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.

At its core, RxAgent-Zoo is a reactive RL framework that treats data events from environments, replay buffers, and training loops as observable streams. Users can chain operators to preprocess observations, update networks, and log metrics asynchronously. The library offers parallel environment support, configurable schedulers, and integration with popular Gym and Atari benchmarks. A plug-and-play API allows seamless swapping of agent components, facilitating reproducible research, rapid experimentation, and scalable training workflows.

PromptMage is a Python framework that aims to streamline the development of complex, multi-step applications using large language models (LLMs). It offers a variety of features including a prompt playground, built-in version control, and an auto-generated API. Ideal for both small teams and large enterprises, PromptMage improves productivity and facilitates effective prompt testing and development. It can be deployed locally or on a server, making it accessible and manageable for diverse users.

Arcade is a developer-oriented framework that simplifies building AI agents by providing a cohesive SDK and command-line interface. Using familiar JS/TS syntax, you can define workflows that integrate large language model calls, external API endpoints, and custom logic. Arcade handles conversation memory, context batching, and error handling out of the box. With features like pluggable models, tool invocation, and a local testing playground, you can iterate quickly. Whether you're automating customer support, generating reports, or orchestrating complex data pipelines, Arcade streamlines the process and provides deployment tools for production rollout.

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.