Pokemon Showdown AI Agents Built with Free LLM APIs and Tool-Calling

A developer built a system where LLMs like Llama 3, Qwen, and Gemma autonomously play Pokémon Showdown battles. The agents analyze the full battle state each turn—type matchups, HP, weather, field conditions, revealed opponent info—and decide whether to attack or switch using structured tool calls.

Key Details

• Routes everything through LiteLLM and exclusively uses models with free API tiers (Groq, Cerebras, OpenRouter, Google AI Studio).
• Zero inference cost to run locally.
• Two modes: Human vs. AI (play against the bot) and AI vs. AI (pit two models against each other).
• Supports 15+ free models out of the box.
• Full observability via Langfuse to see exact tool calls and reasoning per turn.

Architecture Highlights

The agent uses tool-calling to structure decisions—rather than simple prompt-response—raw battlefield data is fed into the LLM, which then selects attack or switch actions via predefined tool schemas. This allows reasoning about complex board states like type advantages and dynamic field effects.

GitHub Repo

Code and setup instructions: github.com/MohamedMostafa259/pokemon-ai-agent