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Comprehensive Echtzeit-Monitoring Tools for Every Need

Get access to Echtzeit-Monitoring solutions that address multiple requirements. One-stop resources for streamlined workflows.

Echtzeit-Monitoring

  • NeXent

    NeXent is an open-source platform for building, deploying, and managing AI agents with modular pipelines.
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    What is NeXent?
    NeXent is a flexible AI agent framework that lets you define custom digital workers via YAML or Python SDK. You can integrate multiple LLMs, external APIs, and toolchains into modular pipelines. Built-in memory modules enable stateful interactions, while a monitoring dashboard provides real-time insights. NeXent supports local and cloud deployment, Docker containers, and scales horizontally for enterprise workloads. The open-source design encourages extensibility and community-driven plugins.

  • Fleak AI Workflows

    Fleak simplifies AI workflow automation for data teams.
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    What is Fleak AI Workflows?
    Fleak empowers data teams to create, manage, and automate AI-driven workflows without the need for infrastructure. Its intuitive interface allows users to develop and deploy API endpoints effortlessly, making it ideal for those looking to streamline data operations. With robust monitoring tools, Fleak ensures that workflows are not only scalable but also efficient, allowing teams to focus on innovation rather than maintenance. The platform supports integration with leading data services, making it a comprehensive solution for data orchestration.

  • Lakera Guard

    Lakera provides enterprise-grade security for large language models (LLMs).
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    What is Lakera Guard?
    Lakera is focused on delivering enterprise-grade security solutions for large language models (LLMs). Its core product, Lakera Guard, empowers organizations to develop and operate generative AI applications without worrying about prompt injections, data loss, or exposure to harmful content. By providing tools like real-time monitoring, threat detection, and automated compliance checks, Lakera ensures that AI models are reliable, secure, and trustworthy.

  • Multi-Agent DDPG with PyTorch & Unity ML-Agents

    Implements decentralized multi-agent DDPG reinforcement learning using PyTorch and Unity ML-Agents for collaborative agent training.
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    What is Multi-Agent DDPG with PyTorch & Unity ML-Agents?
    This open-source project delivers a complete multi-agent reinforcement learning framework built on PyTorch and Unity ML-Agents. It offers decentralized DDPG algorithms, environment wrappers, and training scripts. Users can configure agent policies, critic networks, replay buffers, and parallel training workers. Logging hooks allow TensorBoard monitoring, while modular code supports custom reward functions and environment parameters. The repository includes sample Unity scenes demonstrating collaborative navigation tasks, making it ideal for extending and benchmarking multi-agent scenarios in simulation.
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