llm-poker 0.1.7

Texas Hold'em environment with LLM players

README.md

llm_poker

A minimal Texas Hold’em environment that seats multiple LLM-based players (via the llm library) and manages everything from dealing hole cards to forced blinds, betting rounds, and a straightforward showdown.

Core features:

• Blinds: Each hand forces a small blind and a big blind, ensuring there’s money in the pot.
• Betting: We query each LLM once per betting round, requesting an action in strict JSON form (fold, call, or raise).
• Local showdown logic: The environment determines the best 5-card hand from each player’s 7 cards and awards the pot.
• Pydantic-based JSON validation: The LLM responses are parsed and validated. If invalid, we retry.
• Optional CLI: The llm_poker command can run multiple rounds using the specified LLMs.

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Installation

1. Install the package:

   pip install llm_poker
   

You must also configure your llm library with the API keys for whichever LLM models you plan to use (e.g., gpt-4o, Anthropic, etc.). For example:

llm keys set openai

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Quickstart Examples

1. Running the Sample run.py

python run.py

Deals up to 5 rounds between multiple players: gpt-4o, claude-3-5-haiku-latest, claude-3-5-sonnet-latest, deepseek-chat. Uses elimination_count=0 so the game does not stop early (unless someone busts). The minimum raise is 500 chips. Logs each hand’s actions, culminating in a final standings table.

2. Using the CLI If you installed with the included console script, you can do:

llm_poker --models gpt-4o --models claude-3-5-haiku-latest --rounds 5

This deals 5 rounds of heads-up between gpt-4o and claude-3-5-haiku-latest.

Once installed, you have access to:

llm_poker [OPTIONS]

--models/-m: Multiple model names or aliases recognized by llm (defaults to ["gpt-4o"]). --rounds/-r: How many hands to deal (default 3). --elimination-count/-e: Stop once only this many players remain (default 1). --stack/-s: Starting chip stack (default 10000).

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Known Limitations

• No side pots: Currently, if a player goes all-in, the environment doesn’t handle side pots.
• Manual environment checks: If the LLM returns “check” while facing a bet, the code interprets it as invalid and re-prompts.
• Fictitious ‘expert-level poker AI’: The LLM’s strategic brilliance is not guaranteed. This is more a demonstration environment than a truly advanced solver.