pantheon-toolsets 0.5.0

Distributed toolsets for pantheon-agents, provide service via magique message transfer server.

Pantheon Toolsets

Distributed toolsets for Pantheon Agents.

Work in progress

Toolsets

• Python Interpreter
• R Interpreter
• Web browse
  • Duckduckgo search
  • Crawl4ai
• ScraperAPI
  • Google search
  • Web crawl
• Shell
• Convert toolset to MCP(Model Context Protocol)
• File editor/Filesystem access
• File transfer
• RAG system
• LaTeX compiler
• Browser-use

Installation

git clone https://github.com/aristoteleo/magique-ai.git
cd magique-ai
pip install -e ".[dev]"

Usage

Built-in toolsets:

Toolset	Package path	Description
Python Interpreter	pantheon.toolsets.python	Run Python code in an interpreter.
R Interpreter	pantheon.toolsets.r	Run R code in an interpreter.
Shell	pantheon.toolsets.shell	Run shell commands.
Web browse	pantheon.toolsets.web_browse	Search the web and return the contents of the pages.
ScraperAPI	pantheon.toolsets.scraper	Use ScraperAPI to perform google search and web crawl.
File editor/Filesystem access	pantheon.toolsets.file_manager	Edit files and access the filesystem.
Vector RAG	pantheon.toolsets.vector_rag	Query a vector based RAG database.

Start a toolset, for example, the python interpreter from the command line:

python -m pantheon.toolsets.python

See help with:

python -m pantheon.toolsets.python -- --help
NAME
    __main__.py

SYNOPSIS
    __main__.py <flags>

FLAGS
    -s, --service_name=SERVICE_NAME
        Type: str
        Default: 'python-interpreter'
    --mcp=MCP
        Type: bool
        Default: False
    --mcp_kwargs=MCP_KWARGS
        Type: dict
        Default: {}
    -t, --toolset_kwargs=TOOLSET_KWARGS
        Type: dict
        Default: {}

Development

Project structure:

1. Built-in Toolsets

Test the package

Please start a magique message transfer server first.

python -m magique.server

Then export the server url and run the test:

export MAGIQUE_SERVER_URL=ws://localhost:8765/ws
pytest -s tests/

Environment configration

Firstly, you need docker and buildx installed. See docker docs and buildx docs for installation.

Magique-ai's built-in environments are stored in the environments folder. And all environments could be managed by the environment/build_images.py script:

$ python environment/build_images.py -h
usage: build_images.py [-h] [-a] [-l] [-b TARGET] [--registry REGISTRY_PATH] [--push]

Docker image build automation

options:
  -h, --help            show this help message and exit
  -a, --all             Build all detected images
  -l, --list            List available Docker configurations
  -b TARGET, --build TARGET
                        Build specific image by target name
  --registry REGISTRY_PATH
                        Specify Docker registry path (e.g., ghcr.io/username)
  --push                Push the image(s) to the specified registry after building