On Board

Name: On Board
Author: swisspra

by swisspraversion 4.2.0i★ –iRuns on your computer

Registry nameio.github.swisspra/On_Board

Install

Overview

Description from the maker (original English)

Cross-platform shared memory and ticket coordination for AI agents across MCP clients.

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↓ 66/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
You need to fill in: AGENT_PROJECT_DIR

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: AGENT_PROJECT_DIR

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": {
    "on-board": {
      "command": "uvx",
      "args": [
        "onboard-memory-mcp"
      ],
      "env": {
        "AGENT_PROJECT_DIR": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add on-board --env AGENT_PROJECT_DIR=your-value -- uvx onboard-memory-mcp

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": {
    "on-board": {
      "command": "uvx",
      "args": [
        "onboard-memory-mcp"
      ],
      "env": {
        "AGENT_PROJECT_DIR": "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": {
    "on-board": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "onboard-memory-mcp"
      ],
      "env": {
        "AGENT_PROJECT_DIR": "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

AGENT_PROJECT_DIR required env
Absolute path to the project that owns the .agent-mem directory.

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 9, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account swisspra, 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 5745 servers in this category
Published to the registry more recently than 74% of the other 5745 servers in this category
Basic checks equal to or better than 100% of the other 5745 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 On Board 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: AGENT_PROJECT_DIR

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 (4.2.0) reached the registry 13 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

Context7

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

by upstashWidely used↓ 972.7K/wk★ 64.2KUpdated 2 days ago

Computer or cloud

Install

Codebase Memory

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

by DeusDataWidely used↓ 18K/wk★ 48.1KUpdated 22 days ago

Runs on your computer

Install

Exa

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

by exa-labsWidely used★ 5.2KUpdated 2 months ago

Runs in the cloud✓ exa.ai

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.

On Board

Shared project memory for agents.

One MCP server, one project memory folder, many IDEs and agent clients.

Skill-first setup, one shared runtime, and project-scoped configuration.

What this is

On Board is a local MCP server for coordinating AI agents across a project. It gives Claude Desktop, Claude Code, Codex, Cursor, Antigravity, and other MCP clients the same project memory, ticket queue, and handoff history.

The goal is simple: when one agent stops and another agent continues, the next agent should not need the human to explain the project again.

onboard → read memory → claim work → write progress → hand off

Everything stays local to the project unless you choose to connect other tools.

Quick start (skill-first)

Install the onboard skill from inside the project you want to configure:

# Node.js 22.20 or later; select your agent when prompted.

npx skills add swisspra/On_Board --skill onboard

npx runs the skills CLI from npm; the skill itself comes from Git. No separate On Board npm package is needed. The skill guides runtime installation and native client configuration; installing the skill alone does not start an MCP server, upgrade an existing shared runtime, or initialize memory.

Native resources require a verified 4.2+ runtime. A working 4.1.1 or source installation can stay in place; see runtime upgrades or the optional source runtime.

Then send your agent this prompt:

Using /onboard, set up On Board for this project. Keep existing memory and other MCP servers unchanged.

The /onboard skill reuses one shared onboard-memory-mcp runtime and configures one project — the one you run it in — for one client of your choice. It resolves an existing runtime first and installs the shared runtime only when none is present (reuse, not a per-project install). It reads and writes only that project's own MCP config. It does not initialize or migrate .agent-mem/, and it does not touch other MCP servers, your instructions, or hooks.

What happens, in order:

Reload your agent after the install so it discovers the new /onboard skill (reopen the window, or start a new session).
The skill resolves or installs the shared runtime and writes the project's MCP config.
Trust and restart your MCP client so it loads the new agent-memory server. Some clients ask before running a newly added server; approve the prompt, then restart.

Prerequisites are real, not hidden behind a one-command promise:

Node.js ≥ 22.20 to run npx skills.
Setup helper: Python ≥ 3.11. The skill's scripts/setup.py needs it; where the machine's python3 is older, run the one-time helper with uv run --no-project --python 3.12 scripts/setup.py ….
Runtime: Python ≥ 3.10, plus uv or pipx already installed, for the onboard-memory-mcp runtime the skill installs.

Runtime states, so you know where you are: Runtime available (the runtime is resolved or installed) → Configured (the project's MCP config now points at it) → Awaiting trust or restart (the client has not loaded it yet) → Ready (the client shows the agent-memory tools). Setup ends at Ready; initializing or joining a board is a separate step, started only when you explicitly ask the agent to begin work.

Prefer to wire things by hand, or install from a package manager or a clone? Those paths stay fully supported — see Manual installation and docs/SETUP.md.

🚀 v4.0.0 — agents now wake each other

Until v4 this board was pull-only: an agent found out about new work when a human told it to look. v4 ships `memory_wait_for_event` — an agent parks inside one tool call and wakes the moment a peer creates a ticket, submits work, or delivers a verdict.

This is not a demo claim. In the launch trial, a GPT agent (Codex) and a Claude agent closed a full reject → fix → resubmit cycle on this board with zero human relay — the reviewer's fix instructions travelled inside the wake payload, the worker re-read the file, attached a sha256, and resubmitted; the reviewer reproduced the hash byte-for-byte before approving:

Full mechanics in Agent-to-agent: the listening half · breaking changes in CHANGELOG.md · release notes.

Why this exists

Most agent workflows break for boring reasons:

The next chat does not know what the last chat did.
Parallel agents overwrite or redo each other's work.
Important decisions live only in conversation history.
Handoffs are informal, so review and follow-up work drift.

On Board keeps those facts in project-local files under .agent-mem/. The MCP tools expose that memory to any supported client.

Who this is for

Solo developers using more than one agent or IDE
Teams experimenting with multi-agent coding workflows
Projects where handoffs, tickets, and review notes matter
Local-first MCP users who want shared context without a hosted service

It is not an autonomous project manager. Humans still decide what matters, review important changes, and accept the final result.

Manual installation

These paths stay supported; they are just secondary to the skill-first quick start above. Use them to install from a package manager, or to run everything from a clone by hand.

Install the server

The server is published as onboard-memory-mcp. Install it with whichever tool you prefer:

# Homebrew (tap once, then the short name works: brew install onboard-memory)

brew install swisspra/tap/onboard-memory

pipx

pipx install onboard-memory-mcp

uv

uv tool install --python 3.12 onboard-memory-mcp

All three provide the onboard-memory-mcp command (Homebrew also adds a short onboard-memory alias). Homebrew covers macOS and Linux; on Windows use pipx or uv (the command is onboard-memory-mcp.exe). Point your MCP client's command at it instead of python3 onboard_server.py. You can also skip this and run from a clone using the setup paths below. (On Homebrew 6+, approve the one-time tap-trust prompt, or run brew trust swisspra/tap.)

The v4.2.0 runtime uses mcp[cli]>=2.3.0,<3 and the SDK's MCPServer API. The lockfile selects MCP 2.3.0; SDK 1.x is no longer supported. Minimum AnyIO, PyJWT, and cryptography versions also prevent installers from retaining known-vulnerable transitive dependencies.

Headless config (no clone)

With the server installed, wire your MCP client to it directly — no repo checkout, no setup-project.sh:

"mcpServers": { "agent-memory": { "command": "onboard-memory-mcp", "env": { "AGENT_PROJECT_DIR": "/full/path/to/your/project" } } } }

CLI clients (Claude Code, Codex) inherit your shell PATH, so the bare onboard-memory-mcp works.
GUI clients (Claude Desktop, Cursor) launch with a minimal PATH. Use the absolute path from which onboard-memory-mcp (where on Windows) as command — typically /opt/homebrew/bin/onboard-memory-mcp (Homebrew, Apple Silicon), /usr/local/bin/onboard-memory-mcp (Homebrew, Intel), /home/linuxbrew/.linuxbrew/bin/onboard-memory-mcp (Homebrew, Linux), ~/.local/bin/onboard-memory-mcp (pipx / uv on macOS/Linux), or %USERPROFILE%\.local\bin\onboard-memory-mcp.exe (pipx / uv on Windows).

AGENT_PROJECT_DIR is required — it decides which project owns .agent-mem/. Nothing here initializes memory: starting work is a separate step. In your first work session, call memory_init once (creates .agent-mem/) — or memory_bootstrap when adopting an existing repo — then memory_onboard each session. If .agent-mem/ already exists, never bootstrap it again; just join.

The pipx/uv path normally uses prebuilt dependency wheels on supported CPython 3.11+ platforms. Wheel availability depends on Python and architecture; Python 3.10 or Intel macOS may need a C/Rust toolchain. Homebrew bottle builds target ARM macOS and x86_64 Linux. Upstream dependency bottles are currently unavailable for Intel macOS, so Homebrew is not a compiler-free fallback there. Template: configs/binary-mcp.json; full detail and platform notes in docs/SETUP.md.

Set up a project

Choose one setup path:

Option 1: Agent setup

The skill-first quick start above is the recommended path. If you would rather not use the skill CLI, ask an agent to read AGENT_SETUP.md — it delegates to the canonical install reference instead of inventing a second recipe — and to docs/SETUP.md for the manual steps.

Option 2: Script setup

git clone https://github.com/swisspra/On_Board.git

cd On_Board bash setup-project.sh /full/path/to/your/project bash doctor.sh /full/path/to/your/project

Add the generated MCP config to your client:

/full/path/to/your/project/.onboard/mcp.generated.json

Some clients accept this JSON directly. Others require you to merge it into their own MCP settings file.

After memory is initialized, open the dashboard with:

bash /full/path/to/your/project/.onboard/run-dashboard.sh

On Board is installed once. Each project points to the same On Board folder, but gets separate memory through AGENT_PROJECT_DIR.

Each setup-project.sh run also registers the project locally in .onboard/linked-projects.json inside the On Board checkout. This file is gitignored and only helps updates remember which projects point here.

The setup script uses uv sync --inexact to install/update dependencies without pruning local test/dev extras. MCP clients run python3 onboard_server.py; the launcher uses the local .venv directly and rebuilds it only if the venv is missing. This keeps normal startup fast, avoids uv run startup timeouts, and makes a shared central checkout more durable.

On Board does not write memory from end-turn hooks. Current Stop hooks in several agent clients run every turn, which creates noisy memory and can force agents to re-onboard too often.

Optional: add AGENT_MEM_CONTEXT_DIRS to the generated MCP config when agents should read shared docs/specs outside the project folder.

Option 3: Advanced manual setup

If you do not want to run the setup script, install with uv sync, write the MCP config yourself, and add project rules/hooks manually. See docs/SETUP.md.

Setup stops at the config; nothing above initializes memory. When you later start work in the project — a separate, explicit request — do it in a chat with any MCP-aware agent (Claude Desktop, Claude Code, Cursor, Codex, Antigravity). Skip memory_bootstrap if .agent-mem/ already exists and just join:

memory_bootstrap(

agent_name="dev-main", description

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

On Board

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

On Board

What this is

Quick start (skill-first)

🚀 v4.0.0 — agents now wake each other

Why this exists

Who this is for

Manual installation

Install the server

pipx

uv

Headless config (no clone)

Set up a project

Option 1: Agent setup

Option 2: Script setup

Option 3: Advanced manual setup