Showing 3 open source projects for "robust"

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  • Context for your AI agents Icon
    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

    Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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  • 1

    Aliza Gaming API

    An extensible development framework for roleplay games.

    AlizaGameAPI is a robust, open-source Java-based framework designed to streamline and enhance the development of 2D and 3D games. It offers a comprehensive set of tools, utilities, and libraries, empowering developers to create immersive and dynamic gaming experiences with ease. Key Features: Modular Architecture, Rich Graphics and UI Components, Comprehensive Game Logic and Character Management, Environment and World-Building Tools, Statistical and Mathematical Utilities, Enhanced Debugging and Logging, Data Management and Integration, Integrated LLM for NPC Dialog, Cross-Platform Compatibility Dive into the world of game development with AlizaGameAPI. ...
    Downloads: 0 This Week
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  • 2
    DIET Agents is a lightweight, scalable and robust multi-agent platform in Java. It is especially suitable for rapidly developing P2P prototype applications and/or adaptive, distributed applications that use bottom-up, nature-inspired techniques.
    Downloads: 0 This Week
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  • 3
    videonetwork

    videonetwork

    A simulator for self-organizing video replication and more

    ...One can say a complex adaptive network emerges, with all its problems. We tackle these problems by introducing a bio-inspired algorithm, which lets the content place itself where it is needed. It is robust, scalable and adaptive. The transport path of content is exploited to replicate content and limited storage space is handled by clean-up mechanisms. We implemented a simulator to show the algorithm's performance and to test its behavior under real circumstances. It is easily extendable to support several new scenarios or changes to the algorithm (see replication and client models).
    Downloads: 0 This Week
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