helices 0.1.1

Framework for reproducible autonomous research loops

Inspired by karpathy/autoresearch, helix generalizes the idea of autonomous AI research loops beyond LLM training. Give an agent a codebase, a metric, and a fixed time budget. It experiments overnight. You wake up to results.

The git history is the research trail. experiments.tsv is the proof. Anyone can clone a helix, run it on their hardware, and independently verify every result.

Concepts

Term	Meaning
helix	A git repo containing helix.yaml + program.md + a codebase the agent can modify
helix.yaml	Machine-readable spec: what to optimize, how to measure it, which files are editable
program.md	Human-written instructions for the agent: domain knowledge, constraints, techniques to try
experiments.tsv	Append-only ledger of every experiment: commit, metric, status, description
helix run	CLI command that launches an autonomous session on your hardware

Quick start

helix is agent-agnostic. Pick a backend or bring your own.

Backend	Install	Requires
ClaudeBackend (default)	pip install 'helices[claude]'	Claude Code CLI
GeminiBackend	pip install helices	Gemini CLI
Custom	pip install helices	Implement the AgentBackend protocol

Start from a template

helix init my-project --template generic --domain "AI/ML" --description "Optimize X for task Y."
cd my-project
git init
helix run

Run an existing helix

# from within a helix directory (one that has helix.yaml)
helix run              # start a session tagged with today's date
helix run --tag exp1   # custom tag
helix status           # show current best and recent experiments

Templates

Template	Description
generic	Blank slate: solver.py + evaluate.py. Print score: <value> at the end.
ai-inference	LLM inference throughput on WikiText-2. Metrics: tokens_per_sec + bpb.

Examples

helix-examples is a curated gallery of standalone helices, each in its own repo and included as a git submodule.

git clone --recurse-submodules [e-mail]:VectorInstitute/helix-examples.git
cd helix-examples/inference-opt
uv run prepare.py   # one-time: download model + dataset
helix run

The first example, helix-inference-opt, optimizes inference throughput for a causal language model on WikiText-2. The agent modifies infer.py (batching, quantization, torch.compile, etc.) and automatically merges improvements back to main.

Writing your own helix

1. Create a new git repo.
2. Add helix.yaml describing your metric, evaluation command, and editable scope.
3. Add program.md with domain-specific instructions for the agent.
4. Add your codebase.
5. Run helix run.

Minimal helix.yaml:

name: my-helix
domain: AI/ML
description: Optimize X for task Y.

scope:
  editable: [solver.py]
  readonly: [evaluate.py, program.md, helix.yaml]

metrics:
  primary:
    name: accuracy
    optimize: maximize
  evaluate:
    command: python evaluate.py
    timeout_seconds: 120
    output_format: pattern
    patterns:
      primary: '^accuracy:\s+([\d.]+)'