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.

Multiagent-Prediction-Reward is a research-oriented framework that integrates prediction models and reward distribution mechanisms for multi-agent reinforcement learning. It includes environment wrappers, neural modules for forecasting peer actions, and customizable reward routing logic that adapts to agent performance. The repository provides configuration files, example scripts, and evaluation dashboards to run experiments on cooperative tasks. Users can extend the code to test novel reward functions, integrate new environments, and benchmark against established multi-agent RL algorithms.