LlamaGym

"Agents" originated in reinforcement learning, where they learn by interacting with an environment and receiving a reward signal. However, LLM-based agents today do not learn online (i.e. continuously in real time) via reinforcement.

OpenAI created Gym to standardize and simplify RL environments, but if you try dropping an LLM-based agent into a Gym environment for training, you'd find it's still quite a bit of code to handle LLM conversation context, episode batches, reward assignment, PPO setup, and more.

LlamaGym seeks to simplify fine-tuning LLM agents with RL. Right now, it's a single Agent abstract class that handles all the issues mentioned above, letting you quickly iterate and experiment with agent prompting & hyperparameters across any Gym environment.