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AI-native programming language for deterministic, inspectable applications.

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Project description

namel3ss

AI-native programming language for deterministic, inspectable applications. Deterministic execution with explicit AI boundaries and governed runtime.

namel3ss: one .ai file defining data, flows, explicit AI, and deterministic UI

One .ai file defines records, deterministic flows, explicit AI boundaries, and UI — with inspectable state changes.

license: MIT tests


Start here

Try it in 60 seconds: docs/quickstart.md.

What you can run today

RAG support (deterministic)

namel3ss supports production-grade retrieval-augmented generation as a first-class pattern:

  • Deterministic ingestion and retrieval
  • Answering with mandatory citations
  • PDF page preview and exact source highlighting
  • Explain mode for auditable selection
  • Optional embeddings (runtime config only, no grammar changes)

See the canonical overview: docs/rag/overview.md

Core stable

Core stable is the graduation gate for the language surface, compiler determinism, and safety guards. See the authoritative definition in docs/graduation/core_stable.md.

Get started

Develop from source

Use this for development and testing.

python3 -m venv .venv && . .venv/bin/activate
python3 -m pip install --upgrade pip && python3 -m pip install -e ".[dev]"
python3 -m namel3ss --help

Run with Docker (isolated runtime)

Use this for a clean, repeatable runtime.

docker build -t namel3ss:local .
docker run --rm namel3ss:local n3 --help

Learn / explore (docs & templates)

Use the local Docker image to open Studio for a bundled demo.

docker run -d --name namel3ss_studio -p 7340:7340 \
  -v "$PWD:/workspace" -w /workspace \
  namel3ss:local n3 app/app.ai studio --host 0.0.0.0 --port 7340
docker logs namel3ss_studio
docker rm -f namel3ss_studio

See docs/install-and-run.md for complete install and Studio instructions.

Installation (summary)

Supported paths:

  • Install from source (development)
  • Docker (local) for an isolated runtime

Full guide: docs/install-and-run.md

Docker quick check:

docker build -t namel3ss:local .
docker run --rm namel3ss:local n3 --help

Install + Verify

Install (source, editable):

python3 -m pip install --upgrade pip
python3 -m pip install -e ".[dev]"

Verify CLI and package:

n3 --version
n3 doc
python3 tools/package_verify.py

Verify embedding (C example, requires C toolchain + cargo):

python3 -m pytest -q tests/embed/test_embed_c_example.py

Browser Protocol

Browser Protocol is defined in docs/runtime/browser-protocol.md.

Docker & CI guarantees

  • Docker builds install from local source, not PyPI.
  • Docker builds are validated in CI.
  • CLI smoke tests are enforced automatically.
  • Wheel installs are smoke-tested in release automation.

References:

Release & governance

Release invariants (enforced):

  • VERSION is metadata.
  • Docker builds do not depend on PyPI.
  • Publish is gated by CI and guards.
  • Canonical sequence: VERSION bump → tests → tag → PyPI publish → docker image → release notes.

References:

How it works (high level)

Design guarantees:

  • Deterministic execution
  • Explicit AI boundary
  • Inspectable state and traces
  • Governed memory

Runtime guarantees:

  • Stable CLI surface
  • Deterministic manifests and outputs
  • Read-only diagnostics and explain output
  • Safe cleanup of runtime artifacts

See docs/trust-and-governance.md.

UX as a Contract

UX behavior is deterministic and explainable through stable manifests and n3 see output.

Guarantees:

  • Uploads (progress, preview metadata, async errors)
  • Conditional UI (state-gated visibility)
  • Reusable UI patterns (compile-time expansion)
  • Accessibility by default (roles, labels, keyboard, contrast)

Detailed UX contracts: docs/ui/overview.md

Language Contracts

Language Contracts are defined here:

Reserved identifiers

If you must use reserved identifiers, escape them with backticks and use n3 reserved to list them. The reserved words list is in docs/language/reserved-words.md.

Documentation index

Getting started:

Language & grammar:

Runtime & backend:

UI & Studio:

Governance & releases:

Discussions & Design Conversations

GitHub Discussions is the canonical place for architectural and language design conversations. Use Discussions for design questions, trade-offs, and long-form proposals; use Issues for bugs and actionable feature requests.

Reference discussion: https://github.com/namel3ss-Ai/namel3ss/discussions/2

Contributing

See CONTRIBUTING.md.

Summary:

  • Clone the repo.
  • Create and activate a virtual environment.
  • Install editable + run tests (python3 -m pytest -q).

Troubleshooting

Project details


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