Plan Agent with Meta-Agent provides a layered AI agent architecture: the Plan Agent generates structured strategies to achieve high-level goals, while the Meta-Agent oversees execution, adjusts plans in real-time, and delegates subtasks to specialized sub-agents. It features plug-and-play tool connectors (e.g., web APIs, databases), persistent memory for context retention, and configurable logging for performance analysis. Users can extend the framework with custom modules to suit diverse automation scenarios, from data processing to content generation and decision support.

defaultmodeAGENT is a Python-based framework designed to simplify the creation of intelligent agents that perform multi-step workflows autonomously. It features default-mode planning—an adaptive strategy for deciding when to explore versus exploit—alongside seamless integration of custom tools and APIs. Agents maintain conversational memory, support dynamic prompting, and offer logging for debugging. Built on top of OpenAI’s API, it allows rapid prototyping of assistants for data extraction, research, and task automation.

TimeHero is a smart work management tool designed to help users plan and organize their daily tasks efficiently. By leveraging artificial intelligence, TimeHero intelligently schedules tasks around existing commitments such as meetings and deadlines, ensuring optimal use of your time. The platform is user-friendly and integrates seamlessly with other tools, allowing for a unified view of your workload. With features like task prioritization, recurring tasks, and adaptive planning, users can effortlessly manage their workflows and maximize productivity, making it a valuable asset for teams and organizations.

DEf-MARL (Decentralized Execution Framework for Multi-Agent Reinforcement Learning) provides a robust infrastructure to execute and train cooperative agents without centralized controllers. It leverages peer-to-peer communication protocols to share policies and observations among agents, enabling coordination through local interactions. The framework integrates seamlessly with common RL toolkits like PyTorch and TensorFlow, offering customizable environment wrappers, distributed rollout collection, and gradient synchronization modules. Users can define agent-specific observation spaces, reward functions, and communication topologies. DEf-MARL supports dynamic agent addition and removal at runtime, fault-tolerant execution by replicating critical state across nodes, and adaptive communication scheduling to balance exploration and exploitation. It accelerates training by parallelizing environment simulations and reducing central bottlenecks, making it suitable for large-scale MARL research and industrial simulations.

Molly is an advanced AI agent that serves as your personal assistant. It helps manage your schedule by organizing tasks, setting reminders, and facilitating meetings. With its smart natural language processing, you can communicate with Molly in conversational language, making it easy to delegate tasks and receive updates. Whether you're running late or need to reschedule meetings, Molly adapts to your needs, ensuring you remain productive.