CrewAI-Learning is an open-source library designed to streamline multi-agent reinforcement learning projects. It offers environment scaffolding, modular agent definitions, customizable reward functions, and a suite of built-in algorithms such as DQN, PPO, and A3C adapted for collaborative tasks. Users can define scenarios, manage training loops, log metrics, and visualize results. The framework supports dynamic configuration of agent teams and reward sharing strategies, making it easy to prototype, evaluate, and optimize cooperative AI solutions across various domains.

Lekko is a powerful dynamic configuration tool that enhances software management by allowing runtime adjustments to code. Developers can set up dynamic functions in their applications, enabling real-time changes without interrupting the user experience or requiring code redeployments. By focusing on both the front-end and back-end, Lekko merges configuration management and development processes, driving efficiency and minimizing risks associated with updates. It incorporates features beyond traditional feature flags, making it a vital asset for any engineering team wishing to optimize their workflow.