Merge pull request #77 from Madhuvod/ai-chess
Added new demo - AI Agents playing Chess
This commit is contained in:
Shubham Saboo
GitHub
commit
2a3f173a90
3 changed files with 285 additions and 0 deletions
31
ai_agent_tutorials/ai_chess_game/README.md
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31
ai_agent_tutorials/ai_chess_game/README.md
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# AI Chess Game - Autogen
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This is a simple Chess game that uses an AI agents - Player black and player white to play the game. There's also a board proxy agent to execute the tools and manage the game. It is important to use a board proxy as a non-LLM "guard rail" to ensure the game is played correctly and to prevent agents from making illegal moves.
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Two agents (agent_white and agent_black) are initialized using the OpenAI API key. These agents are configured to play chess as white and black, respectively.
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A board_proxy agent is created to manage the board state and validate moves.
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Functions (make_move and available_moves) are registered with the agents to allow them to interact with the board.
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd ai_agent_tutorials/ai_chess_game
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Get your OpenAI API Key
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- Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key.
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4. Run the Streamlit App
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```bash
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streamlit run ai_chess_agents.py
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```
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249
ai_agent_tutorials/ai_chess_game/ai_chess_agents.py
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249
ai_agent_tutorials/ai_chess_game/ai_chess_agents.py
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import chess
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import chess.svg
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import streamlit as st
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from autogen import ConversableAgent, register_function
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if "openai_api_key" not in st.session_state:
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st.session_state.openai_api_key = None
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if "board" not in st.session_state:
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st.session_state.board = chess.Board()
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if "made_move" not in st.session_state:
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st.session_state.made_move = False
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if "board_svg" not in st.session_state:
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st.session_state.board_svg = None
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if "move_history" not in st.session_state:
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st.session_state.move_history = []
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if "max_turns" not in st.session_state:
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st.session_state.max_turns = 5
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st.sidebar.title("Chess Agent Configuration")
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openai_api_key = st.sidebar.text_input("Enter your OpenAI API key:", type="password")
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if openai_api_key:
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st.session_state.openai_api_key = openai_api_key
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st.sidebar.success("API key saved!")
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st.sidebar.info("""
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For a complete chess game with potential checkmate, it would take max_turns > 200 approximately.
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However, this will consume significant API credits and a lot of time.
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For demo purposes, using 5-10 turns is recommended.
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""")
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max_turns_input = st.sidebar.number_input(
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"Enter the number of turns (max_turns):",
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min_value=1,
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max_value=1000,
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value=st.session_state.max_turns,
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step=1
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)
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if max_turns_input:
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st.session_state.max_turns = max_turns_input
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st.sidebar.success(f"Max turns of total chess moves set to {st.session_state.max_turns}!")
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st.title("Chess with AutoGen Agents")
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def available_moves() -> str:
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available_moves = [str(move) for move in st.session_state.board.legal_moves]
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return "Available moves are: " + ",".join(available_moves)
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def execute_move(move: str) -> str:
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try:
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chess_move = chess.Move.from_uci(move)
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if chess_move not in st.session_state.board.legal_moves:
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return f"Invalid move: {move}. Please call available_moves() to see valid moves."
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# Update board state
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st.session_state.board.push(chess_move)
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st.session_state.made_move = True
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# Generate and store board visualization
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board_svg = chess.svg.board(st.session_state.board,
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arrows=[(chess_move.from_square, chess_move.to_square)],
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fill={chess_move.from_square: "gray"},
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size=400)
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st.session_state.board_svg = board_svg
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st.session_state.move_history.append(board_svg)
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# Get piece information
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moved_piece = st.session_state.board.piece_at(chess_move.to_square)
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piece_unicode = moved_piece.unicode_symbol()
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piece_type_name = chess.piece_name(moved_piece.piece_type)
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piece_name = piece_type_name.capitalize() if piece_unicode.isupper() else piece_type_name
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# Generate move description
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from_square = chess.SQUARE_NAMES[chess_move.from_square]
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to_square = chess.SQUARE_NAMES[chess_move.to_square]
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move_desc = f"Moved {piece_name} ({piece_unicode}) from {from_square} to {to_square}."
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if st.session_state.board.is_checkmate():
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winner = 'White' if st.session_state.board.turn == chess.BLACK else 'Black'
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move_desc += f"\nCheckmate! {winner} wins!"
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elif st.session_state.board.is_stalemate():
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move_desc += "\nGame ended in stalemate!"
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elif st.session_state.board.is_insufficient_material():
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move_desc += "\nGame ended - insufficient material to checkmate!"
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elif st.session_state.board.is_check():
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move_desc += "\nCheck!"
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return move_desc
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except ValueError:
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return f"Invalid move format: {move}. Please use UCI format (e.g., 'e2e4')."
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def check_made_move(msg):
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if st.session_state.made_move:
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st.session_state.made_move = False
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return True
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else:
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return False
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if st.session_state.openai_api_key:
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try:
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agent_white_config_list = [
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{
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"model": "gpt-4o-mini",
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"api_key": st.session_state.openai_api_key,
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},
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]
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agent_black_config_list = [
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{
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"model": "gpt-4o-mini",
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"api_key": st.session_state.openai_api_key,
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},
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]
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agent_white = ConversableAgent(
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name="Agent_White",
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system_message="You are a professional chess player and you play as white. "
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"First call available_moves() first, to get list of legal available moves. "
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"Then call execute_move(move) to make a move.",
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llm_config={"config_list": agent_white_config_list, "cache_seed": None},
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)
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agent_black = ConversableAgent(
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name="Agent_Black",
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system_message="You are a professional chess player and you play as black. "
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"First call available_moves() first, to get list of legal available moves. "
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"Then call execute_move(move) to make a move.",
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llm_config={"config_list": agent_black_config_list, "cache_seed": None},
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)
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game_master = ConversableAgent(
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name="Game_Master",
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llm_config=False,
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is_termination_msg=check_made_move,
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default_auto_reply="Please make a move.",
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human_input_mode="NEVER",
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)
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register_function(
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execute_move,
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caller=agent_white,
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executor=game_master,
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name="execute_move",
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description="Call this tool to make a move.",
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)
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register_function(
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available_moves,
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caller=agent_white,
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executor=game_master,
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name="available_moves",
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description="Get legal moves.",
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)
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register_function(
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execute_move,
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caller=agent_black,
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executor=game_master,
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name="execute_move",
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description="Call this tool to make a move.",
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)
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register_function(
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available_moves,
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caller=agent_black,
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executor=game_master,
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name="available_moves",
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description="Get legal moves.",
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)
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agent_white.register_nested_chats(
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trigger=agent_black,
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chat_queue=[
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{
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"sender": game_master,
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"recipient": agent_white,
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"summary_method": "last_msg",
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}
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],
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)
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agent_black.register_nested_chats(
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trigger=agent_white,
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chat_queue=[
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{
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"sender": game_master,
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"recipient": agent_black,
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"summary_method": "last_msg",
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}
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],
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)
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st.info("""
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This chess game is played between two AG2 AI agents:
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- **Agent White**: A GPT-4o-mini powered chess player controlling white pieces
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- **Agent Black**: A GPT-4o-mini powered chess player controlling black pieces
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The game is managed by a **Game Master** that:
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- Validates all moves
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- Updates the chess board
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- Manages turn-taking between players
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- Provides legal move information
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""")
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initial_board_svg = chess.svg.board(st.session_state.board, size=300)
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st.subheader("Initial Board")
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st.image(initial_board_svg)
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if st.button("Start Game"):
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st.session_state.board.reset()
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st.session_state.made_move = False
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st.session_state.move_history = []
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st.session_state.board_svg = chess.svg.board(st.session_state.board, size=300)
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st.info("The AI agents will now play against each other. Each agent will analyze the board, "
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"request legal moves from the Game Master (proxy agent), and make strategic decisions.")
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st.success("You can view the interaction between the agents in the terminal output, after the turns between agents end, you get view all the chess board moves displayed below!")
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st.write("Game started! White's turn.")
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chat_result = agent_black.initiate_chat(
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recipient=agent_white,
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message="Let's play chess! You go first, its your move.",
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max_turns=st.session_state.max_turns,
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summary_method="reflection_with_llm"
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)
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st.markdown(chat_result.summary)
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# Display the move history (boards for each move)
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st.subheader("Move History")
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for i, move_svg in enumerate(st.session_state.move_history):
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# Determine which agent made the move
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if i % 2 == 0:
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move_by = "Agent White" # Even-indexed moves are by White
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else:
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move_by = "Agent Black" # Odd-indexed moves are by Black
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st.write(f"Move {i + 1} by {move_by}")
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st.image(move_svg)
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if st.button("Reset Game"):
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st.session_state.board.reset()
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st.session_state.made_move = False
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st.session_state.move_history = []
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st.session_state.board_svg = None
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st.write("Game reset! Click 'Start Game' to begin a new game.")
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except Exception as e:
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st.error(f"An error occurred: {e}. Please check your API key and try again.")
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else:
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st.warning("Please enter your OpenAI API key in the sidebar to start the game.")
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5
ai_agent_tutorials/ai_chess_game/requirements.txt
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ai_agent_tutorials/ai_chess_game/requirements.txt
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streamlit
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chess==1.11.1
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autogen==0.6.1
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cairosvg
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pillow
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