loci

Name: loci
Author: IvenKooLab

by IvenKooLabversion 0.4.0i★ –iRuns on your computer

Registry nameio.github.IvenKooLab/loci

Install

Overview

Description from the maker (original English)

Cross-session memory and hybrid RAG over your notes and docs. Local-first, with cited answers.

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
UsageiNot measured yet★ –

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?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "loci": {
      "command": "uvx",
      "args": [
        "loci-rag"
      ]
    }
  }
}

Run this in your terminal

claude mcp add loci -- uvx loci-rag

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "loci": {
      "command": "uvx",
      "args": [
        "loci-rag"
      ]
    }
  }
}

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": {
    "loci": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "loci-rag"
      ]
    }
  }
}

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 IvenKooLab, 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 5666 servers in this category
Published to the registry more recently than 43% of the other 5666 servers in this category
Basic checks equal to or better than 100% of the other 5666 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 loci 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 (0.4.0) reached the registry 2 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 yesterday

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 21 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 2 months ago

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.

English · 简体中文 · 繁體中文 · 日本語 · 한국어

Turn scattered notes, project docs and agent memories into a knowledge base you can search and ask questions about — as an MCP server, from your terminal, or in the browser.

loci combines vector search with BM25 and returns answers with file > section citations. Your files and index stay on your machine. Embedding and chat requests go to the providers you configure; local endpoints can keep the entire workflow offline.

MCP is the point. Mount loci-mcp in Claude Desktop, Cursor, Cline or any other MCP host and your assistant gets the whole knowledge base as tools. The console and a desktop shell are alternate front-ends to the same index — you never need them.

Quick start · MCP setup · Web console · Usage guide · 中文说明

*Actual console UI with fictional demo data. No personal knowledge base, account or API key is connected; counts are illustrative.*

Dark theme preview

What you can do

Need	How loci helps
Find a decision buried in your notes	Hybrid search across directories, with tag, path and date filters
Ask a question and check the evidence	Answers cite source sections; optional claim-by-claim verification
Carry context between AI tools	MCP tools share one instance; durable memories are Markdown files
See what each coding agent remembers	Harness discovery, file browsing, per-tool stats and digest notes
Manage the knowledge base visually	Built-in Web console for search, Q&A, memories, indexing and audit
Resume a task exactly where it left off	Structured state by exact key (`brain_state_save` / `brain_state_get`) — no ranking in the way
Prune memory with evidence instead of vibes	Memory health: citations, corrections and age per note (`loci memory-health`)

Markdown, text, HTML, org-mode and chat exports work out of the box. Optional extras add PDF, Word and OCR support. Keep using your existing note editor or Obsidian vault.

What belongs where

Three kinds of "memory" fail differently, and mixing them is the classic misuse:

Kind	Lives in	Retrieved by	Right for	Wrong for
Free-text knowledge	memory notes (`brain_remember`)	hybrid retrieval	"what do we know about X"	exact resume of a task
Structured state	`state/ / .json` (`brain_state_save`)	exact key, no ranking	task snapshots, entity records, slot state worth persisting	prose you might want to fuzzy-find
Live dialogue slots	the host agent's session	the host's own context	this turn's variables	anything you expect to survive the session

Rules of thumb:

If retrieval returning a *similar but wrong* record would be a bug, it is state — key it and fetch exactly (brain_state_get type=task key=8f3c).
Real-time slot values change every turn; do not put them in the vector store. Persist the ones worth keeping across sessions as state records.
Prose that a future question might phrase differently is knowledge — brain_remember it, and let ranking do the work.

State records are plain JSON, hand-editable, and deliberately not indexed into vectors: that is the boundary, not an omission.

Memory health

Every search or answer that uses a memory note is ledgered (citations.jsonl in the instance directory). loci memory-health joins that with feedback (loci feedback bad — which also sinks the rated chunks in later retrieval) and age, and flags notes worth looking at:

$ loci memory-health

12 memory note(s), most-cited first cited corr age(d) last-cited flags file 17 0 41.2 0.3 - 20260920-103000-deploy-policy.md 0 2 63.8 - corrected 20260830-090000-old-strategy.md 0 0 51.4 - never-cited 20260901-120000-random-idea.md

corrected = rated bad after being used; never-cited = old and unused. Both are candidates for brain_forget — with evidence, not vibes. loci sees tool calls, not your conversation, so corrections reach it only through explicit feedback; the report never silently rewrites retrieval.

Quick start

Try it first, with nothing configured

pip install loci-rag

loci demo

loci demo copies six fictional notes into ./loci-demo-notes, chunks them and answers three example questions — cited down to file > section — with no config.toml, no API key and no network. It is keyword-only (BM25, the same ranking hybrid search fuses with vector hits), which is exactly the point: something runs before you commit a key. Add [embed] later and the same index goes hybrid.

*Actual loci demo output on the bundled sample notes; paths shortened, timings vary.*

Index your own notes

Requires Python 3.11+. Start from source so the example configuration is available:

git clone https://github.com/IvenKooLab/loci.git

cd loci python -m pip install -e . cp config.example.toml config.toml

Edit config.toml: fill in [llm] and [embed], and change [[sources]].path to your notes directory. The embedding endpoint must support /embeddings; chat and embeddings may use different providers.

loci ingest

loci search "deployment decisions" loci ask "What did we decide about deployment, and why?"

Already have a configuration? Install the package with python -m pip install loci-rag and set LOCI_HOME to your instance directory. For optional document support, use python -m pip install 'loci-rag[pdf,docx]'.

API keys can be supplied through BRAIN_LLM_API_KEY and BRAIN_EMBED_API_KEY. After changing the embedding model, rebuild with loci ingest --force.

Using Obsidian? See the vault setup, privacy and compatibility guide.

Web console

loci serve-http

Open [127.0.0.1:8765/ui](http://127.0.0.1:8765/ui) to browse harness memories, search, ask questions, edit shared notes and inspect indexing activity. Clicking a search result opens an evidence pane with the passage it was cut from — the file > section citation, checkable without leaving the console. The console includes Chinese/English and light/dark switches; no frontend build is required.

By default, anonymous loopback clients have admin access. Behind a reverse proxy or SSH tunnel, set [http] local_admin = false and configure role-based tokens. See access control.

For DeepSeek Harness, loci-dsh brings search, Q&A and memory capture into the Harness workbench through the same HTTP backend.

Connect your AI tools

Point each MCP client at the same `LOCI_HOME` so they share the same config, index and memory files:

"mcpServers": { "loci": { "command": "loci-mcp", "env": { "LOCI_HOME": "/absolute/path/to/your/loci-instance" } } } }

Use an absolute executable path if your client cannot find loci-mcp. The instance directory must contain your config.toml.

MCP tools	Purpose
`brain_search` · `brain_ask`	Retrieve excerpts or get cited answers
`brain_remember` · `brain_forget`	Store or retract durable memories
`brain_links` · `brain_graph`	Explore note links and extracted relations
`brain_wiki` · `brain_harness`	Consolidate knowledge and inspect agent memories
`brain_stats` · `brain_ingest`	Inspect and refresh the index

The server also exposes note resources and briefing, study-plan and contradiction-check prompts. See the MCP guide for details.

*Actual loci-mcp output: initialize, tools/list and prompts/list on the real binary.*

Native build (Rust)

The same retrieval pipeline and MCP surface, with no Python runtime to install — one binary, and the index is a single SQLite file instead of a database directory:

cargo build --release -p loci-cli     # in native/

./target/release/loci-mcp --config ~/.loci/config.toml ./target/release/loci gui # the same console, served natively

Status: retrieval, a subset of MCP tools, harness discovery, migration and the console are ported. Harness file browsing and digest generation remain Python-only. See native/ for what is ported and the fidelity tests that pin it (chunking, the frozen default tokenizer, the CJK bigram decoy case, RRF, and incremental ingest).

The two builds keep separate store formats (SQLite vs ChromaDB directory), but they exchange data: export from Python with loci export, import in the native build with loci import, which refuses to mix embedding spaces unless you pass --force.

A small daily workflow

loci watch                            # keep changed documents indexed

loci remember "Use staging for demos" --title "Demo environment" loci search "demo environment" --tag memory loci ask "How should I prepare a demo?" --verify loci harness # inspect detected agent memories

Indexing is incremental. Notes stay readable as Markdown. For multiple machines, sync source files and memory notes, then rebuild each local index; keep credentials out of the repository.

See a retrieval session

The usage guide includes the original bilingual retrieval example and a small evaluation you can rerun against your own corpus.

Documentation

Guide	Covers
Usage guide / 中文指南	Commands, filters, harness setup, access control and local models
Configuration example	Provider settings, source directories and runtime options
Architecture	Modules and the retrieval pipeline
Changelog · Roadmap	Released changes and planned work
Contributing	Development conventions and offline testing

loci uses the OpenAI-compatible SDK and ChromaDB, with no LangChain or LlamaIndex dependency. BM25, loaders and the MCP transport are implemented in the project.

python -m pip install -e '.[dev,pdf]'

pytest # offline tests; no API keys required

Find loci on Glama, ModelScope and [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-server

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

loci

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

What you can do

What belongs where

Memory health

Quick start

Try it first, with nothing configured

Index your own notes

Web console

Connect your AI tools

Native build (Rust)

A small daily workflow

Documentation