Nudger is an AI-powered roadmap planner that simplifies the process of goal setting and project management. It helps you break down your goals into manageable tasks and subtasks, providing dynamic views and progress tracking. With features like speech-to-text input, intelligent subtask creation, and an AI assistant to help you through complex tasks, Nudger ensures you stay on track. You can easily export your roadmaps to your favorite calendars and update tasks directly from there.

Rhythmic leverages Human-Centric AI to help agile teams transform ideas into actionable plans. Our AI assists in converting business cases to epics, breaking down complex tasks, simulating user journeys, and prioritizing effectively. By integrating with existing tools, Rhythmic significantly enhances productivity, providing intelligent estimations, tailored suggestions, and control over project management. Join our early access program to revolutionize your product planning processes.

Stridly is an innovative AI-powered goal-setting app designed to help users achieve their personal and professional aspirations. The app allows users to set any goal, from simple habits to ambitious dreams, and breaks these down into smaller, actionable quests tailored to the user’s specific needs. With AI-powered advice and adaptive learning, Stridly provides personalized tips, resources, and motivation to help users stay on track. It visualizes the user’s journey with clear timelines and milestones and helps integrate new habits into daily routines for long-term success. Stridly is catered for anyone looking for structured guidance and support in achieving their goals.

AI Agents with LangGraph leverages a graph representation to define relationships and communication between autonomous AI agents. Each node represents an agent or tool, enabling task decomposition, prompt customization, and dynamic action routing. The framework integrates seamlessly with popular LLMs and supports custom tool functions, memory stores, and logging for debugging. Developers can prototype complex workflows, automate multi-step processes, and experiment with collaborative agent interactions in just a few lines of Python code.

Fast-LLM-Agent-MCP is a lightweight, open-source Python framework for building AI agents that combine memory management, chain-of-thought reasoning, and multi-step planning. Developers can integrate it with OpenAI, Azure OpenAI, local Llama, and other models to maintain conversational context, generate structured reasoning traces, and decompose complex tasks into executable subtasks. Its modular design allows custom tool integration and memory stores, making it ideal for applications like virtual assistants, decision support systems, and automated customer support bots.

Dynamic Multi-Agent RAG Pathway provides a modular architecture where each agent handles specific tasks—such as document retrieval, vector search, context summarization, or generation—while a central orchestrator dynamically routes inputs and outputs between them. Developers can define custom agents, assemble pipelines via simple configuration files, and leverage built-in logging, monitoring, and plugin support. This framework accelerates development of complex RAG-based solutions, enabling adaptive task decomposition and parallel processing to improve throughput and accuracy.