Work in progress

Toolsets

• Python Interpreter
• R Interpreter
• Web toolset ()
  • Web fetch with link resolution
  • Web search using DDGS (DuckDuckGo)
• Web browse (legacy)
  • Duckduckgo search
  • Crawl4ai
• Code search ()
  • File pattern search (glob)
  • Content search (grep)
  • Directory listing (ls)
• Notebook toolset (Jupyter)
  • Read, edit, create notebooks
  • Cell management (add, delete, move)
  • Professional templates
• ScraperAPI
  • Google search
  • Web crawl
• Shell
• Convert toolset to MCP(Model Context Protocol)
• File editor/Filesystem access ()
• File transfer
• RAG system
• Todo/Task management (Claude Code style)
• Code search (glob, grep, ls)
• Jupyter notebook editing
• LaTeX compiler
• Browser-use

Installation

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

Additional Dependencies

For enhanced web functionality:

pip install ddgs  # For reliable web search
pip install beautifulsoup4  # For better HTML parsing

For notebook functionality:

pip install nbformat  # For Jupyter notebook handling

All toolsets use Rich for beautiful console output, which is included in the base installation.

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 Toolset	pantheon.toolsets.web	** web fetch and search with DDGS.**
Domain Research	pantheon.toolsets.domain_research	OmicVerse-backed domain research with demo fallback.
Code Search	pantheon.toolsets.code_search	** file search with glob, grep, ls.**
Notebook Toolset	pantheon.toolsets.notebook	Edit and manage Jupyter notebooks (no execution).
File Editor	pantheon.toolsets.file_editor	** file editing with diffs.**
File Manager	pantheon.toolsets.file_manager	General filesystem access and management.
Web Browse (legacy)	pantheon.toolsets.web_browse	Legacy web search using DuckDuckGo and Crawl4ai.
ScraperAPI	pantheon.toolsets.scraper	Use ScraperAPI to perform google search and web crawl.
Vector RAG	pantheon.toolsets.vector_rag	Query a vector based RAG database.
File Transfer	pantheon.toolsets.file_transfer	Transfer files between different systems.
Todo Management	pantheon.toolsets.todo	Claude Code-style task management with status tracking.
Endpoint Hub	pantheon.toolsets.endpoint	Manage and coordinate multiple toolset endpoints.

Toolsets

The following toolsets provide Claude Code-like functionality for enhanced developer experience:

Web Toolset (pantheon.toolsets.web)

• web_fetch: Fetch web content with intelligent link resolution and markdown conversion
• web_search: Search using DDGS (DuckDuckGo Search) library - reliable and fast

Code Search Toolset (pantheon.toolsets.code_search)

• glob: Find files by patterns (e.g., *.py, **/*.ts)
• grep: Search content across files with regex support
• ls: List directory contents with detailed information

Notebook Toolset (pantheon.toolsets.notebook)

• read_notebook: Display notebook contents with beautiful formatting
• edit_notebook_cell: Edit specific cells (code/markdown)
• add_notebook_cell: Add new cells at specific positions
• create_notebook: Create new notebooks with professional templates

File Editor Toolset (pantheon.toolsets.file_editor)

• read_file: Read files with line numbers
• edit_file: Edit files with diff display
• write_file: Create new files

Todo Management Toolset (pantheon.toolsets.todo)

• add_todo: Add new todo items with status tracking
• show_todos: Display todos in Claude Code style with checkboxes
• update_todo_status: Change status (pending ☐, in_progress ◐, completed ☑)
• complete_todo: Mark todos as completed
• start_todo: Mark todos as in progress
• remove_todo: Delete todos
• clear_completed_todos: Remove all completed tasks

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

python -m pantheon.toolsets.python

Example usage of new toolsets:

# Start web toolset for web fetch and search
python -m pantheon.toolsets.web

# Start code search toolset for file operations
python -m pantheon.toolsets.code_search

# Start notebook toolset for Jupyter editing
python -m pantheon.toolsets.notebook

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

Domain Research Toolset (pantheon.toolsets.domain_research)

• run_research: Generate a concise, cited report for a query.
  • Backends: demo (offline), web, web:duckduckgo, web:tavily, web:embed[:backend]
  • Optional LLM synthesis: set synth=True and provide model, base_url, api_key
  • Optional LLM scoping: llm_scope=True (uses OpenAI-compatible API if available)

Start the toolset service:

python -m pantheon.toolsets.domain_research

Invoke via RPC (example):

from magique.client import connect_remote
import asyncio

async def demo():
    s = await connect_remote("domain_research")
    resp = await s.invoke("run_research", {
        "query": "PBMC annotation best practices",
        "backend": "demo",    # or 'web', 'web:duckduckgo', 'web:embed', ...
        "fmt": "markdown"
    })
    print(resp["report"])  # Markdown or HTML

asyncio.run(demo())

With OmicVerse and live web + LLM synthesis (optional):

resp = await s.invoke("run_research", {
    "query": "Recent advances in scRNA-seq batch correction",
    "backend": "web",  # auto-selects Tavily if TAVILY_API_KEY set else DuckDuckGo
    "synth": True,
    "model": "gpt-5",
    "base_url": "https://api.openai.com/v1",
    "api_key": "<OPENAI_API_KEY>",
    "llm_scope": True
})

Verification and enrichment (optional):

```bash
# Enable verification and network checks
export OV_DR_VERIFY_NETWORK=1

# Enable metadata enrichment from Crossref (optional mailto for User-Agent)
export OV_DR_ENRICH=1
export CROSSREF_MAILTO="you@example.com"  # polite usage per Crossref guidelines

# Enable PubMed enrichment (requires key)
export NCBI_API_KEY=xxxx

Pantheon-CLI integration: the CLI now loads this toolset by default. Launch:

```bash
python -m pantheon_cli.cli --disable_web False --disable_dr False

Then ask the assistant to run domain research (it can call run_research), for example:

"Research PBMC annotation best practices using live web and cite sources"