wordlepro 0.2.0

Finds the optimal solution for a game of Wordle

WordlePro

An entropy-based Wordle solver that computes optimal guesses using information theory. Given the remaining possible answers, WordlePro selects whichever guess maximizes expected information gain — minimizing the number of guesses needed to solve the puzzle. Supports standard Wordle and multi-board variants like Quordle.

Features

• Optimal guessing — picks the guess with maximum expected entropy across all remaining candidates
• Multi-board support — solve Quordle and other N-board variants with --boards N
• Pattern matrix cache — precomputes the full guess × answer clue table once and caches it to disk; subsequent runs are instant
• Interactive solver — guides you through a live game step by step
• Benchmark mode — runs the solver against every answer word and reports guess-count statistics

Installation

Requires uv.

git clone https://github.com/nhaberla/WordlePro.git
cd WordlePro
uv sync

Usage

Interactive solver

uv run wordlepro solve

WordlePro suggests the best guess each turn. Enter the word you played, then enter the result as a string of digits — 0 (grey), 1 (yellow), 2 (green) — for each board.

Example for a single board:

Suggested guess: CRANE  (5.23 bits)
Enter your guess: crane
Enter results (b1): 01200

Multi-board (e.g. Quordle)

uv run wordlepro solve --boards 4

Results are entered space-separated, one per unsolved board:

Enter results (b1 b2 b3 b4): 00000 01100 22222 00120

Benchmark

uv run wordlepro benchmark

Runs the solver against every answer word and prints a distribution table. Use --json for machine-readable output.

uv run wordlepro benchmark --boards 4 --json

Options

Flag	Default	Description
--boards N	1	Number of simultaneous boards
--max-guesses N	6	Maximum guesses allowed
--answers-file PATH	bundled	Override the answer word list
--guesses-file PATH	bundled	Override the allowed guess list

How it works

1. Pattern matrix — for every (guess, answer) pair, encodes the Wordle clue as a base-3 integer (0 = grey, 1 = yellow, 2 = green). This matrix is computed once and cached as a .npy file via platformdirs.
2. Entropy calculation — for each candidate guess, computes the Shannon entropy of the clue distribution across remaining possible answers. Higher entropy → more information gained → fewer guesses expected.
3. Guess selection — picks the guess with the highest expected entropy. On multi-board games, entropies are summed across boards.
4. Filtering — after each guess, eliminates answers that are inconsistent with the observed clue pattern.

Development

uv run pytest              # run tests
uv run mypy --strict src/  # type checking

Author

Noah Haberland — [e-mail] · github.com/nhaberla