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Isu - Structured Pseudocode for LLMs (in the Sui project)

This repository is shifting its primary focus from the original line-based Sui language to Isu, a structured pseudocode designed for deterministic parsing and step-level repair loops.

日本語版 README

Status

  • Isu (new): implemented as a minimal v0 pipeline (isu/) — Isu text ⇄ IIR (JSON-compatible AST) ⇄ canonical Isu.
  • Sui (legacy): moved to sui_legacy/ and kept behind compatibility wrappers (sui.py, sui2py.py, etc.).

What is Isu?

Isu is the primary artifact an LLM reads/writes. It is parsed deterministically into IIR (Isu Intermediate Representation), a closed-vocabulary AST designed for:

  • deterministic parsing and canonicalization (AUTO_ID, fixed shapes)
  • step-level error localization and patch workflows
  • backend independence (Python/Wasm/LLVM IR planned; not implemented yet)

Design docs:

  • isu/implementation-plan_en.md
  • isu/implementation-plan_ja.md

Quick start (from source)

Isu is currently a repository-local Python package. Run it from the repo root:

python -c 'from isu import parse_isu, pretty_print_isu; \
src = """META:\n  AUTO_ID: true\nFUNC:\n  NAME: f\nIO:\n  INPUT: []\n  OUTPUT: []\nSTATE:\n  []\nLOCAL:\n  []\nSTEPS:\n  SEQ: BEGIN\n    ASSIGN:\n      TARGET: x\n      EXPR: (const 1)\n    RETURN:\n      EXPR: (var x)\n  END\n"""; \
p = parse_isu(src); print(pretty_print_isu(p))'

Repository layout (high-level)

isu/          # Isu v0 pipeline (parser/canonicalizer/pretty-printer/validator)
tests/        # pytest (includes Isu v0 round-trip tests)
sui_legacy/   # legacy Sui implementation (interpreter/transpilers/wasm tools)
sui.py        # compatibility wrapper -> sui_legacy.sui

Legacy Sui (kept for now)

The original Sui language/tooling remains available:

  • Playground: playground/index.html
  • Examples: examples/
  • Tools: sui.py, sui2py.py, py2sui.py, sui2wasm.py, suiwasm.py (wrappers)

It is explicitly treated as legacy, and new work should target Isu/IIR.

License

MIT License

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粋 (Sui) - A programming language optimized for LLM code generation

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