Showing 4 open source projects for "operating system kernel"

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  • 1
    MemOS

    MemOS

    AI memory OS for LLM and Agent systems

    MemOS is an experimental operating system and runtime built around the concept of memory-centric computing, where memory objects are first-class citizens and program execution is organized around efficient, persistent memory access rather than traditional process and file system boundaries. The project explores rethinking system abstractions by tightly coupling computation with memory objects so that programs can operate on large datasets without expensive serialization or context switching. ...
    Downloads: 0 This Week
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  • 2
    LabClaw

    LabClaw

    Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)

    LabClaw is an open-source AI experimentation and agent orchestration platform designed to help developers build, test, and iterate on complex autonomous workflows in a controlled and modular environment. It provides a framework for composing multiple tools, prompts, and execution steps into structured pipelines that can be reused and evaluated across different scenarios. The system emphasizes experimentation, allowing users to run multiple variations of agent workflows, compare outputs, and...
    Downloads: 2 This Week
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  • 3
    MimiClaw

    MimiClaw

    Run OpenClaw on a $5 chip

    MimiClaw (from the mimiclaw project) is an edge-AI personal assistant that runs directly on extremely low-cost hardware like an ESP32-S3 microcontroller without a full operating system, Node.js, or cloud backend. By running pure C on a bare-metal chip, MimiClaw brings AI interactions and persistent memory to a tiny USB-powered device you can carry in your pocket. You connect the device to Wi-Fi and chat with it using Telegram, making it a convenient always-on assistant for tasks like reminders, quick lookups, or custom AI interactions. ...
    Downloads: 2 This Week
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  • 4
    Claw Compactor

    Claw Compactor

    14-stage Fusion Pipeline for LLM token compression

    ...It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach allows long-running agent sessions to continue operating efficiently without losing critical context. It is especially useful in autonomous workflows where agents accumulate large volumes of interaction history over time. ...
    Downloads: 0 This Week
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