CharaChat leverages cutting-edge AI language models to facilitate engaging, personalized text-based conversations with virtual characters. Users can choose from a variety of predefined personas—such as friendly guides, storytellers and supportive companions—or create custom characters by setting personality traits, conversation goals and themes. The platform maintains contextual memory across sessions, enabling deeper interactions. Customizable backgrounds, avatars and specialized chat topics enhance immersion. CharaChat also offers chat log export, sharing options, and integration APIs for embedding AI characters into websites or apps. Ideal for roleplaying enthusiasts, writers seeking inspiration, language learners, or anyone looking for empathetic AI companionship, CharaChat combines versatility and ease of use to deliver an interactive, AI-driven dialogue experience.

Agentic-Systems is designed to streamline the development of sophisticated autonomous AI applications by offering a modular architecture composed of agent, tool, and memory components. Developers can define custom tools that encapsulate external APIs or internal functions, while memory modules retain contextual information across agent iterations. The built-in orchestration engine schedules tasks, resolves dependencies, and manages multi-agent interactions for collaborative workflows. By decoupling agent logic from execution details, the framework enables rapid experimentation, easy scaling, and fine-grained control over agent behavior. Whether prototyping research assistants, automating data pipelines, or deploying decision-support agents, Agentic-Systems provides the necessary abstractions and templates to accelerate end-to-end AI solution development.

Autonomais is a modular AI agent framework designed for full autonomy in task planning and execution. It integrates large language models to generate plans, orchestrates actions via a customizable pipeline, and stores context in memory modules for coherent multi-step reasoning. Developers can plug in external tools like web scrapers, databases, and APIs, define custom action handlers, and fine-tune agent behavior through configurable skills. The framework supports logging, error handling, and step-by-step debugging, ensuring reliable automation of research tasks, data analysis, and web interactions. With its extensible plugin architecture, Autonomais enables rapid development of specialized agents capable of complex decision-making and dynamic tool usage.