Together Open Data Scientist

An AI-powered data analysis assistant that follows the ReAct (Reasoning + Acting) framework to perform comprehensive data science tasks. The agent can execute Python code either locally via Docker or in the cloud using Together Code Interpreter (TCI).

⚠️ Experimental Software Notice

This is an experimental tool powered by large language models. Please be aware of the following limitations:

• AI-Generated Code: All analysis and code is generated by AI and may contain errors, bugs, or suboptimal approaches
• No Guarantee of Accuracy: Results should be carefully reviewed and validated before making important decisions
• Learning Tool: Best suited for exploration, learning, and initial analysis rather than production use
• Human Oversight Required: Always verify outputs, especially for critical business or research applications
• Evolving Technology: Capabilities and reliability may vary as the underlying models are updated

🚀 Quick Start

Install Together Open Data Scientist using PyPI

pip install open-data-scientist

Run Together Open Data Scientist using command line and TCI

# export together api key
export TOGETHER_API_KEY="your-api-key-here"

# run the agent
open-data-scientist --executor tci --write-report

📖 Example Output

Our Open Data Scientist can perform comprehensive data analysis and generate detailed reports. Below is an example of a complete analysis report for molecular solubility prediction (see the example):

Report Example

🤖 Install from Source

Prerequisites

• Python 3.12 or higher
• uv - Fast Python package manager
• Together AI API key (get one at together.ai)
• Docker and Docker Compose (for local execution mode)

Installation

Clone the repository:

cd open-data-scientist

Install the package:

# Install uv (faster alternative to pip)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create and activate virtual environment
uv venv --python=3.12
source .venv/bin/activate
uv pip install -e .

Set up your API key:

export TOGETHER_API_KEY="your-api-key-here"

(Optional, needed when using docker for code execution) Docker Mode Setup

⚠️ Important: Docker mode has session isolation limitations and security considerations for local development. (1) Session isolation: While user variables are isolated between sessions, module modifications and global state changes affect all sessions. (2) Host directory access: The container has read-write access to specific host directories. (3)Best for: Single-user local development and data analysis workflows. For detailed technical information, security warnings, and setup instructions, see the Interpreter README.

1. launch docker service:

   cd interpreter
   docker-compose up --build -d

2. Stop services:

   docker-compose down

Usage

1. Command Line Interface (CLI): The easiest way to get started is using the command line interface

# Basic usage with local Docker execution
open-data-scientist

# Use cloud execution with TCI
open-data-scientist --executor tci

# Specify a custom model and more iterations
open-data-scientist --model "deepseek-ai/DeepSeek-V3" --iterations 15

# Use specific data directory
open-data-scientist --data-dir /path/to/your/data

# Combine options
open-data-scientist --executor tci --model "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo" --iterations 20 --data-dir ./my_data

CLI Options

Option	Short	Description	Default
--model	-m	Language model to use	deepseek-ai/DeepSeek-V3
--iterations	-i	Maximum reasoning iterations	20
--executor	-e	Execution mode: tci or internal	internal
--data-dir	-d	Data directory to upload	Current directory (with confirmation)
--session-id	-s	Reuse existing session ID	Auto-generated
--help	-h	Show help message	-

2. Python API: For programmatic usage, you can also use the Python API directly

from open_data_scientist.codeagent import ReActDataScienceAgent

# Cloud execution with TCI
agent = ReActDataScienceAgent(
    executor="tci",
    data_dir="path/to/your/data",  # Optional: auto-upload files
    max_iterations=10
)

# Local execution with Docker
agent = ReActDataScienceAgent(
    executor="internal", 
    data_dir="path/to/your/data",  # Optional: auto-upload files
    max_iterations=10
)

result = agent.run("Explore the uploaded CSV files and create summary statistics")

🎯 Execution Modes

The ReAct agent supports two execution modes for running Python code:

Feature	TCI (Together Code Interpreter)	Docker/Internal
Execution Location	☁️ Cloud-based (Together AI)	🏠 Local Docker container
Setup Required	API key only	Docker + docker-compose
File Handling	☁️ Files uploaded to cloud	🏠 Files stay local
Session Persistence	✅ Managed by Together	✅ Local session management
Session Isolation	✅ Independent isolated sessions	⚠️ Limited isolation (see below)
Concurrent Usage	✅ Multiple users/processes safely	⚠️ File conflicts possible
Dependencies	Pre-installed environment	Custom Docker environment
Plot Saving	✅ Can save created plots to disk	❌ Plots not saved to disk

⚠️ Important Privacy Warning

TCI Mode: Using TCI will upload your files to Together AI's cloud servers. Only use this mode if you're comfortable with your data being processed in the cloud.

Docker Mode: All code execution and file processing happens locally in your Docker container. For detailed technical information, security warnings, and setup instructions, see the Interpreter README