Files

T

Igor Lins e Silva c35686c9e1 docs(install): recommend uv as the package manager

End-user installs now lead with `uv tool install mempalace`, with
`pip install mempalace` kept as a fallback. Dev/contributor docs lead
with `uv sync --extra dev` and `uv run` for tests/benchmarks/lint, with
the equivalent pip recipe kept inline. The shipped `/mempalace:init`
skill instructions (mempalace/instructions/init.md) try `uv tool install`
first when uv is on PATH, then fall back through the pip variants.

Adds a .python-version pin at 3.12 because the lockfile's
onnxruntime==1.24.3 only ships wheels for Python >=3.11; without the
pin, `uv sync` on a host where uv prefers 3.10 fails with no source
distribution available, which would make the documented command a
footgun. pyproject's `requires-python = ">=3.9"` is unchanged — pip
users on 3.9/3.10 are unaffected.

Files updated: README.md, CONTRIBUTING.md, CLAUDE.md, the gemini-cli
guide and example, the .claude-plugin / .codex-plugin READMEs, the
mempalace SKILL, the openclaw SKILL, tools/save.md, the three
benchmarks docs, and the corresponding website mirrors.

2026-05-08 01:38:00 -03:00

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Gemini CLI Integration Guide

This guide explains how to set up MemPalace as a permanent memory for the Gemini CLI.

Prerequisites

Python 3.9+
Gemini CLI installed and configured

1. Installation

On many Linux systems, installing Python packages globally is restricted. We recommend uv, which creates and manages a local virtual environment for you.

# Clone the repository (if you haven't already)
git clone https://github.com/MemPalace/mempalace.git
cd mempalace

# Create the venv and install MemPalace + dependencies in editable mode
uv sync

This produces a .venv/ directory inside the repo with everything installed. If you prefer plain pip:

python3 -m venv .venv
.venv/bin/pip install -e .

2. Initialization

Set up your "Palace" (the database) and configure your identity.

# Initialize the palace in the current directory
uv run python -m mempalace init .

Identity and Wings (Optional but Recommended)

You can manually define who you are and what projects you work on by creating/editing these files in ~/.mempalace/:

~/.mempalace/identity.txt: A plain text file describing your role and focus.
~/.mempalace/wing_config.json: A JSON file mapping projects and name variants to "Wings".

3. Connect to Gemini CLI (MCP)

gemini mcp add mempalace /absolute/path/to/mempalace/.venv/bin/python3 -m mempalace.mcp_server --scope user

Note: Use the absolute path to ensure it works from any directory.

4. Enable Auto-Saving (Hooks)

To ensure the AI saves memories automatically when conversation history becomes too long, add a PreCompress hook to your Gemini CLI settings.

Edit your ~/.gemini/settings.json and add the following:

{
  "hooks": {
    "PreCompress": [
      {
        "matcher": "*",
        "hooks": [
          {
            "type": "command",
            "command": "/absolute/path/to/mempalace/hooks/mempal_precompact_hook.sh"
          }
        ]
      }
    ]
  }
}

Make sure the hook scripts are executable:

chmod +x hooks/*.sh

5. Usage

Once connected, Gemini CLI will automatically:

Start the MemPalace server on launch.
Use mempalace_search to find relevant past discussions.
Use the PreCompress hook to save new memories before they are lost.

Manual Mining

If you want the AI to learn from your existing code or docs immediately, run the "mine" command:

uv run python -m mempalace mine /path/to/your/project

Verification

In a Gemini CLI session, you can run:

/mcp list: Verify mempalace is CONNECTED.
/hooks panel: Verify the PreCompress hook is active.

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