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.

CoderAssistants is an AI Agent framework designed to streamline software development workflows by embedding intelligent coding assistance directly into popular development environments and pipelines. At its core, CoderAssistants orchestrates large language models to generate boilerplate code, suggest improvements, automatically refactor legacy code, diagnose bugs based on error messages, and produce contextual documentation. Its modular plugin system allows teams to tailor agents for specific languages, frameworks, or compliance requirements, including custom prompt templates, workflow hooks, and automated testing integrations. By providing interactive chat-like assistants, CLI tools, and API endpoints, CoderAssistants ensures developers can interactively refine code, automate repetitive tasks, and maintain high code quality across projects.

ExampleAgent is a developer-focused toolkit designed to accelerate the creation of AI-driven assistants. It integrates directly with OpenAI’s GPT models to handle natural language understanding and generation, and offers a pluggable system for adding custom tools or APIs. The framework manages conversation context, memory, and error handling, enabling agents to perform information retrieval, task automation, and decision-making workflows. With clear code templates, documentation, and examples, teams can rapidly prototype domain-specific agents for chatbots, data extraction, scheduling, and more.