currentai-org/os-ai-map

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The open source AI stack is already strong, but it’s fragmented, duplicative, and hard to see as a whole. The Open Source AI Gap Map makes it legible: a living map of what exists across the stack, how open each piece is, how widely it’s used, and where the gaps are — so the community can see where to build, where to invest, and where to open things up.

This repo is the data behind that map. Everything is curated YAML in sources/; a deterministic pipeline validates it, serializes it to build/notebook_data.json, and renders the published map.

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Contribute

The map is a public, iterative effort, and community curation is the point. There are two ways in:

1. File an issue (no code needed): suggest a product, report an error, or propose a category.

2. Open a pull request editing sources/. Adding a product is a handful of small YAML files: a product record, a score with citations, and an entry in one category roster and one organization roster. CONTRIBUTING.md has the full recipe and the scoring rubric.

A few ground rules:

• Every score cites a primary source. The map excludes anything it can’t verify against one.
• Don’t hand-edit generated files. build/notebook_data.json and notebooks/ai-stack-map.py are regenerated by a bot on merge; PRs that touch them are blocked.
• CI runs on every PR (build.validate + pytest), so you’ll know quickly if something is off.

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How scoring works

Each product is graded on three independent, multi-source axes:

• Openness — a 0–5 grade against openness frameworks (the Model Openness Framework for models, OSI license classes for software), not a yes/no. The open-source vs. open-weights distinction is the one the map exists to draw.
• Adoption — real usage (downloads, active users, deployments), not GitHub stars.
• Capability — community benchmarks where they exist, feature coverage where they don’t.

Adoption and capability blend into a single overall score per product, banded as Leading (score ≥ 4.5) or Strong (4.0 ≤ score < 4.5). Categories then roll up from their products into a Maturity Stage (0 Void → 5 Mature) plus a set of gaps naming what the open ecosystem still lacks. The taxonomy and openness framework descend from the 2024 Columbia Convening on Openness in AI.

The complete method — the stage and gap formulas, per-axis sources, and stated limitations — is in docs/methodology.md, the hand-authored source of truth (also rendered on the site).

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What’s in sources/

One YAML file per record: four concerns plus the single sources/taxonomy.yaml manifest. This is what you edit.

Category slugs use underscore form (base_pretrained); product and org slugs use hyphenated kebab-case (llama-3-1, allen-ai). Artifact keys on products (only those that apply): github, npm, pypi, crates, go, huggingface_model, huggingface_dataset. JSON Schemas for every file type live in docs/schemas/.

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Run it locally

Requires Python 3.12+ and uv. No API key is needed to edit sources or validate.

uv sync
uv run python -m build.validate    # schema + cross-file checks; must print "0 error(s)"
uv run pytest -q                   # optional; the same suite CI runs

To preview the generated map locally (the output is for preview only — don’t commit it):

uv run python -m build.serialize                                          # sources/ → build/notebook_data.json
uv run python -m build.render                                             # → notebooks/ai-stack-map.py
uv run marimo export html notebooks/ai-stack-map.py -o /tmp/preview.html

Warehouse queries (via pyoso) need OSO_API_KEY; with direnv, place it in .env and it loads automatically.

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Repository layout

Guides worth knowing: openness scoring, adoption scoring, gap analysis, and query conventions. See AGENTS.md for agent-oriented project context, the full directory map, and maintainer operations. How the data assets fit together — namespaces, gates, and the repository boundary — is in docs/architecture/.

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Code and data are MIT licensed.