Agentic Kaggle

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Agentic Kaggle Project

Local-first Kaggle framework with structured workspaces, SQLite experiment tracking, safe notebook mining, and offline export.

Project information

  • Category: ML Systems · Kaggle Automation · Experiment Tracking
  • Interface: CLI workflow with run, ingest-notebook, ensemble, export, and multi-phase helpers
  • Persistence: SQLite timeline with optional MLflow mirroring
  • Workspace model: workspaces/<slug>/raw|derived|runs|exports
  • Core features: baseline training, notebook analysis, ensemble blending, offline notebook export, compliance guardrails

Overview

Agentic Kaggle is a reproducible, local-first framework for running Kaggle competition workflows with clear artifact history. The focus is on the systems layer around experimentation: workspace structure, run tracking, safe data acquisition, ensemble management, and exportability.

Rather than hiding work inside ad hoc notebooks, the project makes each competition run explicit and recoverable, from downloaded inputs to generated metrics, submissions, and export notebooks.

Source code: private / available on request.

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Problem

Kaggle-style work can devolve into unreproducible notebooks, scattered artifacts, and lost experiment context.

Mechanism

Structured workspaces, SQLite tracking, safe notebook analysis, ensemble helpers, and offline exports make runs recoverable.

What It Proves

Evaluation pressure improves when artifacts, metrics, and decisions are inspectable after the run.

Engagement Relevance

Shows the same systems discipline used in agent reliability: repeatable workflows, audit trails, and safe automation.

CTA

Use this as technical proof while reviewing reliability engagement options.

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Project Goal

Build a workflow for Kaggle-style experimentation that is repeatable, inspectable, and export-friendly. The project is meant to reduce the usual notebook chaos by giving each competition a consistent filesystem layout, a run ledger, and a clean path from baseline training to final notebook export.

Run Lifecycle

A typical baseline run follows this sequence:

  • Ingest: download competition files into workspaces/<slug>/raw/.
  • Analyze: infer and write derived/competition_spec.json from the local files.
  • Train baseline: create a run directory with artifacts such as oof.parquet, model.joblib, metrics.json, and submission.csv.
  • Track everything: write runs, metrics, notebook-mining results, and artifacts into agentic_kaggle.db.
  • Export: generate an offline Kaggle notebook under workspaces/<slug>/exports/<run_id>/final.ipynb.

Workspace & Artifact Structure

The framework enforces a predictable per-competition workspace:

  • raw/: downloaded competition inputs
  • derived/: inferred specs, ideas, mined notebook metadata, and generated helper outputs
  • runs/: per-run models, OOF predictions, submissions, metrics, and reports
  • exports/: final notebook packages for Kaggle submission workflows

Experiment Tracking

  • SQLite-first tracking: competitions, runs, artifacts, metrics, and notebook analysis results are persisted locally in SQLite.
  • Optional MLflow: if MLFLOW_TRACKING_URI is configured, parameters, metrics, and artifacts can also be mirrored to MLflow.
  • Relative path hygiene: the system avoids leaking unnecessary absolute paths into the tracking timeline.

Workflow Commands & Automation

  • run: baseline workflow for a competition.
  • ingest-notebook: analyze a local notebook file and store structured hints in SQLite.
  • ensemble: build a blended run from prior OOF predictions and submission files.
  • export: create an offline Kaggle notebook plus copied submission artifacts.
  • sop, search, ideate, report, autoresearch: multi-phase helpers for planning, search, writeups, and iteration.
  • watch, worker, scheduler: queue and process notebook/dataset mining jobs across competitions.

Modeling & Export Strategy

  • Baseline adapters: the default configuration uses a sklearn-based tabular adapter, with time-series support also present in the codebase.
  • Ensembling: ridge blending over saved OOF predictions is supported, and diversity metrics are recorded alongside ensemble outputs.
  • Offline notebook export: exported notebooks are designed to run without internet access inside Kaggle.
  • Forward-looking design: Hugging Face / LLM-backed pieces are scaffolded so more agentic generation can be added without rewriting the core workflow.

Compliance & Safety Defaults

The project is intentionally conservative about Kaggle content handling.

  • API-only mode: default compliance.kaggle_mode=api_only avoids HTML scraping paths.
  • Notebook mining is manual-first: local .ipynb files can be analyzed, but automated kernel scraping is not the default behavior.
  • Kernel pull guardrails: source downloads are disabled by default and capped by count/bytes when explicitly enabled.
  • Retention tools: cleanup helpers can purge old raw notebook pull folders under derived/notebooks/.

Why This Project Is Valuable

This is a strong ML systems project because it treats Kaggle work as an engineering workflow, not just a notebook. It combines experiment tracking, artifact discipline, safe data handling, reproducible exports, and room for future agentic automation.