
Together Open Data ScientistOpen-source ReAct agent that runs Python to explore data, build models, and generate analysis reports
Overview
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
- Category
- AI Data Analysts
- 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 6 battles in the Pantheon.
Last 5 battles
- #6
AI Data Analysts Showdown — July 8, 2026
Jul 8, 2026 · #6 of 9
- #3
AI Data Analysts Showdown — March 20, 2026
Mar 20, 2026 · #3 of 9
- #2
AI Data Analysts Showdown — December 26, 2025
Dec 26, 2025 · #2 of 6
- #1
AI Data Analysts Showdown — December 18, 2025
Dec 18, 2025 · #1 of 4
- #5
AI Data Analysts Showdown — April 5, 2024
Apr 5, 2024 · #5 of 9
Reviews
Average from 4 ratings.
Sign in to leave a review.
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.
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.
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.
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.
Q&A
What output does the tool generate and how can I retrieve it?
After analysis, the agent can generate a markdown report using the `--write-report` flag, and it also produces a trace of the reasoning steps. Both the report and trace are saved to your working directory, making it easy to review the results and the code that was executed.
Asked by Julia Steiner · Aug 21, 2025
Can I customize the language model or limit the reasoning steps?
Yes. The agent is model‑agnostic; you can specify any compatible LLM for the reasoning part and set a maximum number of ReAct iterations, allowing you to control how many reasoning‑and‑acting cycles the agent performs on a task.
Asked by Sami Virtanen · Aug 6, 2025
What are the requirements for using the cloud (TCI) execution mode?
To use the "tci" mode you need a valid Together AI API key, which authenticates requests to the Together Code Interpreter service. The service runs your code in a managed cloud sandbox, so no local Docker installation is required.
Asked by Ines Fernandes · Jul 23, 2025
How does the execution mode choice affect where my Python code runs?
Together Open Data Scientist offers two modes: "internal" runs the generated Python inside a local Docker container for single‑user development, while "tci" sends the code to Together Code Interpreter, a cloud sandbox accessed via the Together AI API. Choose Docker for full local control; choose TCI for cloud execution without local Docker setup.
Asked by Hasan Demir · Jun 15, 2025
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