snake-python/README.md

Battlesnake Python Starter Project

An official Battlesnake template written in Python. Get started at play.battlesnake.com.

This project is a great starting point for anyone wanting to program their first Battlesnake in Python. It can be run locally or easily deployed to a cloud provider of your choosing. See the Battlesnake API Docs for more detail.

Technologies Used

This project requires Python 3.15+ and uses Quart with Hypercorn. The Dockerfile uses the uv Python 3.15 runtime.

Run Your Battlesnake

Install the pinned Python runtime and locked dependencies using uv:

uv python install 3.15
uv sync --locked

Python modernization (2026-11-03):

• Python 3.15 lazy imports defer database backend loading and benchmark-only dependencies. Optional-dependency try/except imports keep their fallback behavior.
• Python 3.14 deferred annotations replace from __future__ import annotations. Type-only dependencies use lazy bindings to avoid loading circular imports at startup.
• Python 3.14 Path.info checks reuse directory-entry metadata during dataset and customization asset discovery. Paths reused after mutations retain fresh checks.
• The shared quart_common submodule also requires Python 3.15 and already uses Python 3.14 UUIDv7 request IDs.

Run tests with uv run --locked pytest tests quart_common/tests/test_user_agent.py. Python 3.14 is not a supported runtime: this project uses 3.15-only syntax.

Start your Battlesnake

uv run --locked python main.py

You should see the following output once it is running

Running your Battlesnake at http://0.0.0.0:8000
 * Serving Flask app 'My Battlesnake'
 * Debug mode: off

Open localhost:8000 in your browser and you should see

{"apiversion":"1","author":"","color":"#888888","head":"default","tail":"default"}

Play a Game Locally

Install the Battlesnake CLI

• You can download compiled binaries here
• or install as a go package (requires Go 1.18 or higher)

Command to run a local game

battlesnake play -W 11 -H 11 --name 'Python Starter Project' --url http://localhost:8000 -g solo --browser

Next Steps

Continue with the Battlesnake Quickstart Guide to customize and improve your Battlesnake's behavior.

Included Competitive Snake

This repo retains snakes/legacy/BestBattleSnake.py, a stronger historical snake that combines:

• collision and head-to-head risk checks
• flood-fill space evaluation to avoid traps
• food routing that gets more aggressive as health drops
• tail access checks for better long-term survival

Run it explicitly with:

SNAKE=BestBattleSnake python main.py

Optional duel tuning (when only 2 snakes are alive):

BATTLE_SNAKE_DUEL_STYLE=balanced python main.py

Allowed values: safe, balanced, aggressive.

Snake package layout

The snake code is split by responsibility:

• snakes/strategies/ — actively maintained Apex and Prism entry points
• snakes/engine/ — reusable bitboards, spatial mixins, duel search, and survival search
• snakes/core/ — shared base classes
• snakes/legacy/ — historical snakes retained for compatibility and benchmarks

Snake selection still uses the existing registry names, so deployment values such as SNAKE=PrismBattleSnake_GPT_5_6_Sol remain unchanged.

PrismBattleSnake_GPT_5_6_Sol

PrismBattleSnake_GPT_5_6_Sol is a separate snake that keeps Apex's strategy while accelerating hot spatial operations with a Python-integer bitboard engine. It also shares duel transpositions across candidate moves, uses principal-variation ordering, aspiration windows, path-aware food races, and a deeper tactical horizon, and runs a compact adversarial multiplayer rollout with simultaneous enemy responses and cached occupancy/evaluation states. Its filename, class, and registry key include the model name, while its public Battlesnake API name remains PrismBattleSnake.

Run it with:

SNAKE=PrismBattleSnake_GPT_5_6_Sol python main.py

Benchmark Apex and Prism against sampled positions from a gameplay database:

python scripts/benchmark_snakes_from_db.py \
  --database /path/to/gameplay.sqlite3 \
  --samples 100

The benchmark samples every --stride-th stored turn from the newest games (SQLite is opened read-only; a postgresql:// DSN works too) and reports mean, median, p95, and maximum move latency. Increase --samples for a broader but slower comparison.

Run the deterministic CI-friendly arena benchmark without a gameplay database:

just bench-snake-arena positions=100

It rotates through duel, hazard, multiplayer, constrictor, and cramped-endgame positions. It reports latency, completed duel/rollout depth, searched nodes, cache hits, deadline exits, and move disagreements between Apex and Prism. Use --scenario hazard (repeatable) when invoking the Python script to isolate a scenario. Add output=data/arena-report.json to save a machine-readable report. For representative strategy evaluation, provide recorded positions to scripts/benchmark_snake_arena.py --database /path/to/gameplay.sqlite3.

Run paired seeded games through the official local Battlesnake rules engine:

just bench-snake-tournament games=20 gametype=standard map=standard

Each seed is played twice with Apex and Prism swapping initial engine slots. The report includes wins, draws, win rates, and average game length. Save all per-game results with output=data/tournament-report.json. The tournament starts both snake servers with gameplay persistence disabled, adds the engine identity header required by the API, and shuts them down when finished.

Compact gameplay database

Gameplay storage keeps game results in games and snake identity (name, customizations) once per game in game_snakes. Replay data is stored compactly:

• Running games write one game_turns row per turn and one snake_states row per snake and turn, keyed by small integer ids (games.gid, game_snakes.gsid) instead of repeating text ids. Moves are 1-byte codes, latency is an integer, bodies are a head position plus one step code per segment, and food/hazards are only stored when they changed since the previous turn. Reasoning fields that equal stored values (turn, move, head, health, length, food_count, board_size, snakes) are stripped and restored exactly on read.
• Finished games are packed into one zstd-compressed blob per game in game_replays, and their rows are deleted. Turns arriving after a game was packed are merged into the blob by the stale-game sweep.

Replay loading rebuilds the normal Battlesnake board payload, so the dashboard is unchanged. Head position, length, and inferred moves are derived from the stored bodies.

Migrate the legacy layout

Databases written by older versions store replays in turns/snake_turns. Migrate them in place (SQLite path or postgresql:// DSN). Every game is read back and compared after writing; failed games are reported and keep their legacy rows:

python scripts/migrate_gameplay_to_compact.py --source /path/to/gameplay.sqlite3
python scripts/migrate_gameplay_to_compact.py --source /path/to/gameplay.sqlite3 --drop-legacy

--destination writes to a different database instead, for example from SQLite into PostgreSQL. --minimum-quality medium re-rates replays by structural completeness, valid moves, thinking coverage, game length, move diversity, opponent data, and terminal outcome, and keeps only replays of that tier or better. All game-result rows remain stored, so historical win/loss rates stay persistent. --drop-legacy only drops the old tables when every legacy replay exists in compact form.

For a live PostgreSQL database: run the migration while the old version keeps recording, deploy the new version, run the migration again with --since <start time of the first run> to pick up games recorded meanwhile, then run --drop-legacy. A PostgreSQL server configured with a SQLite GAMEPLAY_DB_PATH imports that legacy file automatically on startup.

Record new games while cleanup runs

Point the running server at a temporary delta database while the old database is being migrated or cleaned. After stopping the writer and flushing the delta database, merge it into the cleaned copy:

python scripts/merge_gameplay_databases.py \
  --base /path/to/gameplay.compact.sqlite3 \
  --delta /path/to/gameplay.delta.sqlite3 \
  --destination /path/to/gameplay.merged.sqlite3 \
  --minimum-quality medium

The merger copies game by game, keeps all game results, quality-rates delta games, assigns fresh integer ids, and verifies game, snake, turn, and snake-state counts. Identical game IDs are skipped; conflicting duplicates abort the merge. After reviewing the result, --replace-base backs up and replaces the cleaned base. Stop the delta writer before the final merge and file swap.

Export Training Dataset

Game saves now include a dataset section with labeled move samples.

Export all stored samples to JSONL:

python -m server.DatasetExporter --input data --output data/dataset/good_moves.jsonl

Or with just:

just export-dataset

Curate a high-quality training subset (single file):

python -m server.DatasetCurator --input good_moves-2026-04-03.jsonl --output data/dataset/best_moves.jsonl

Curate from multiple JSONL sources (repeat --input):

python -m server.DatasetCurator \
  --input good_moves-2026-04-03.jsonl \
  --input good_moves-2026-04-04.jsonl \
  --output data/dataset/best_moves.jsonl

Curate from folder or glob:

python -m server.DatasetCurator --input data/dataset --output data/dataset/best_moves.jsonl
python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl

Append mode (keeps existing curated rows and deduplicates against them):

python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl --append

Archive processed input files after curation:

python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl --append --archive-input
python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl --append --archive-input --archive-dir data/dataset/archive

Or with just:

just curate-dataset
just curate-dataset append=true
just curate-dataset append=true archive=true archive_dir=data/dataset/archive

Analyze dataset quality overall and by day (best game overall/day included):

python -m server.DatasetStats --input "good_moves-*.jsonl"
python -m server.DatasetStats --input data/dataset --output data/dataset/stats-report.json

The stats report now includes both:

• best_game (survival/length focused)
• best_pressure_game (high-pressure quality focused: fewer safe options + strong survival)

Or with just:

just analyze-dataset
just analyze-dataset input=data/dataset output=data/dataset/stats-report.json

To store compact dataset-only records (JSONL) and skip full per-game JSON files:

STORE_DATASET_ONLY=true DATASET_JSONL_PATH=data/dataset/good_moves.jsonl python main.py

Optional compact storage tuning:

• DATASET_ROTATE_DAILY=true creates one JSONL file per day (default: true)
• DATASET_JSONL_MAX_MB=50 rotates when file reaches max size in MB (default: 50)
• DATASET_COMPRESS_ROTATED=true gzip-compresses rotated/old JSONL files (default: true)

Note: To play games on play.battlesnake.com you'll need to deploy your Battlesnake to a live web server OR use a port forwarding tool like ngrok to access your server locally.