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Locus

by EDKarlssonversion 0.6.2i★ –iRuns on your computer
Registry nameio.github.EDKarlsson/locus

Install

Overview

Description from the maker (original English)

Hierarchical markdown memory palace for AI agents — structured palace navigation via MCP tools.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

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": {
    "locus": {
      "command": "uvx",
      "args": [
        "locus-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add locus -- uvx locus-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "locus": {
      "command": "uvx",
      "args": [
        "locus-mcp"
      ]
    }
  }
}

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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "locus": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "locus-mcp"
      ]
    }
  }
}

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account EDKarlsson, 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 7251 servers in this category
  • Published to the registry more recently than 4% of the other 7251 servers in this category
  • Basic checks equal to or better than 100% of the other 7251 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 Locus 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 (0.6.2) reached the registry 8 months 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:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

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★ 64,224MITUpdated today
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

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.

Locus

Hierarchical markdown-based memory system for autonomous AI agents. Each directory is a room (locus) in the palace, containing specific knowledge navigated on demand. Named for the atomic unit of the Method of Loci.

Core idea: Keep context windows small. Load only the room you need, not the whole palace.


How it works

palace/

INDEX.md ← always read first (~50 lines max) global/ toolchain/ toolchain.md ← canonical facts about tools projects/ my-project/ my-project.md ← room overview + key files technical-gotchas.md ← specialty: issues & resolutions sessions/ 2026-03-02.md ← append-only session log

An agent reads INDEX.md, navigates to the relevant room, and reads only that room. Session logs accumulate until consolidation merges them into canonical files.

See the wiki for full documentation.


Quick start

# Install

pip install locus-mcp

or: uvx locus-mcp --palace ~/.locus (no install needed)

Create a palace from the packaged example

locus init ~/.locus

Edit ~/.locus/INDEX.md to describe your palace

Run the MCP server

locus-mcp --palace ~/.locus

or: LOCUS_PALACE=~/.locus locus-mcp


Installation

MCP server (recommended for MCP-capable clients)
pip install locus-mcp

Or run without installing using uvx:

uvx locus-mcp --palace ~/.locus
Agent skills

The skill files are the one part of Locus written for a specific runtime. The palace convention, the MCP server, and the recall / lint / index CLIs are runtime-neutral and work from anything that can read a file or speak MCP. The skills in skills/claude/ are written and maintained for Claude Code, and that is the only set shipped here. They are plain markdown with YAML frontmatter, so another runtime is welcome to adapt them. Per-runtime copies used to live in skills/codex/ and skills/gemini/; they were removed because keeping three variants honest cost more than it returned.

Install them from a clone:

git clone https://github.com/Nano-Nimbus/locus.git

cd locus make install-skills # copies skills/claude/* to ~/.claude/skills/ make install-skills-dry # print what would be copied, write nothing

CLAUDE_SKILLS_DIR overrides the destination.

SkillCommandDescription
locus/locusRecall, navigate the palace, write rooms and session logs, regenerate indexes
locus-consolidate/locus-consolidateMerge session logs into canonical files
locus-audit/locus-auditAudit palace health
locus-feedback/locus-feedbackRecord explicit feedback on a palace recall
locus-release/locus-releasePost-release verification workflow (contributors)
locus-security/locus-securityTrust tags, nonce discipline, and the locus-security CLI
locus-palace-init/locus-palace-initBootstrap a palace from existing memory files
Agent SDK (Python)
pip install locus-mcp

locus --palace ~/.locus --task "What toolchain conventions are set?"


MCP Server

The locus-mcp command exposes five tools over the Model Context Protocol.

Use stdio for all local integrations (Claude Desktop, Claude Code, Codex, Gemini — default, no extra flags needed). SSE transport is available for network deployments (--transport sse) and requires FASTMCP_HOST=0.0.0.0 to be set explicitly — the server binds to loopback by default.

ToolDescription
memory_listReturns INDEX.md (no args) or lists a room's files
memory_readReads any file in the palace
memory_writeAtomically writes a file (guarded — cannot write to _metrics/, sessions/, .sig/, .security/)
memory_searchRanked full-text search over the shared FTS5 index (see Recall); ripgrep only without FTS5
memory_batchReads up to 20 palace files in a single call — use for multi-room loads

Add --security to enable Ed25519 signature verification on reads and automatic signing on writes. See Security below.

Claude Desktop (claude_desktop_config.json)
{

"mcpServers": { "locus": { "command": "locus-mcp", "args": ["--palace", "/path/to/palace"] } } }

Or using uvx (no install required):

{

"mcpServers": { "locus": { "command": "uvx", "args": ["locus-mcp", "--palace", "/path/to/palace"] } } }

Cursor / Zed
{

"mcp": { "servers": { "locus": { "command": "locus-mcp", "args": ["--palace", "/path/to/palace"] } } } }

Environment variable

All clients support LOCUS_PALACE as an alternative to --palace:

export LOCUS_PALACE=~/.locus

locus-mcp

See MCP Server Configuration for the full client setup guide and spec/mcp-server.md for architecture details.


Recall

locus recall answers "what do I already know about this?" in one call, fast enough to run on every prompt from a hook. It keeps a SQLite FTS5 index (standard library only, no PyYAML) over any number of markdown roots: a palace, an OKF bundle, a Claude Code memory directory, or all of them at once.

locus recall --root ~/memory --root ./docs "why does the flux kustomization stall"
Recalled memory:
  1. Flux healthcheck stall (human-reviewed, 2026-08-22) /home/me/memory/project_flux-healthcheck-stall.md Flux Kustomization with wait:true stalls on health checks for a bad revision ...
  2. [STALE] Old Flux bootstrap procedure (unverified, 2025-11-02) /home/me/docs/runbooks/flux-bootstrap.md Bootstrap Flux with a personal access token ...
FlagDefaultMeaning
--root DIR.locus.toml, then LOCUS_PALACEDirectory to index; repeatable
-k N3Number of hits
--budget BYTES4096Hard cap on text output
--include journaloffInclude type: Journal files
--type TYPEallOnly this frontmatter type; repeatable
--jsonoffPrint a JSON list instead of text
--refreshoffRebuild the index from scratch

Frontmatter drives the result: title (or name, or the first heading), description, tags, type (or metadata.type), modified (else generated.at, else file mtime), status, stale_after, and verified. Ranking is bm25 with title and description weighted above the body; exact ties go to human-reviewed files, then to the newest modified. A hit is flagged STALE when its stale_after has passed or its status is deprecated. Trust tiers follow OKF: unverified, machine-confirmed (only non-human verified entries), human-reviewed (any verified entry whose by starts with human:).

Roots can live in a .locus.toml in the project or any parent directory:

[recall]

roots = ["docs", "~/memory/shared"]

The index is stored at ${XDG_CACHE_HOME:-~/.cache}/locus/ .sqlite, never inside a root, and is refreshed incrementally (mtime, then content hash) on every call. With no hits the text output is empty and the exit status is still 0, so a prompt hook can call it unconditionally:

#!/bin/sh

Claude Code UserPromptSubmit hook: whatever this prints is injected as context.

prompt=$(jq -r .prompt) exec locus recall -k 3 --budget 4096 "$prompt"

The MCP server's memory_search uses the same index, so MCP results are ranked the same way. Full rules in spec/recall.md.


Lint and index

locus lint checks markdown roots for Open Knowledge Format v0.2 conformance and the Locus palace conventions. locus index generates the index files those conventions define. Both read the same frontmatter recall indexes, and neither imports the Agent SDK, so a CI job that only checks conformance does not install it.

locus lint  --root docs --check          # CI gate: exit 1 on any error

locus lint --root docs --fix # add inferable fields, rewrite nothing locus index --root docs --check # exit 1 when a generated index drifted

docs/runbooks/valve-chatter.md: error [okf.type-missing] frontmatter has no non-empty type (fix: add type: Runbook) docs/log.md: error [okf.log-order] entries run oldest first: 2026-05-09 follows 2026-05-01 docs/reference/platform.md: warning [locus.size-limit] 214 lines exceeds the 200-line soft limit for a specialty file 2 error(s), 1 warning(s), 1 fixable

lint
FlagDefaultMeaning
--root DIR.locus.toml, then LOCUS_PALACEDirectory to check; repeatable
--checkoffExit non-zero on any error. For CI
--strictoffTreat warnings as errors under --check
--fixoffAdd inferable fields. Never rewrites an existing key
--type-map DIR=TYPEnoneInfer this OKF type under DIR; repeatable
--archive-glob GLOBnonePaths that should carry status: deprecated; repeatable
--jsonoffPrint a JSON report instead of text

Rules split in two. okf.* checks what the specification requires: a parseable frontmatter block with a non-empty type on every non-reserved document, an index.md with no frontmatter beyond a bundle-root okf_version, a log.md that is date-headed and newest first, and ISO 8601 timestamps. Unknown keys and unknown type values are never reported: the spec requires consumers to tolerate both. locus.* checks the palace conventions: the size limits from spec/size-limits.md and the room main-file rule from spec/room-conventions.md.

Errors fail --check; warnings are advisory. A palace legitimately carries no frontmatter at all, so on a palace root the missing type rules are warnings rather than a CI failure on a layout the palace spec itself describes.

--fix adds three fields and only three: type from --type-map or [lint.types], generated.at from the file's first git commit, and status: deprecated for archive paths. It never rewrites or deletes a key, it never invents a generated block (nothing in a file says who produced it), and running it twice produces identical bytes.

index
FlagDefaultMeaning
--root DIR.locus.toml, then LOCUS_PALACEDirectory to index; repeatable
--checkoffWrite nothing; exit non-zero on drift. For CI
--kindautoForce okf, palace, or memory classification
--jsonoffPrint a JSON report instead of text

What gets generated depends on the root: an OKF bundle gets an index.md per directory in section 8 form (* [Title](path) - description, with okf_version: "0.2" frontmatter at the bundle root only), a palace gets the 50-line routing table INDEX.md, and a Claude Code memory directory gets a MEMORY.md of one - [Title](file.md) - description line per topic file. Output is deterministic, so --check is a byte comparison, and only index files are ever written.

Configure both from one .locus.toml:

[lint]

roots = ["docs"] archive_globs = ["archive/*"]

[lint.types] "." = "Reference" runbooks = "Runbook"

Full rules in [`spec/lint-and-index

Only the beginning is shown; read the rest in the repo

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