Agent Engines Core — Chess AI Infrastructure - #1094
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chinweobtagaz merged 5 commits intoAug 26, 2026
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Implement deterministic guardrails that intercept and validate every chess move mention in LLM response text using python-chess. Includes: - Regex parsers for SAN, UCI, and piece-to-square coordinate notation - Legality validation against current board state - Best-effort correction of hallucinated moves - Prompt injection detection and blocking - Full pytest suite (24 tests) with <5ms per-response overhead 🤖 Generated with Codebuff Co-Authored-By: Codebuff <noreply@codebuff.com>
Implement async parallel engine dispatcher that queries Stockfish, LCZero, and Berserk concurrently. Includes: - Non-blocking concurrent engine execution with semaphore controls - Consensus score calculation (best-move agreement + eval delta) - Neural vs classical divergence detection (>100 cp delta flagging) - Crash resilience — individual engine failures don't abort the request - Process timeout controls per engine instance - Full pytest suite (16 tests) with mocked engine processes 🤖 Generated with Codebuff Co-Authored-By: Codebuff <noreply@codebuff.com>
…onalities Implement end-to-end PGN-to-adapter pipeline for player-customized chess AI companions. Includes: - PGN ingestion with move tokenization and vocabulary building - Lightweight LoRA model with low-rank adaptation matrices - Training loop with cross-entropy loss and gradient updates - Compact adapter weight saving/loading (< 20MB npz files) - Dynamic adapter loading for inference at game runtime - Full pytest suite (27 tests) covering vocab, dataset, model, and pipeline 🤖 Generated with Codebuff Co-Authored-By: Codebuff <noreply@codebuff.com>
Implement high-performance vectorised FEN-to-bitboard tensor converter and LRU evaluation cache for batch inference. Includes: - Direct FEN string parser bypassing python-chess Board (>100k FENs/s) - 8×8×14 binary tensor representation (12 piece + EP + side-to-move) - Vectorised board-to-tensor via piece_map for Board objects - LRU cache with hit-rate tracking and eviction for deduplication - BatchEvaluator that ties tokenizer + cache for concurrent games - Position hash ignoring move counters for equivalence - Full pytest suite (37 tests) including throughput benchmark 🤖 Generated with Codebuff Co-Authored-By: Codebuff <noreply@codebuff.com>
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Resolved 7 conflicted files: - __init__.py: updated imports for new modules (MoveGuardrail, EnsembleEvaluator, etc.) - batch.py, nl_intent_parser.py, pool.py, training_pipeline.py: kept our tested implementations - pyproject.toml: merged dependencies from both branches - tests/test_batch.py: kept our tested suite 🤖 Generated with Codebuff Co-Authored-By: Codebuff <noreply@codebuff.com>
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Summary
Implements four foundational modules for the chess AI engine system: move validation, multi-engine consensus, player personality training, and high-performance FEN tokenization.
Changes
TASK 1: MoveGuardrail (nl_intent_parser.py) — 24 tests
Closes #1071
Regex parsers for SAN, UCI, and piece-to-square notation
Legality validation via python-chess, best-effort hallucination correction
Prompt injection detection and blocking
TASK 2: EnsembleEvaluator (pool.py) — 16 tests
Closes #1072
Async parallel engine dispatcher for Stockfish, LCZero, Berserk
Consensus score + neural vs classical divergence detection
Crash resilience per-engine, timeout controls
TASK 3: LoRA Training Pipeline (training_pipeline.py) — 27 tests
Closes #1073
PGN ingestion → tokenization → LoRA fine-tuning → adapter save/load
Compact adapters (<20MB), dynamic loading at inference time
TASK 4: FEN Tokenizer & Tensor Cache (batch.py) — 37 tests
Closes #1074