Trainable Agents is designed as a modular, extensible toolkit for rapid development and training of AI agents powered by state-of-the-art large language models. The framework abstracts core components such as interaction environments, policy interfaces, and feedback loops, enabling developers to define tasks, supply demonstrations, and implement reward functions effortlessly. With built-in support for OpenAI GPT and Anthropic Claude, the library facilitates experience replay, batch training, and performance evaluation. Trainable Agents also includes utilities for logging, metrics tracking, and exporting trained policies for deployment. Whether building conversational bots, automating workflows, or conducting research, this framework streamlines the entire lifecycle from prototyping to production in a unified Python package.

RL-Agents is a research-grade reinforcement learning framework built on PyTorch that bundles popular RL algorithms across value-based, policy-based, and actor-critic methods. The library features a modular agent API, GPU acceleration, seamless integration with OpenAI Gym, and built-in logging and visualization tools. Users can configure hyperparameters, customize training loops, and benchmark performance with a few lines of code, making RL-Agents ideal for academic research, prototyping, and industrial experimentation.

HMAS is an open-source Python framework that enables development of hierarchical multi-agent systems. It offers abstractions for defining agent hierarchies, inter-agent communication protocols, environment integration, and built-in training loops. Researchers and developers can use HMAS to prototype complex multi-agent interactions, train coordinated policies, and evaluate performance in simulated environments. Its modular design makes it easy to extend and customize agents, environments, and training strategies.