agentic-ml

An agentic framework that tackles a data scientist's machine learning problems with LLM agents. Instead of a fixed AutoML search space, an agent writes and runs real training code in an experimentation loop, keeps an auditable record of every trial, and exports a lightweight, production-ready model.

Status: early prototype. The first (and currently only) supported task is tabular regression. Agent-written trials may only use numpy, pandas and scikit-learn as ML dependencies — this keeps the evaluation environment predictable, but also means no deep learning / gradient-boosting libraries (e.g. XGBoost, LightGBM, PyTorch) are available yet. The sandbox that runs agent-written code is intentionally naive: see Security note for exactly what it does and does not protect against.

Table of Contents

• Installation
• Links of interest
• Security note
• Development
• License

Installation

Requires Python 3.11 (this project currently pins >=3.11,<3.12).

uv add jcaste05-agentic-ml              # base install: the light production model only
uv add "jcaste05-agentic-ml[research]"  # adds Strands, the agent runtime that drives research

jcaste05-agentic-ml[research] installs strands-agents itself but not a specific LLM provider. Strands ships each provider as its own extra, so you pick whichever one matches the LLM API you want the research agent to use, e.g.:

uv add "strands-agents[gemini]"   # Google Gemini
uv add "strands-agents[openai]"   # OpenAI-compatible endpoints

See the Strands Agents docs for the full list of supported model providers and how to configure each one.

If you only need AgenticModel to serve an already-exported model in production, skip [research] entirely: it depends solely on numpy, pandas, scikit-learn and joblib.

Links of interest

• Quick example — a runnable end-to-end tutorial notebook (tabular regression on the scikit-learn diabetes dataset).
• Documentation — architecture overview and full API reference generated from the source code's docstrings.

Security note

Research and AgenticModel.load execute Python code produced by the agent (or previously saved by it). Every trial is run out-of-process by a naive sandbox (core/sandbox.py), which only provides:

• A hard timeout on the subprocess (timeout in research(...)), so a hung trial doesn't stall the whole run.
• Environment variable filtering: any variable whose name contains KEY, TOKEN, SECRET, PASSWORD, PASSWD or CREDENTIAL is stripped before launching the subprocess, to reduce accidental secret leakage into agent-written code.
• A prompt-level (not enforced) instruction telling the agent to only use numpy, pandas and scikit-learn.

It does not provide process isolation, filesystem or network sandboxing, or CPU/memory limits — the subprocess runs as the same user, with the same filesystem and network access as your own process. Treat agent-written code and saved artifacts with the same trust level as a pickle: only run/load artifacts you produced or trust.

Development

uv sync --all-extras
uv run ruff check .
uv run ruff format . --check --diff
uv run pytest

To preview the documentation locally:

uv run mkdocs serve

License

Apache-2.0.