Cross-platform shared memory and ticket coordination for AI agents across MCP clients.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Shared project memory for agents. One MCP server, one project memory folder, many IDEs and agent clients. New in v4.0.0: agents wake each other. The human stops being the message pump.
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.
Everything stays local to the project unless you choose to connect other tools.
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 β 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 β fix β resubmit cycle on this board with zero human relay β 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 Β· breaking changes in CHANGELOG.md Β· release notes.
Most agent workflows break for boring reasons:
On Board keeps those facts in project-local files under .agent-mem/.
The MCP tools expose that memory to any supported client.
It is not an autonomous project manager. Humans still decide what matters, review important changes, and accept the final result.
The server is published as onboard-memory-mcp.
Install it with whichever tool you prefer:
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.)
With the server installed, wire your MCP client to it directly β no repo
checkout, no setup-project.sh:
PATH, so the bare
onboard-memory-mcp works.PATH. Use the
absolute path from which onboard-memory-mcp (where on Windows) as
command β 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 β it decides which project owns .agent-mem/.
In your first chat, call memory_init once (creates .agent-mem/), then
memory_onboard each session. Nothing to create by hand.
The pipx/uv path installs from prebuilt wheels (no compiler) on Python 3.11+
for Linux, Windows, and Apple-Silicon macOS; on Python 3.10 or Intel macOS a
couple of Rust/C dependencies may build from source, so prefer brew there.
Template: configs/binary-mcp.json; full detail and
platform notes in docs/SETUP.md.
Choose one setup path:
Ask an agent to read AGENT_SETUP.md and help you set up the project. This is the easiest path if you already have an agent available.
Add the generated MCP config to your client:
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:
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.
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.
In your first chat with any MCP-aware agent (Claude Desktop, Claude Code, Cursor, Codex, Antigravity):
That's it. The agent now sees the project briefing, the open tickets, the recent memory, and the protocol it should follow. Every subsequent action is stamped with its identity.
Full setup details and manual setup: see docs/SETUP.md.
To update an existing install, run bash update.sh in the central On Board
checkout. It will show known linked projects. Refresh all of them with
bash update.sh --refresh-linked, or inspect them with
bash setup-project.sh --list-linked.
When this loop runs cleanly, a single ticket goes from open to "shipped
to production" in 4β15 minutes of agent time. The human checks in at the
end, not in the middle.
Everything above still works pull-style. v4 adds the missing edge: agents can now wake each other instead of waiting for a human to relay messages.
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