lucena-engine 0.1.2

Deterministic chess truth for grounding language models (board core + Stockfish + Maia)

lucena-engine

Deterministic chess truth for grounding language models.

lucena-engine turns a chess position into grounded facts — a piece list, a material count, a strategic read, tactical motifs, move evaluations, and a plain-language briefing — computed by Stockfish and a Rust board core, never guessed. It exists so an LLM can interpret chess instead of calculating it: you hand the model the facts, and it explains, teaches, or narrates — without hallucinating whose move it is, miscounting material, inventing a piece on f6, or imagining a fork that isn't there.

If you're building anything where an LLM talks about chess positions — a coach, an annotator, a commentary bot, a puzzle explainer — this is the layer that keeps it honest.

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Why

LLMs are fluent about chess and quietly wrong. Ask one "what's going on here?" with a FEN and it will confidently misread the side to move, hang a piece in analysis, or narrate a tactic from a different game. The fix isn't a bigger prompt — it's grounding: compute everything the model could get wrong, hand it over as facts, and instruct it to reason only from those.

lucena-engine is the grounding source. It emits two shapes of truth:

• Structured facts — typed records with squares, scores, and provenance (for your own logic, board arrows, or tool-calling).
• A natural-language fact sheet — a short list of deterministic English statements you can drop straight into a prompt.

Every output is reproducible given the engine limit. No randomness in the facts.

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Install

pip install lucena-engine        # library + `lucena-engine` CLI
brew install stockfish           # required engine (or apt-get install stockfish)

lucena-engine doctor             # check the runtime is wired up
lucena-engine analyze "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"

Optional extras: pip install 'lucena-engine[grpc]' for the server; human-move prediction (Maia) is opt-in via lucena-engine setup-maia — see Human-move prediction.

From source (development): needs a Rust toolchain.

pip install maturin && maturin develop --release   # builds the Rust board core
export LUCENA_STOCKFISH=$(which stockfish)          # defaults to `stockfish` on PATH

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Quickstart — a grounded fact sheet in five lines

from lucena_engine import Engine
from lucena_engine.board import Board
from lucena_engine.analysis import build_analysis

engine = Engine()                                          # spawns Stockfish
board  = Board("r1bqkb1r/pppp1ppp/2n2n2/4p3/2B1P3/2N2N2/PPPP1PPP/R1B1K2R b KQkq - 0 4")

for line in build_analysis(board, engine, movetime_ms=500):
    print(line)

The position is roughly equal.
Material is equal.
White's pieces are more active than Black's.
The center is contested.
Tactics: the knight on f6 can capture on e4.

That list is the grounding. Put it in a prompt:

facts = build_analysis(board, engine, movetime_ms=500)
prompt = (
    "You are a chess coach. Ground every claim ONLY in these engine facts — "
    "never invent a piece, square, line, or number:\n" + "\n".join(facts) +
    f"\n\nStudent asked: what should I be thinking about here?"
)
# → hand `prompt` to your LLM. It interprets; it does not calculate.

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What's in the fact sheet

build_analysis composes, in order:

1. Evaluation — translated to words ("roughly equal", "White is clearly better"); never raw centipawns or win%.
2. Material — a standing ("Black is up a pawn").
3. Strategic read — king safety, piece activity, pawns, and center, leads first.
4. Tactics — the salient motifs on the board, joined into one line.

Each piece is also available on its own if you'd rather build your own briefing:

from lucena_engine import build_fact_sheet
from lucena_engine.analysis import analyze_positional
from lucena_engine.reads import material, piece_list, eval_block, pv_san

analysis = engine.analyse(board.fen, multipv=2, movetime_ms=500)
material(board)                    # {"white": 10, "black": 10, "net": 0, "standing": "Material is equal"}
eval_block(analysis.best.score)    # {"cp": 12, "win_pct": 52.1, ...}
pv_san(board.fen, analysis.best.pv)  # ["Nxe4", "d5", "Bd3", ...]  (UCI → SAN)
analyze_positional(board)          # the five strategic terms, each with a "standing"

---

Structured facts (typed, with squares)

For tool-calling, board arrows, or your own routing, take the facts as records instead of prose:

from lucena_engine import build_fact_sheet

for f in build_fact_sheet(board, engine, movetime_ms=500):
    print(f.kind, f.squares, f.salience, "—", f.text)
# hanging   ['e4']   0.9  — the pawn on e4 is undefended
# fork      ['c7']   0.8  — a knight on c7 would fork the king and rook

Fact = kind, squares, text, provenance, salience (0..1). The individual detectors are public too:

from lucena_engine import (
    detect_hanging, detect_fork, detect_pin, detect_defender_removed,
    detect_combination, detect_null_move_threat,
)

---

The board core

A fast Rust board (cozy-chess + a custom SAN encoder and SEE) with a small Python surface — legal moves, SAN ⇄ UCI, static exchange evaluation, attackers/defenders, apply:

from lucena_engine.board import Board

b = Board(fen)
b.side_to_move        # "white" | "black"
b.legal_moves()       # ["e2e4", "g1f3", ...] (UCI)
b.san("f6e4")         # "Nxe4"
b.uci("Nxe4")         # "f6e4"
b.see("f6e4")         # static exchange eval (centipawns) — is this capture safe?
b.attackers("e4", "white")   # squares of white pieces hitting e4
b.apply("f6e4")       # → a new Board after the move (immutable)

Nothing here calls the engine — it's pure, instant board logic.

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Move evaluation, hints, and the rest

# Best move + a worded verdict for a candidate
from lucena_engine import classify, Glyph
before = engine.analyse(fen, multipv=2, movetime_ms=500)
after  = engine.analyse(board.apply("f6e4").fen, movetime_ms=500)
glyph, _ = classify(before.best.score, after.best.score.negated())   # OK / dubious / mistake / blunder …

# A ladder of hints, vague → specific, none of which is the answer
from lucena_engine import derive_hints
# Brilliancy / sacrifice detection
from lucena_engine import is_brilliant, is_material_sacrifice
# Whole-game annotation
from lucena_engine import parse_pgn, run_pass
game = parse_pgn(pgn_text)          # Game(plies=[Ply(san, uci, fen), ...])

---

Human-move prediction (Maia)

Optional. Predicts what a human of a given rating would actually play (not the engine's best) — useful for "this is a common trap at your level" style grounding. Runs as its own subprocess.

from lucena_engine.maia import MaiaEngine   # requires LUCENA_MAIA to point at a maia UCI wrapper

maia = MaiaEngine()
maia.top_human_moves(fen, rating=1500, n=5)   # [{"uci": "f6e4", "rank": 1, "policy": 0.42}, ...]

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Over the wire (gRPC)

Prefer a language-agnostic or networked boundary? The same truth is exposed as a gRPC service — handy if your app isn't Python, or if you want the engine (and its GPL dependencies) in a separate process.

python -m lucena_engine.server.serve          # listens on :50051 (LUCENA_ENGINE_PORT)

Two services (proto in proto/lucena/engine/v1/engine.proto, SAN on the wire):

• Truth — Analyze (FEN → fact sheet), Evaluate, Hints, LegalMoves, Apply, ValidateFen, DetectFens, ParsePgn, GetInfo.
• Behaviour — TopHumanMoves, PoisonedLine (Maia-backed; optional).

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Design principles

• Calculate vs. interpret. The engine and board calculate the truth; your LLM only interprets it. Anything the model could get wrong is grounded (handed over) or guarded (made structurally impossible) — never left to the model.
• Deterministic. Same position + same engine limit → same facts. Production uses movetime_ms; tests use nodes with threads=1 for bit-reproducibility.
• SAN on the wire. UCI is engine-internal; everything you see and send is human-readable SAN.
• No eval leakage into prose. The natural-language layer says "you're winning", never "‑347 cp".
• Copyleft hygiene. GPL engines (Stockfish, Maia) are invoked only as subprocesses over UCI — arm's-length, never linked. A CI gate keeps GPL libraries out of the shipped code.

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Requirements

• Python ≥ 3.9, Rust toolchain (for the board core via maturin).
• Stockfish (18 recommended; the NNUE eval parser is version-aware). Point LUCENA_STOCKFISH at it.
• Optional: Maia (maia3) in its own environment, via LUCENA_MAIA.

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Contributing & license

Licensed under AGPL-3.0-or-later (see LICENSE, NOTICE, and THIRD-PARTY-NOTICES.md). If you run a modified version as a network service, AGPL §13 requires you to offer users its source.

Contributions are welcome under the CLA in CONTRIBUTING.md. Note the copyleft-hygiene rule: the shipped library never imports python-chess (GPL) — the board core replaces it, and a test enforces it.