Showing 3 open source projects for "server local"

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  • 1
    webclaw

    webclaw

    Fast, local-first web content extraction for LLMs

    ...The tool addresses a major inefficiency in AI workflows by removing irrelevant elements like navigation menus, ads, and scripts, significantly reducing token usage when feeding data into language models. It supports multiple modes of operation, including CLI usage, REST API access, and an MCP server for direct integration with agent-based systems. Webclaw also provides advanced capabilities such as recursive crawling, structured JSON extraction, summarization, and content comparison, making it suitable for research and data pipelines. Its local-first architecture ensures privacy and eliminates the need for API keys.
    Downloads: 9 This Week
    Last Update:
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  • 2
    mistral.rs

    mistral.rs

    Fast, flexible LLM inference

    mistral.rs is a fast and flexible LLM inference engine implemented in Rust, designed to run and serve modern language models with an emphasis on performance and practical deployment. It provides multiple entry points for developers, including a CLI for running models locally and an HTTP server that exposes an OpenAI-compatible API surface for easy integration with existing clients. The project includes hardware-aware tooling that can benchmark a system and choose sensible quantization and...
    Downloads: 73 This Week
    Last Update:
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  • 3
    Extractous

    Extractous

    Fast and efficient unstructured data extraction

    Extractous is a Rust-based unstructured data extraction library focused on fast local parsing of documents and other content-heavy files. Its purpose is to extract text and metadata efficiently from formats such as PDF, Word, HTML, email archives, images, and more, without depending on external APIs or separate parsing servers. The project emphasizes performance and low memory usage, and its maintainers describe it as a local-first alternative to heavier extraction stacks. ...
    Downloads: 0 This Week
    Last Update:
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