BeeAI Framework Py Starter is an open-source Python project designed to bootstrap AI agent creation. It includes core modules for agent orchestration, a plugin system to extend functionality, and adapters for connecting to popular LLM APIs. Developers can define tasks, manage conversational memory, and integrate external tools through simple configuration files. The framework emphasizes modularity and ease of use, enabling rapid prototyping of chatbots, automated assistants, and data-processing agents without boilerplate code.

Java-Action-Datetime is an add-on module for the LightJason multi-agent system framework, designed to handle all temporal operations within your agents. It provides actions to retrieve the current timestamp, parse date/time strings into Java temporal objects, apply custom formatting patterns, perform arithmetic operations such as adding or subtracting durations, compute differences between datetimes, and convert between timezones. These actions seamlessly integrate into LightJason agent code, reducing boilerplate and enabling reliable, consistent temporal reasoning across distributed agent deployments.

Java-Action-Shape is a dedicated action library designed to extend the LightJason multi-agent framework with advanced geometric capabilities. It provides agents with out-of-the-box actions to instantiate common shapes (circle, rectangle, polygon), apply transformations (translate, rotate, scale), and perform analytical computations (area, perimeter, centroid). Each action is thread-safe and integrates with LightJason’s asynchronous execution model, ensuring efficient parallel processing. Developers can define custom shapes by specifying vertices and edges, register them within the agent’s action registry, and include them in plan definitions. By centralizing shape-related logic, Java-Action-Shape reduces boilerplate code, enforces consistent APIs, and accelerates the creation of geometry-driven agent applications, from simulations to educational tools.

GPT-CodeApp provides an intuitive GUI where users type natural language descriptions (e.g., “build a React login form”) and instantly receive ready-to-use code snippets or project scaffolding. It supports major languages like JavaScript, Python, Java, C#, and more. Users can customize prompts, browse history, and export generated files. Built with Electron and React, it runs cross-platform without requiring an IDE plugin. GPT-CodeApp helps accelerate development, reduce boilerplate, and learn new frameworks by example.

LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.