Building a chess playing agent using DSPy

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…