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Together Open Data ScientistOpen-source ReAct agent that runs Python to explore data, build models, and generate analysis reports

4.3 (4)
Daniel NikulshynReviewed by Daniel Nikulshyn·Updated June 2026

Overview

Together Open Data Scientist is an open-source, AI-powered data analysis agent released by Together AI on GitHub. It follows the ReAct (Reasoning + Acting) framework, alternating between language-model reasoning steps and concrete Python code execution to carry out end-to-end data science tasks such as exploring datasets, computing summary statistics, building models, and producing detailed written analysis reports. The agent can execute Python in one of two modes. The "internal" mode runs code locally inside a Docker container, which is suited to single-user local development, while the "tci" mode offloads execution to Together Code Interpreter (TCI), a cloud sandbox accessed through the Together AI API. Users can upload a data directory for automatic ingestion, set a maximum number of reasoning iterations, and pick which underlying model drives the agent — DeepSeek-V3 is the default, but Llama models and others available through Together's platform can be specified. It is distributed as a pip-installable package (open-data-scientist) and exposes both a command-line interface and a Python API. The CLI supports options such as --write-report to generate a Markdown analysis report, --save-trace to log the full query and execution trace, and session reuse via session IDs. The Python API centers on a ReActDataScienceAgent class that takes a natural-language task and returns results. The project is explicitly labeled experimental software. Because all code and analysis are AI-generated, outputs may contain errors or suboptimal approaches and are best treated as a starting point for exploration and learning rather than production decision-making. The maintainers stress that human oversight and validation are required, especially for critical business or research applications. Compared with commercial AI data-analysis assistants like ChatGPT's Advanced Data Analysis or notebook copilots, Together Open Data Scientist is differentiated by being fully open source, self-hostable, model-agnostic within Together's ecosystem, and capable of autonomously chaining many code-execution steps toward a complete report rather than a single one-shot answer.

Key features

  • ReAct reasoning-and-acting agent loop
  • Two execution modes: local Docker or Together Code Interpreter cloud
  • Automatic data directory upload for analysis
  • Markdown report generation with --write-report
  • Configurable model and maximum reasoning iterations
  • Command-line interface and programmatic Python API

Pricing

Model
Free
Rating
4.3 / 5 (4)

Use cases

Automated Dataset Exploration

Run the agent on a new dataset to perform exploratory data analysis with Python and receive a detailed report of findings.

Model Building Assistance

Use the agent to prototype and build machine learning models on your data, either locally or in the cloud.

Analysis Report Generation

Generate detailed written analysis reports summarizing dataset insights and model results for stakeholders.

Local or Cloud Python Workflows

Execute Python-based data science tasks flexibly on a local machine or in cloud environments depending on compute needs.

Pros & Cons

Pros

  • Open source and self-hostable
  • Runs real Python code locally via Docker or in the cloud via TCI
  • Model-agnostic, with configurable underlying LLM and iteration count
  • CLI and Python API, plus automatic report and trace generation

Cons

  • Explicitly experimental; AI-generated code may contain errors
  • Requires human review and not suited for production decisions
  • Docker mode has session isolation and security limitations
  • Tied to a Together AI API key for cloud execution

Battle record

Across 1 battle in the Pantheon.

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Last battle

Reviews

4.3

Average from 4 ratings.

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V

Victor Nguyen

Apr 23, 2026

Solid for our team

We rolled this out across the team last quarter and the value for money is strong. The integrations fits neatly into how we already work, and the automation removed a step we used to do by hand. A few rough edges remain, which is the main caveat, but it has held up under daily use.

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Kwame Mensah

Apr 12, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is the API — handled better than most — and it is genuinely easy to set up. A few rough edges remain is my one real gripe. Worth the time if this is your use case.

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Liam O’Connor

Nov 5, 2025

Use it every day

Honestly didn't expect to like it this much. The API is exactly what I needed, and it saves real time. I do wish the docs could be deeper, but I reach for it almost every day now and it just clicks.

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Diego Fernández

Sep 24, 2025

Does the job

Pretty happy overall. The core workflow just works and support is responsive. The docs could be deeper can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

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