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