Betafish.js
RepositoryFreeAI-enhanced chess analysis with customizable thinking times and FEN...
Capabilities8 decomposed
fen-based chess position import and export
Medium confidenceParses Forsyth-Edwards Notation (FEN) strings to reconstruct complete chess board states including piece placement, active player, castling rights, en passant targets, and move counters. Enables bidirectional conversion between FEN format and internal board representation, allowing users to load specific positions from games or export analyzed positions for external use. Implements standard FEN parsing with validation of piece placement, turn indicators, and special move flags.
Implements bidirectional FEN conversion as a core input mechanism rather than relying solely on move-by-move board construction, enabling direct position analysis without game replay overhead
Faster position loading than move-replay-based systems because it reconstructs board state directly from FEN rather than executing move sequences
configurable search depth chess engine analysis
Medium confidenceExecutes minimax-based chess position evaluation with adjustable search depth (thinking time) to balance analysis quality against computation latency. Implements alpha-beta pruning to reduce the game tree search space, allowing users to control the trade-off between deeper analysis and faster results. The thinking time parameter directly maps to search depth, enabling users to analyze positions in seconds (shallow) or minutes (deep) depending on device capability and analysis requirements.
Exposes search depth as a user-configurable parameter (thinking time) rather than fixed engine strength, allowing real-time adjustment of analysis depth without restarting the engine or changing engine versions
More flexible than fixed-strength engines (like Stockfish levels 1-20) because users can dial in exact thinking time for their device, whereas alternatives require discrete strength selection
real-time position evaluation scoring
Medium confidenceComputes numeric evaluation scores (in centipawns) for chess positions using a heuristic evaluation function that assesses material balance, piece positioning, pawn structure, and king safety. Returns evaluation from the perspective of the side to move, with positive scores indicating advantage for the moving player and negative scores indicating disadvantage. Updates evaluation dynamically as the engine searches deeper, allowing users to observe how the assessment changes with additional computation.
Provides incremental evaluation updates as search depth increases, allowing users to observe evaluation convergence and understand position complexity through score stability
More transparent than black-box engines because users can see how evaluation changes with thinking time, whereas commercial engines often hide intermediate evaluations
best move recommendation with principal variation
Medium confidenceIdentifies the strongest move in a position by selecting the move with the highest evaluation score from the minimax search tree, and returns the principal variation (PV) — the sequence of best moves both sides would play in response. Implements move ordering heuristics (killer moves, history heuristics) to prioritize promising moves early in the search, improving alpha-beta pruning efficiency. Returns both the recommended move in algebraic notation and the full line of play that justifies the recommendation.
Returns principal variation alongside the best move, providing context for the recommendation rather than isolated move suggestions, enabling users to understand the engine's reasoning
More educational than engines that only show the best move because the PV reveals the expected continuation and helps players understand positional consequences
interactive board state manipulation and visualization
Medium confidenceProvides a graphical chess board interface that allows users to place pieces, set up custom positions, and visualize the current board state with piece symbols and square highlighting. Implements click-based piece movement with validation to ensure moves are legal (no moving opponent pieces, respecting piece movement rules). Updates the visual board representation in real-time as positions change, and maintains internal board state synchronized with the displayed board.
Implements real-time board state synchronization between visual representation and internal game logic, ensuring UI always reflects the current position without manual refresh
More intuitive for non-technical users than notation-based input because visual board interaction requires no knowledge of algebraic notation
browser-based local computation without cloud dependency
Medium confidenceExecutes all chess engine analysis entirely within the browser using JavaScript, eliminating the need for external API calls or cloud servers. The engine runs as client-side code, processing positions and computing evaluations on the user's device without transmitting position data to remote servers. This architecture ensures privacy (positions never leave the device), offline functionality (analysis works without internet), and zero latency for engine communication (no network round-trips).
Prioritizes privacy and offline functionality by design, running the entire engine locally rather than as a cloud service, eliminating data transmission and external dependencies
More private and offline-capable than cloud-based engines like Lichess or Chess.com because positions never leave the user's device, but slower than cloud engines due to local CPU constraints
move legality validation and piece movement rules enforcement
Medium confidenceValidates that moves conform to chess rules by checking piece movement patterns (pawns move forward one square or two from starting position, knights move in L-shape, bishops move diagonally, rooks move horizontally/vertically, queens move any direction, kings move one square). Prevents illegal moves such as moving into check, capturing your own pieces, or moving opponent pieces. Implements special move handling for castling (king and rook movement with position requirements), en passant (pawn capture of enemy pawn that just moved two squares), and pawn promotion (automatic or user-selected piece).
Implements comprehensive chess rule validation including special moves (castling, en passant, promotion) as core constraints rather than optional features, ensuring all moves conform to official chess rules
More robust than simple piece-movement checking because it validates the full chess rule set including check detection and special moves, preventing invalid positions
game state persistence and move history tracking
Medium confidenceMaintains a complete record of moves played during a game session, allowing users to navigate backward and forward through the move history to review the game progression. Stores each position state and the move that led to it, enabling undo/redo functionality and position replay. Implements move history as a linear sequence (no branching variations), allowing users to step through the game move-by-move or jump to specific positions.
Tracks complete move history with position snapshots, enabling efficient backward navigation without recomputing positions from the start of the game
More efficient than recomputing positions from the initial state because it stores position snapshots, enabling O(1) navigation to any position in the game
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Chess students and coaches analyzing specific positions from their games
- ✓Players integrating analysis into study workflows across multiple platforms
- ✓Developers building chess analysis pipelines that need position interchange
- ✓Individual chess players studying positions on consumer hardware
- ✓Students who need flexible analysis depth without cloud dependencies
- ✓Players with varying device capabilities (mobile, laptop, desktop)
- ✓Chess players learning position evaluation and material counting
- ✓Students analyzing whether their moves improved or worsened their position
Known Limitations
- ⚠FEN format is position-only — does not preserve game history or move sequence
- ⚠No validation of position legality (e.g., impossible pawn placements or multiple kings)
- ⚠Cannot reconstruct full game context from FEN alone, only current board state
- ⚠Browser-based computation means analysis speed is bounded by client CPU, not cloud resources — deep analysis on complex positions may take minutes
- ⚠Alpha-beta pruning effectiveness depends on move ordering heuristics; poor heuristics reduce pruning efficiency
- ⚠No distributed computing or GPU acceleration — single-threaded JavaScript execution limits practical search depth to ~8-10 plies on typical hardware
Requirements
Input / Output
UnfragileRank
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About
AI-enhanced chess analysis with customizable thinking times and FEN support.
Unfragile Review
Betafish.js is a solid web-based chess analysis tool that leverages AI to evaluate positions with adjustable thinking times, making it accessible for players who want deeper insights without expensive subscriptions. The FEN support and customizable analysis depth mean serious players can integrate it into their study routine, though it lacks the polished UI and comprehensive features of Lichess or Chess.com's built-in engines.
Pros
- +Completely free with no paywalls or account requirements
- +Customizable thinking time lets you balance analysis depth against computation speed
- +Native FEN import/export makes it easy to analyze specific positions from your games
Cons
- -Bare-bones interface feels dated compared to modern chess platforms
- -Limited documentation and minimal community presence make troubleshooting difficult
- -Browser-based computation means analysis speed depends on your device rather than cloud power
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