MCTS-Gen: A Generic MCTS Framework

This project provides a generic Monte Carlo Tree Search (MCTS) framework. Its core concept is the replacement of the Genetic Programming (GP) engine from chess-ant with a modern AI agent. While inspired by AlphaZero, it uses a simpler, decoupled approach: the standard UCT algorithm is augmented by an external AI agent that provides policy and value predictions.

Features

• AI-Augmented UCT: Utilizes the standard UCT algorithm. The AI agent enhances the search by providing value predictions and, most importantly, by performing Policy Pruning—narrowing the search space by supplying a pre-filtered list of promising moves.
• AI Agent Integration: Exposes the MCTS engine as a set of tools, enabling seamless integration with AI agents like the Gemini CLI.
• Extensible Game Logic: Easily add support for new games by creating a new game state module.
• Optional Dependencies: Install support for specific games on demand (e.g., pip install mcts-gen[shogi]).

Quickstart

1. Installation

Standard Installation

The core package can be installed directly using pip:

pip install mcts-gen

Installation with Game-Specific Dependencies

To include support for specific games, you can install optional dependencies.

• For Shogi support:

  pip install mcts-gen[shogi]

  This installs the python-shogi library.

• For Chess support:

  pip install mcts-gen[chess]

  This installs the python-chess library.

• For Ligand Generation support:

  pip install mcts-gen[ligand]

  This installs rdkit and pandas for de novo ligand design capabilities.

  Additionally, this feature requires the external tool fpocket for identifying protein binding pockets. It can be installed on Debian/Ubuntu systems using snap:

  sudo snap install fpocket

2. Server Setup for Gemini CLI

To allow the Gemini agent to use the MCTS-Gen tools, you must register the server in your settings.json file. This allows the Gemini CLI to automatically manage the server process and provide the necessary context files.

Note on v0.0.2+: As of version 0.0.2, the core agent instructions are built-in. Specifying a context file is no longer required for standard operation. Only configure the context block if you wish to provide additional instructions to the agent.

Create or update your settings.json file with the following configuration:

{
  "context": {
    "fileName": [
      "src/mcts_gen/AGENTS.md",
      "GEMINI.md"
    ]
  },
  "mcpServers": {
    "mcts_gen_simulator_server": {
      "command": "python",
      "args": [
        "-m",
        "mcts_gen.fastmcp_server"
      ]
    }
  }
}

Note: The context block tells the Gemini CLI to load AGENTS.md (and GEMINI.md if it exists), which is crucial for the agent to understand how to use the tools.

You can place this settings.json file in one of two locations:

1. Project-Specific: ./.gemini/settings.json (inside this project directory)
2. Global: ~/.gemini/settings.json (in your home directory)

For an alternative setup method using the fastmcp command-line tool, please see the official guide:

• Gemini CLI 🤝 FastMCP

Installation with uv (Recommended)

For a faster and more modern package management experience, we recommend using uv.

1. Install pipx and uv:

   # Install pipx (a tool to install and run Python applications in isolated environments)
   sudo apt install pipx
   
   # Install uv using pipx
   pipx install uv

2. Set up the environment and install mcts-gen:

   # Create a virtual environment in your project directory
   uv venv
   
   # Activate the environment
   source .venv/bin/activate
   
   # Install mcts-gen with Shogi support
   uv pip install mcts-gen[shogi]

   To exit the virtual environment, simply run deactivate.

3. Configure gemini-cli with fastmcp: Instead of manually editing settings.json, you can use the fastmcp command to automatically configure the tool server.

   fastmcp install gemini-cli .venv/lib/python3.12/site-packages/mcts_gen/fastmcp_server.py:mcp

   This command will automatically detect and configure the mcts_gen server, creating a .gemini/settings.json file for you.

   Note on the :mcp suffix: The :mcp at the end is required because fastmcp_server.py contains multiple objects. This suffix explicitly tells fastmcp which object is the MCP server instance to be run.

Agent Context Configuration with uv

Note on v0.0.2+: The instructions below are for older versions or for cases where you need to add custom, additional context. As of v0.0.2, specifying AGENTS.md is not required for the agent to function correctly.

If you installed the package using uv or pip, the AGENTS.md file is included inside the package. To allow the Gemini agent to use it, you need to specify its full path in your .gemini/settings.json file.

Add the path to the context.fileName list. The exact path may vary depending on your Python version and environment.

Example .gemini/settings.json:

{
  "context": {
    "fileName": [
      ".venv/lib/python3.12/site-packages/mcts_gen/AGENTS.md",
      "GEMINI.md"
    ]
  },
  "mcpServers": {
    "mcts_gen_simulator_server": {
      "command": "uv",
      "args": [
        "run",
        "fastmcp",
        "run",
        ".venv/lib/python3.12/site-packages/mcts_gen/fastmcp_server.py:mcp"
      ]
    }
  }
}

For Maintainers: How to Release a New Version

The package publication process is automated using GitHub Actions.

a) Releasing to TestPyPI (for testing)

To release a version to the TestPyPI repository for verification, create and push a git tag with a -test suffix.

# Example for version 0.1.0
git tag v0.1.0-test1
git push origin v0.1.0-test1

b) Releasing to PyPI (Official)

To perform an official release, create and push a git tag that follows the semantic versioning format (e.g., vX.Y.Z).

# Example for version 0.1.0
git tag v0.1.0
git push origin v0.1.0

Development

Testing

To run all tests:

pytest

License

This project is licensed under the GPL-3.0-or-later license.