https://medium.com/thoughts-on-machine-learning/building-a-chess-playing-agent-using-dspy-9b87c868f71e Open in app Sign up Sign in [ ] Write Sign up Sign in [1] Member-only story Building a chess playing agent using DSPy FS Ndzomga Thoughts on Machine Learning FS Ndzomga * Follow Published in Thoughts on Machine Learning * 2 min read * Just now -- Share Photo by Randy Fath on Unsplash It's underrated how powerful DSPy can be when you want to create agentic workflows. And when you add Typed Predictors from @thomasahle and @NormalComputing in the mix, the results are ... I just built a chess player agent and can watch it play against the stockfish engine. And it's beautiful. Here is how I proceeded. I first explored the capabilities of DSPy, particularly focusing on how it can be leveraged to create intelligent agents with specific skills. The idea was to use DSPy's Typed Predictors to encapsulate the logic for determining the best next move in a chess game, based on the current state of the board, the game's history, and a set of legal moves. The key to making this work was designing a robust data model and a prediction signature that could effectively interact with both the chess library and the DSPy framework. This involved defining a clear interface for our ChessAgent, encapsulating inputs such as the current board state, legal moves, and game history, and outputs that include the next move and reasoning behind it. After setting up the environment and configuring DSPy with an OpenAI API key, I defined a NextMove class to serve as the output model, capturing the best next move along with the reasoning. The ChessAgentSignature class was then defined to specify the inputs and outputs for our chess agent. The integration with the chess library and Stockfish engine was crucial. I initialized a chess board and configured the chess engine to run Stockfish, ensuring we could simulate real chess games for our agent to participate in. The game loop was implemented in the play_game function, where the chess agent and Stockfish take turns making moves. The agent's moves are generated by calling the chess_agent predictor, passing the current state of the game, and processing the response to execute the move on the board. This setup allowed me to run simulations of chess games, observing the performance of the DSPy-based agent against Stockfish, a highly competitive chess engine. Through iterative testing and refinement, I was able to fine-tune the agent's decision-making processes, resulting in a competent chess player that can hold its own in challenging matches. Here is the full code: -- -- FS Ndzomga Thoughts on Machine Learning Follow Written by FS Ndzomga 1.8K Followers *Editor for Thoughts on Machine Learning Engineer passionate about data science, startups, product management, philosophy and French literature. Built https://www.discute.co and https://www.rimbaud.ai Follow Help Status About Careers Blog Privacy Terms Text to speech Teams