": {
"command": "uvx",
"args": [
"mitos-adr"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"mitos": {
"type": "stdio",
"command": "uvx",
"args": [
"mitos-adr"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account dovahkiin-v, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 5696 servers in this category
- Published to the registry more recently than 68% of the other 5696 servers in this category
- Basic checks equal to or better than 100% of the other 5696 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Mitos safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.20.0) reached the registry 20 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud✓ exa.ai
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Mitos
🔧 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 mitosfor 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
mitosCLI via pipx (from PyPI or this repository); - start a local Qdrant container (
qdrant/qdranton port7333, 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](SETUP.md):
- Install (once per machine):
pipx install mitos-adr - Start Qdrant (once per machine, shared by all projects):
docker compose up -dfrom this repo — mitos runs its own instance on:7333, so it never touches a Qdrant you use for other work. - Register the MCP server (once per machine, recommended for agents):
claude mcp add --scope user mitos -- mitos serve. One registration serves every project — see SETUP.md for what it costs, for other harnesses, and for why a leftover per-project.mcp.jsonentry has to go. - Per project:
mitos initfrom the project root, thenmitos set-key --global(one key covers everything; get it at https://aistudio.google.com/app/apikey). Gemini is the tested embedding provider today; a multi-provider abstraction is on the roadmap. - Verify:
mitos status .→READY ✓.
mitos status . is the compass throughout: it says exactly what's done, what's missing, and what to do next for that project. With no project named, mitos status answers the other question — what does this machine have — listing every registered project and checking Qdrant.
Every command names its project. There is no default target: mitos init registers the project by name, and from then on each verb takes -p , -p , or -p . from the project root (agents pass the same thing as a project argument). mitos projects lists what's registered, and mitos unregister removes a registration (the workspace stays as it is). That is what lets one install and one MCP server serve every project on the machine without a call ever landing in the wrong corpus.
How it runs
Mitos is per-project — each project gets its own decision graph and its own Qdrant collections. Day to day, three verbs carry the loop (as MCP tools for agents, with CLI twins):
| Verb | When |
|---|---|
surface_decisions (mitos surface) | *Before* deciding — is there precedent? Every hit carries the alternatives that were already rejected and why. |
record_decision (mitos record) | The moment something is settled — the decision, the rejected paths, and how it relates to prior decisions (supersedes, amends, …). |
query_decisions (mitos query) | Looking something up by meaning (the MCP tool also by exact handle), or searching only the rejected options. |
A few properties worth knowing:
- The markdown is the source of truth. Every decision lands in
decisions.md, and rotation moves committed entries intodecisions/archive/; both are human-readable and greppable, and the graph and the search index are derived from them and can always be rebuilt from them (mitos rebuild). - A decision's axiom is never edited — it's superseded. The axiom and its mechanisms change only through a later decision that supersedes, corrects or amends it, and state (active / superseded / amended) is computed from those typed relations, so the history of *why* always survives. Commentary (rejected paths, context, scope) can be fixed in place with
mitos amend-commentary. - It fails safe. If the search index or the embedding API is down, recording still works and search degrades to an honest text-match over the markdown — nothing blocks, nothing is lost, and degraded output says it's degraded.
- It audits itself. The corpus sweep (
mitos check -p .) finds decisions that silently contradict each other. The commit gate (mitos hook-install -p .) runs ongit commitand holds a commit made after decisions were recorded until a check has been attempted, and--stagedchecks hand-authored entries still pending indecisions.md— see SETUP.md for the commit gate, the staged gate, and the CI and cron recipes.
Explore the rest with mitos --help — the help text doubles as the API reference.
Why it exists
Building software through intensive LLM design reviews produces architectural decisions faster than a person can track. One month of that working style produced close to 900 decision records in a single markdown file — no longer greppable, readable, or manageable by hand. Existing ADR tools are built for human teams logging the occasional decision; mitos is built for a solo developer whose AI assistants generate and consume decisions continuously.
If that's your way of working, project size doesn't matter much — the higher the decision volume, the faster mitos moves from comfort to necessity.
Development
pip install -e '.[test]'MITOS_NO_LIVE_TESTS=1 pytest -m "not packaging" -n auto # offline suite, parallel (~50s) pytest -m "not packaging" # adds the live tier — serial only pytest -m packaging # real-install check: fresh venv + pip install
-n auto is safe for the offline suite and not for the live tier: the test-collection sweep is session-scoped, so parallel workers delete each other's Qdrant collections and the affected tests degrade to skips rather than failures.
The *_live.py suites and golden Layer B make real Gemini and Anthropic API calls against your own keys, and need Qdrant on :7333. They skip when no key is resolvable, so a fresh clone runs the fast path by default.
Keys resolve from the environment, a repo-root .env, or ~/.config/mitos/.env — so if you already *use* mitos, a test run can pick up your personal key and spend against it. Opt out explicitly:
MITOS_NO_LIVE_TESTS=1 PYTHONPATH=. pytest -m "not packaging"The canonical decision format lives in mitos/format-spec.md. License: Apache 2.0.