Acme is a Python-based framework that simplifies the development and evaluation of reinforcement learning agents. It offers a collection of prebuilt agent implementations (e.g., DQN, PPO, SAC), environment wrappers, replay buffers, and distributed execution engines. Researchers can mix and match components to prototype new algorithms, monitor training metrics with built-in logging, and leverage scalable distributed pipelines for large-scale experiments. Acme integrates with TensorFlow and JAX, supports custom environments via OpenAI Gym interfaces, and includes utilities for checkpointing, evaluation, and hyperparameter configuration.

Agents with ADK is an open-source Python framework designed to streamline the creation of intelligent agents powered by large language models. It includes modular agent templates, built-in memory management, tool execution interfaces, and multi-agent coordination capabilities. Developers can quickly plug in custom functions or external APIs, configure planning and reasoning chains, and monitor agent interactions. The framework supports integration with popular LLM providers and provides logging, retry logic, and extensibility for production deployments.

Flat AI is a minimal-dependency Python framework from MindsDB designed to embed AI capabilities into products quickly. It supports chat, document retrieval and QA, text summarization, and more through a consistent interface. Developers can connect to OpenAI, Hugging Face, Anthropic, and other LLMs, as well as popular vector stores, without managing infrastructure. Flat AI handles prompt templating, batching, caching, error handling, multi-tenancy, and monitoring out of the box, enabling scalable, secure deployment of AI features in web apps, analytics tools, and automation workflows.