haive.games.mafia.agent¶

Mafia game agent implementation.

This module provides the core agent implementation for the Mafia game, handling:

  • Game initialization and setup

  • Player turn management

  • Move generation and validation

  • Game state visualization

  • Role-specific behavior

The agent uses LLMs to generate player decisions and narrator actions, creating an engaging and strategic game experience.

Example

>>> from mafia.agent import MafiaAgent
>>> from mafia.config import MafiaAgentConfig
>>>
>>> # Create and initialize agent
>>> config = MafiaAgentConfig.default_config(player_count=7)
>>> agent = MafiaAgent(config)
>>>
>>> # Run the game
>>> for state in agent.app.stream(initial_state):
...     agent.visualize_state(state)

Classes¶

MafiaAgent

Agent for playing Mafia.

Module Contents¶

class haive.games.mafia.agent.MafiaAgent(config)¶

Bases: haive.games.framework.multi_player.agent.MultiPlayerGameAgent[haive.games.mafia.config.MafiaAgentConfig]

Agent for playing Mafia.

This class implements the core game logic for Mafia, managing player turns, move generation, and game progression.

The agent handles:

  • Role assignment and management

  • Turn sequencing and validation

  • LLM-based decision making

  • Game state visualization

  • Win condition checking

state_manager¶

Manager for game state

Type:

MafiaStateManager

role_enum_mapping¶

Role to engine mapping

Type:

Dict[PlayerRole, str]

role_mapping¶

Engine to role mapping

Type:

Dict[str, PlayerRole]

Example

>>> config = MafiaAgentConfig.default_config(player_count=7)
>>> agent = MafiaAgent(config)
>>> initial_state = MafiaStateManager.initialize(
...     ["Player_1", "Player_2", "Narrator"]
... )
>>> for state in agent.app.stream(initial_state):
...     agent.visualize_state(state)

Initialize the Mafia agent.

Parameters:

config (MafiaAgentConfig) – Configuration for the agent

Example

>>> config = MafiaAgentConfig.default_config(player_count=7)
>>> agent = MafiaAgent(config)
determine_next_step_after_player_turn(state)¶

Determine what to do after a player’s turn.

This method decides the next game action based on:

  • Current game phase

  • Completed actions

  • Game end conditions

  • Maximum day limit

Parameters:

state (MafiaGameState) – Current game state

Returns:

Next action (“end_game”, “phase_transition”, or “next_player”)

Return type:

str

Example

>>> next_step = agent.determine_next_step_after_player_turn(state)
>>> print(next_step)  # Shows what happens next
extract_move(response, player_id)¶

Extract move from engine response.

This method processes the LLM response into a valid game action, handling:

  • Response validation

  • Action type conversion

  • Default action generation

  • Error handling

Parameters:

  • response (str) – Raw response from the LLM

  • player_id (str) – ID of the player making the move

Returns:

Validated game action

Return type:

Union[MafiaAction, NarratorAction]

Example

>>> response = engine.invoke(context)
>>> move = agent.extract_move(response, "Player_1")
>>> print(move.action_type)  # Shows the action type
get_engine_for_player(role, function)¶

Get the appropriate engine for a player based on role and function.

Parameters:

  • role (Union[PlayerRole, str]) – Player’s role or role string

  • function (str) – Function type (e.g., “player”)

Returns:

Engine configuration if found, None otherwise

Return type:

Optional[Any]

Example

>>> engine = agent.get_engine_for_player(
...     PlayerRole.MAFIA, "player"
... )
>>> print(engine.name)  # Shows "mafia_player"
get_player_role(state, player_id)¶

Get the role of a player.

Parameters:

  • state (MafiaGameState) – Current game state

  • player_id (str) – ID of the player to check

Returns:

The player’s role

Return type:

PlayerRole

Raises:

Exception – If player not found in state

Example

>>> role = agent.get_player_role(state, "Player_1")
>>> print(role)  # Shows PlayerRole.VILLAGER
handle_narrator_turn(state)¶

Handle the narrator’s turn.

This method manages narrator actions, including:

  • Phase transitions

  • Night action resolution

  • Public announcements

  • Game state updates

Parameters:

state (MafiaGameState) – Current game state

Returns:

Updated game state after narrator action

Return type:

Dict[str, Any]

Example

>>> new_state = agent.handle_narrator_turn(state)
>>> print(new_state["public_announcements"][-1])
handle_player_turn(state)¶

Handle a player’s turn with special Mafia logic.

This method manages a player’s turn, including:

  • Role-specific behavior

  • Move generation and validation

  • State updates

  • Error handling

Parameters:

state (MafiaGameState) – Current game state

Returns:

Updated game state after the turn

Return type:

Dict[str, Any]

Example

>>> new_state = agent.handle_player_turn(state)
>>> print(new_state["game_phase"])  # Shows current phase
prepare_move_context(state, player_id)¶

Prepare context for move generation.

This method gathers all relevant information for a player’s move, including:

  • Game state information

  • Player-specific knowledge

  • Legal moves

  • Recent history

Parameters:

  • state (MafiaGameState) – Current game state

  • player_id (str) – ID of the player making the move

Returns:

Context for move generation

Return type:

Dict[str, Any]

Example

>>> context = agent.prepare_move_context(state, "Player_1")
>>> print(context["phase"])  # Shows current game phase
prepare_narrator_context(state)¶

Prepare context for narrator actions.

This method gathers all information needed for narrator decisions, including:

  • Complete game state

  • Player summaries

  • Phase-specific information

  • Action histories

Parameters:

state (MafiaGameState) – Current game state

Returns:

Context for narrator decisions

Return type:

Dict[str, Any]

Example

>>> context = agent.prepare_narrator_context(state)
>>> print(context["phase"])  # Shows current game phase
state_to_dict(state)¶

Convert state to dictionary consistently.

This method handles various state formats and ensures consistent dictionary conversion for the game graph.

Parameters:

state (MafiaGameState) – State to convert

Returns:

Dictionary representation of the state

Return type:

Dict[str, Any]

Example

>>> state_dict = agent.state_to_dict(state)
>>> print(state_dict["game_phase"])
visualize_state(state_obj, debug=False)¶

Visualize the current game state.

This method creates a human-readable display of:

  • Game phase and status

  • Player information

  • Recent announcements

  • Game statistics

  • Voting results (if applicable)

Parameters:

  • state_obj – Game state (dict, MafiaGameState, or agent)

  • debug (bool, optional) – Show debug information. Defaults to False.

Example

>>> agent.visualize_state(state, debug=True)