mlloop 0.0.1

A scientific-method harness for AI-driven ML training: experiment ledger, hypothesis gates, diagnostics, and data-verdict reports over MCP.

MLLoop

A scientific-method harness for AI-driven machine learning.

Coding agents (Claude Code, opencode, ...) can already write training code and run ten variants overnight. What they don't do by themselves is science: diagnose why a model underperforms, form falsifiable hypotheses, run discriminating experiments, and — when the data itself is the problem — produce evidence strong enough to convince stakeholders.

MLLoop is an MCP server that sits between the agent and your training code and enforces that loop at the tool layer, not via prompts:

• Experiment ledger — every run, hypothesis, and decision recorded in SQLite plus an append-only JSONL event log, all under .mlloop/ in your project.
• Hypothesis gate — run_start refuses any experiment that doesn't test a registered, falsifiable hypothesis. No hypothesis, no run.
• Artifact contract — each run writes standardized predictions.parquet + meta.json; diagnostics never read your training code, so any framework works.
• Diagnostics battery — after every run: error slices, bootstrap noise floor ("what delta counts as evidence"), confusion/residuals, calibration, overfit gap. Diagnosing the previous run is itself a gate: no diagnosis, no next experiment.
• Data Verdict Report — when runs stagnate, forensics_run interrogates the dataset with independent probes (shuffled-label signal check, confident-learning label-noise estimation, conflicting-duplicate bound, learning curve, per-feature signal) and report_generate renders a stakeholder-readable HTML verdict: is the ceiling set by the data or by the modeling? Demo: inject 20% label noise into a clean dataset — the report catches it, quantifies it, and lists the suspect rows.
• Dashboard — mlloop dashboard serves a local read-only UI: the iteration tree (nodes colored by improvement vs parent, edges labeled with the driving hypothesis), hypothesis board, metric timeline, per-run diagnosis details, and the verdict viewer — built for the morning-after review of an overnight autonomous session. Auto-refreshes while the agent works.

Status: Phase 2 — ledger, gates, diagnostics, forensics, reports, and dashboard. Full design: DESIGN.md. Agent setup (Claude Code / opencode / Codex): docs/integrations.md.

Quickstart

pip install -e .
cd your-ml-project
mlloop init --agent claude    # or opencode / codex / all — writes the MCP config

Then tell your agent to train a model. The enforced workflow:

Step	Tool	Gate
1	goal_define	Locks dataset, target column, primary metric. Required first.
2	run_start(kind='baseline')	First run must be a simple baseline.
3	diagnose_run	Every finished run must be diagnosed before the next experiment.
4	hypothesis_register	Falsifiable claim about what limits performance, from the diagnosis.
5	run_start(hypothesis_id=...)	Refused without a registered hypothesis.
6	run_finish	Validates the artifact contract before accepting results.
7	hypothesis_resolve / decision_record	Evidence-backed resolution, recorded decisions.
8	forensics_run → report_generate	When stagnating: interrogate the data, render the verdict.

status shows the current state and allowed actions at any time; ledger_query restores full context after an agent restart or context compaction.

Contributing

Issues, design feedback, and pull requests are welcome — see CONTRIBUTING.md. Please note the Code of Conduct.

License

Apache-2.0