记忆宫殿AI系统

Requirements

• Python 3.9+
• A vector-store backend (ChromaDB by default)
• ~300 MB disk for the embedding model. Onboarding (python -m mempalace.onboarding) offers embeddinggemma-300m (multilingual, 100+ languages, recommended) or all-MiniLM-L6-v2 (English-only, ~30 MB). See the docstring at mempalace/embedding.py for details and migration notes.
• Optional — compute embeddings on a server instead of locally. Set embedding_model: "openai-compat" in ~/.mempalace/config.json together with embedding_api_url / embedding_api_model (and embedding_api_key if the server needs auth) to use any OpenAI-compatible /v1/embeddings endpoint — LM Studio, llama.cpp, vLLM, Ollama's OpenAI shim, or a self-hosted server (e.g. a larger multilingual or GPU-served embedder). Each key is overridable via the matching MEMPALACE_EMBEDDING_API_* env var. When the endpoint is on your machine or LAN, no content leaves your network. Switching to it requires mempalace repair rebuild-index (different vector space).

No API key is required for the core benchmark path.

Install

Agent-guided setup

Install the MemPalace skills first, then ask your coding agent to set up MemPalace. The setup skill detects your system, installs the Python package, configures MCP, and asks whether you want a private local palace, a shared-brain hub, or a client connected to an existing hub:

npx skills add MemPalace/mempalace

The repository exposes three skills: mempalace for guided installation and operations, mempalace-recall for search-before-answer recall, and mempalace-task for logstream delegation. Installing a skill does not by itself install the MemPalace CLI or MCP server; the setup skill guides the agent through those system changes and verifies the live connection.

During guided setup the agent can offer weekly stable-release checks. They are disabled by default, contact only PyPI when enabled, and never install updates automatically. Cached availability appears in scoped mempalace_status fields for the serving runtime and, when a local proxy is present, its client runtime, allowing the agent to explain the release and request authorization before showing an exact upgrade plan. Setup records whether the runtime came from uv tool, pipx, or pip so the plan never proposes an upgrade command for the wrong installation.

Direct CLI setup

MemPalace ships a CLI, so install it in an isolated environment to avoid PEP 668 errors on Debian/Ubuntu/Homebrew Pythons and to keep mempalace's deps (chromadb, numpy, grpcio, …) from conflicting with anything else in your global site-packages.

We recommend uv — uv tool install puts the mempalace CLI in an isolated environment on your PATH:

uv tool install mempalace
mempalace init ~/projects/myapp

pipx works the same way if you prefer it: pipx install mempalace.

Prefer plain pip only inside an activated virtualenv where you explicitly want import mempalace available:

python -m venv .venv && source .venv/bin/activate
pip install mempalace

Docker

A container image is also available for running the MCP server or the CLI without a local Python toolchain. Multi-arch (amd64 + arm64), so it runs natively on Apple Silicon:

docker pull ghcr.io/mempalace/mempalace:latest

Everything persists under /data — palace, config, and the cached embedding model — so mount a volume there and reuse it across runs:

```bash

Quickstart

```bash

Run any CLI command instead. The container only sees what you mount, so