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.

Dead-simple self-learning offers developers a dead-simple approach to create and train reinforcement learning agents in Python. The framework abstracts core RL components, such as environment wrappers, policy modules, and experience buffers, into concise interfaces. Users can quickly initialize environments, define custom policies using familiar PyTorch or TensorFlow backends, and execute training loops with built-in logging and checkpointing. The library supports on-policy and off-policy algorithms, enabling flexible experimentation with Q-learning, policy gradients, and actor-critic methods. By reducing boilerplate code, dead-simple self-learning allows practitioners, educators, and researchers to prototype algorithms, test hypotheses, and visualize agent performance with minimal configuration. Its modular design also facilitates integration with existing ML stacks and custom environments.