Potencia tu agente
Instalación del publicador. No es un endpoint de PlugMCP.
Host a copy with our keys and logs.
Aún no hay una versión empaquetada en el registro. Sigue el README del repositorio.
¿URL remota o un comando en tu máquina? En qué se diferencian las dos instalaciones
Ejecutar en PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
AbrirDel publicador
Long-term memory your AI coding agents actually share. One brain, many agents: what Claude Code learns on Monday, Codex can recall on Friday.
It runs as a single installed executable — no server to start, no container, no database to administer, and nothing leaves your machine. Memories live in one SQLite file in your user directory; embeddings are computed locally on CPU.
Long-term memory your AI coding agents actually share. One brain, many agents — standalone embedded MCP server: SQLite + FTS5 + vector hybrid search, local ONNX embeddings, self-expiring diary. No server, container, or daemon.
Another Brain is a standard Python package — the only prerequisite is Python 3.12+ (no daemon, no root, no container runtime). Install the published package into a venv with pip:
python -m venv .venv
.venv/bin/python -m pip install another-brain # Windows: .venv\Scripts\python
.venv/bin/another-brain setup # Windows: .venv\Scripts\another-brain
Or install a checkout of this repo from its root:
python -m venv .venv
.venv/bin/python -m pip install .
The console script another-brain lands in the venv's bin directory
(Scripts on Windows); activating the venv puts it on PATH.
Restart the harness and your agent has memory. setup is a one-shot
bundle of model pull + connect: it downloads the hash-verified
embedding model (~206 MB) once, then writes the MCP server entry into each
detected harness's own config and installs a skill that teaches the agent
when to use it — no JSON to edit by hand, on any OS. Both steps are
idempotent, so re-running setup is safe; the individual commands stay
available below.
uv is an optional convenience: uv tool install another-brain does the same install and puts another-brain on
PATH in one command. The uv.lock file is a maintainer/development
artifact — pip installs never read it, and uv is never needed at runtime.
Run another-brain connect with no arguments to see which harnesses are
known and which are installed here. Supported today: claude-code, codex,
cursor, gemini-cli, pi.
For pi the connector writes the standard {"command": "another-brain"}
entry into ~/.config/mcp/mcp.json, like every other harness. With the
tools renamed to bare verbs, each harness adds its own prefix on top of the
wire names: pi's adapter exposes another_brain_health, Claude Code shows
mcp__another-brain__health, and the MCP wire names themselves never
change.
Eight tools appear in the agent's toolbox (your harness may prefix them):
Memory here is a diary that forgets on purpose. Each entry gets a lifespan from its importance — 1 to 5 maps to 7, 30, 90, 180, or 365 days — and expires unless an agent reinforces it after actually using it. Nothing accumulates forever, and a memory that turns out to be wrong can be forgotten. Forgetting is soft for 30 days, so a mistake is recoverable.
Search combines two independent signals: semantic similarity (so "how do we handle expired tokens" finds a note about refresh logic) and full-text keyword match (so an exact error string or file path is findable verbatim).
Changed Tool names shortened to bare verbs — remember, search, recent, get, reinforce, forget, health, audit (was brain_*); your harness still prefixes them, so Claude Code shows mcp__another-brain__remember and pi shows another_brain_remember. Hard rename, no aliases. pip is a first-class install path — python -m pip install another-brain; uv stays an optional dev convenience.
recent, admin, connect, and doctor all work without the model
installed.
Memories live in brain.sqlite3 in your per-user data directory, and the
model in your per-user cache directory — another-brain doctor prints both
exact paths. Nothing is uploaded; after model pull the tool never needs the
network again.
Each agent process loads its own copy of the embedding model, about 322 MiB of RAM once it has embedded something — worth knowing if you run several harnesses at once.
Gated in CI on Linux x86_64, macOS 14+ Apple Silicon, and Windows x86_64,
with Python 3.12–3.14. Linux ARM64 and Windows ARM64 work but have no CI
hardware. macOS Intel, macOS 13 and older, and Alpine/musl are not supported
— the install fails clearly rather than silently building from source.
another-brain doctor reports the tier for your machine. Full matrix in
CHANGELOG.md.
MIT licensed.
Long-term memory your AI coding agents actually share. One brain, many agents — standalone embedded MCP server: SQLite + FTS5 + vector hybrid search, local ONNX embeddings, self-expiring diary. No ser
Resumen general
Long-term memory your AI coding agents actually share. One brain, many agents — standalone embedded MCP server: SQLite + FTS5 + vector hybrid search, local ONNX embeddings, self-expiring diary. No server, container, or daemon.
Herramientas
Aún no hay lista de herramientas. Los servidores remotos la reciben tras la primera comprobación de estado.
Preguntas frecuentes
Copia en esta página la configuración stdio para Cursor, Claude Desktop, Claude Code o VS Code. Usa la instalación del publicador, no un proxy de PlugMCP.
Este registro no indica ninguna variable de entorno obligatoria.
Another Brain se ejecuta localmente por stdio.
Última comprobación: 2026-10-28. Long-term memory your AI coding agents actually share. One brain, many agents — standalone embedded MCP server: SQLite + FTS5 + vector hybrid search, local ONNX embeddings, self-expiring diary. No server, container, or daemon.
