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Agentenverhalten anpassen

  • A modular open-source framework for designing custom AI agents with tool integration and memory management.
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    What is AI-Creator?
    AI-Creator provides a flexible architecture for creating AI agents that can execute tasks, interact via natural language, and leverage external tools. It includes modules for prompt management, chain-of-thought reasoning, session memory, and customizable pipelines. Developers can define agent behaviors through simple JSON or code configurations, integrate APIs and databases as tools, and deploy agents as web services or CLI apps. The framework supports extensibility and modularity, making it ideal for prototyping chatbots, virtual assistants, and specialized digital workers.
    AI-Creator Core Features
    • Modular agent definition and pipeline configuration
    • Plugin-based tool and API integration
    • Session memory and context management
    • Prompt templates and chain-of-thought support
    • CLI and web service deployment
  • A Unity ML-Agents based environment for training cooperative multi-agent inspection tasks in customizable 3D virtual scenarios.
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    What is Multi-Agent Inspection Simulation?
    Multi-Agent Inspection Simulation provides a comprehensive framework for simulating and training multiple autonomous agents to perform inspection tasks cooperatively within Unity 3D environments. It integrates with the Unity ML-Agents toolkit, offering configurable scenes with inspection targets, adjustable reward functions, and agent behavior parameters. Researchers can script custom environments, define the number of agents, and set training curricula via Python APIs. The package supports parallel training sessions, TensorBoard logging, and customizable observations including raycasts, camera feeds, and positional data. By adjusting hyperparameters and environment complexity, users can benchmark reinforcement learning algorithms on coverage, efficiency, and coordination metrics. The open-source codebase encourages extension for robotics prototyping, cooperative AI research, and educational demonstrations in multi-agent systems.
  • Simulates an AI-powered taxi call center with GPT-based agents for booking, dispatch, driver coordination, and notifications.
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    What is Taxi Call Center Agents?
    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.
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