Collective Memory

Name: Collective Memory
Author: Hustada

by Hustadaversion 0.1.2i★ –iRuns on your computer

Registry nameio.github.Hustada/collective-memory

Install

Overview

Description from the maker (original English)

MCP server for persistent, semantic memory across AI sessions

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
You need to fill in: OPENAI_API_KEY

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).
You fill in yourself: OPENAI_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

npmNode.js installed (for the npx 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": {
    "collective-memory": {
      "command": "npx",
      "args": [
        "-y",
        "collective-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add collective-memory --env OPENAI_API_KEY=your-value -- npx -y collective-memory

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "collective-memory": {
      "command": "npx",
      "args": [
        "-y",
        "collective-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

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": {
    "collective-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "collective-memory"
      ],
      "env": {
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

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.

Values you fill in

OPENAI_API_KEY secret required env
OpenAI API key for generating embeddings

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 Hustada, 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 7250 servers in this category
Published to the registry more recently than 14% of the other 7250 servers in this category
Basic checks equal to or better than 100% of the other 7250 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 Collective Memory 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?

You need to fill in: OPENAI_API_KEY

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 (0.1.2) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js 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.

Collective Memory

MCP server for persistent, semantic memory across AI sessions. Store context, decisions, and learnings — recall them later with natural language search.

Why

AI assistants forget everything between sessions. Collective Memory fixes that. Store what matters, search by meaning, build context that compounds.

Features

Semantic search — Find memories by meaning, not keywords (OpenAI embeddings + LanceDB)
Automatic deduplication — Won't store near-duplicates (>95% similarity)
Project scoping — Organize memories by project
Type classification — Categorize as decision, milestone, context, learning, or session_summary
Zero config storage — Embedded vector database, no server required

Installation

npm install -g collective-memory

Or clone and build:

git clone https://github.com/Hustada/collective-memory.git

cd collective-memory npm install npm run build

Setup

1. Get an OpenAI API key

Required for embeddings. Get one at platform.openai.com.

2. Add to Claude Code

Add to ~/.claude/settings.json under mcpServers:

"mcpServers": { "collective-memory": { "type": "stdio", "command": "npx", "args": ["collective-memory"], "env": { "OPENAI_API_KEY": "sk-..." } } } }

Or if installed from source:

"mcpServers": { "collective-memory": { "type": "stdio", "command": "node", "args": ["/path/to/collective-memory/dist/index.js"], "env": { "OPENAI_API_KEY": "sk-..." } } } }

3. Add usage instructions to CLAUDE.md

Add to your global ~/.claude/CLAUDE.md:

## Memory

Collective Memory is active. Two tools:

remember(content, project?, type?, tags?) — Persist important context
recall(query, project?, type?, limit?) — Search memory

On session start: Run recall("recent decisions and context") to load relevant memory.

When to remember: after decisions, milestones, completed work, learned patterns. When to recall: session start, context switches, referencing past work.

Types: decision, milestone, context, learning, session_summary.

Tools

remember

Store a memory with semantic embedding.

Parameter	Type	Required	Description
`content`	string	yes	The memory to store — be specific and self-contained
`project`	string	no	Project context (e.g., "myapp", "client-x")
`type`	string	no	One of: decision, milestone, context, learning, session_summary
`tags`	string[]	no	Tags for categorization

Returns the stored memory ID, or existing ID if deduplicated.

recall

Search memories by semantic similarity.

Parameter	Type	Required	Description
`query`	string	yes	Natural language search query
`project`	string	no	Filter to specific project
`type`	string	no	Filter to specific memory type
`limit`	number	no	Max results (default: 10)

Returns array of matching memories with similarity scores.

CLI

Also usable from command line:

# Store a memory

collective-memory remember --content "Decided to use PostgreSQL for the auth service"

Search memories

collective-memory recall --query "database decisions" --limit 5

Pipe content from stdin

echo "Long content here" | collective-memory remember --content-stdin --project myapp

Configuration

Environment Variable	Default	Description
`OPENAI_API_KEY`	(required)	OpenAI API key for embeddings
`COLLECTIVE_MEMORY_PATH`	`~/.collective-memory/data`	Storage location

How it works

Store: Content is embedded using OpenAI's text-embedding-3-small (768 dimensions)
Dedupe: Before storing, checks for >95% similar existing memories
Index: Stored in LanceDB, an embedded vector database
Search: Queries are embedded and matched via cosine similarity

Data

Memories are stored locally at ~/.collective-memory/data (or COLLECTIVE_MEMORY_PATH). It's a LanceDB database — portable, no server process.

To export memories:

npm run export  # Outputs to viz/memories.json

To visualize:

npm run dash    # Opens UMAP visualization at localhost:3333

License

MIT

Credits

Built by The Victor Collective.

Collective Memory

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

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

Collective Memory

Why

Features

Installation

Setup

1. Get an OpenAI API key

2. Add to Claude Code

3. Add usage instructions to CLAUDE.md

Tools

remember

recall

CLI

Search memories

Pipe content from stdin

Configuration

How it works

Data

License

Credits