SegAgent is a Python framework that orchestrates AI agents to perform semantic image segmentation through natural language interaction. By combining GPT-based language understanding with the Segment Anything Model (SAM), it converts user prompts—such as “segment the tumor region” or “refine around the edges”—into accurate masks. The agent retains conversational context, supports iterative refinement of segmentation results, and can integrate custom models or post-processing steps. It provides an extensible API, command-line tools, and Jupyter notebook examples. SegAgent accelerates annotation workflows, reduces manual tracing effort, and allows developers to embed conversational segmentation capabilities into broader pipelines or applications.

HowToReply.AI provides an AI-based solution to create tailored replies for any conversation. This intuitive generator understands the context and tone, making it suitable for both professional and casual interactions. By using HowToReply.AI, you can ensure your messages are always appropriate and effective, boosting customer satisfaction and enhancing your online communication instantly. Its simplicity in design allows users to generate responses quickly, ensuring you never have to worry about what to say next again.

This repository delivers a customizable multi-agent framework simulating a taxi call center. It defines distinct AI agents: CustomerAgent to request rides, DispatchAgent to select drivers based on proximity, DriverAgent to confirm assignments and update statuses, and NotificationAgent for billing and messages. Agents interact through an orchestrator loop using OpenAI GPT calls and memory, enabling asynchronous dialogue, error handling, and logging. Developers can extend or adapt agent prompts, integrate real-time systems, and prototype AI-driven customer service and dispatch workflows with ease.

Chatbot-Grok provides a modular AI Agent framework written in Python, designed to simplify development of conversational bots. It supports multi-turn dialogue management, retains chat memory across sessions, and allows users to define custom prompt templates. The architecture is extensible, letting developers integrate various LLMs including Grok, and connect to platforms such as Telegram or Slack. With clear code organization and plugin-friendly structure, Chatbot-Grok accelerates prototyping and deployment of production-ready chat assistants.