ASTRA Specification

Agentic Schema for Transparent Research Analysis — a declarative YAML format for scientific analyses that separates what you want to learn from how to compute it.

What is ASTRA?

ASTRA is a structured format that captures every consequential choice in a scientific analysis — its inputs, outputs, decisions, and the evidence behind them — so the result is reproducible, auditable, and composable. An agent, a workflow runner, a notebook, or a human reads the spec and produces the results; ASTRA itself stays out of execution.

For the longer argument that motivates the project — per-result trust in the agentic-AI era and why an open substrate matters — see the Lightcone Research position paper.

Quick example

# astra.yaml
version: "1.0"
name: Iris Classification

inputs:
  - id: iris_data
    type: data
    source: sklearn.datasets.load_iris

outputs:
  - id: accuracy
    type: metric
    format: json
    decisions: [scaling, model]
    recipe:
      command: python src/evaluate.py

decisions:
  scaling:
    label: Feature Scaling
    default: standard
    options:
      none:     { label: No Scaling }
      standard: { label: StandardScaler }

  model:
    label: Classification Model
    default: random_forest
    options:
      random_forest: { label: Random Forest }
      svm:
        label: SVM
        requires: [scaling.standard]

A universe picks one option per decision (e.g. { scaling: standard, model: svm }). The same analysis can carry many universes; each one yields one set of results.

Install and try it

uv tool install astra-tools           # installs the `astra` CLI on your PATH
astra init my-analysis && cd my-analysis
astra validate astra.yaml
astra info
astra viz

Full walk-through: Getting started →

Repository layout

This repository (astra-spec) holds the LinkML schema and the documentation site.

src/astra/
  schema/       # LinkML schema source (edit these)
  datamodel/    # Generated Python datamodel (do not edit directly)
docs/           # Documentation source (Zensical → astra-spec.org)
tests/data/    # Valid/invalid YAML fixtures
project/        # Multi-language bindings (Python, TS, JSON Schema, JSON-LD, OWL, Java)

The Python CLI and SDK (astra-tools) live in a separate repository: LightconeResearch/astra-tools. Both packages share the astra.* Python namespace via PEP 420.

Development

git clone https://github.com/LightconeResearch/astra-spec.git
cd astra-spec
uv sync --dev

Requires Python 3.9+, uv, and just.

just test         # Schema generation, Python datamodel, examples
just lint         # LinkML schema lint
just gen-python   # Regenerate Python datamodels
just gen-doc      # Regenerate the schema reference docs
just docs-serve   # Live-reload local preview of the docs site
just --list       # Everything else

The release recipe (just release X.Y.Z) updates CITATION.cff and creates an annotated tag; the version is injected into generated artifacts from the tag at build time. Pushing the tag triggers the Deploy versioned docs on tag workflow, which publishes the versioned docs to gh-pages via mike.

See CONTRIBUTING.md and the Community page for the full contribution workflow.

License

• Schema — CC BY 4.0
• Code — BSD 3-Clause

Both are permissive; commercial and academic use are unrestricted. Schema reuse requires attribution; code reuse requires the standard BSD-3 notice.

Citing

If you use ASTRA, please cite the repository — the easiest way is the "Cite this repository" button on this page (it generates APA and BibTeX from CITATION.cff). Include the schema version you targeted; it is recorded at the top of each LinkML source file and in every generated artefact.

For the broader context, the Lightcone Research position paper is a separate citation, not a replacement.

Credits

Developed by Lightcone Research. Built with LinkML using the linkml-project-copier template.