From Zero to One: Building An Autonomous and Open Data Scientist Agent from Scratch
Together AI
Together published a guide for building an autonomous data scientist agent using open-source language models and the Together Code Interpreter, implementing a ReAct pattern where the agent reasons in natural language then executes Python code for data analysis tasks. The agent was evaluated on OpenAI's MLE-bench and DABStep benchmarks using DeepSeek-V3, with examples showing it can self-correct when encountering errors like token limit constraints or model convergence issues. The implementation is open-sourced and accessible via a CLI tool that requires only a Together API key, allowing users to run automated data science workflows including data exploration, model training, and report generation.
Why it matters
Build a data scientist agent using Together’s open-source models and Code Interpreter—easy to implement, solid benchmarks, and full code on GitHub.
