Showing 4 open source projects for "http client"

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    OpenLLM

    OpenLLM

    Operating LLMs in production

    ...Built-in supports a wide range of open-source LLMs and model runtime, including Llama 2, StableLM, Falcon, Dolly, Flan-T5, ChatGLM, StarCoder, and more. Serve LLMs over RESTful API or gRPC with one command, query via WebUI, CLI, our Python/Javascript client, or any HTTP client.
    Downloads: 3 This Week
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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: 3 This Week
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  • 3
    LangServe

    LangServe

    Helps developers deploy LangChain runnables and chains as a REST API

    ...The tool simplifies the process of turning language-model pipelines, chains, and agents into web services that can be accessed by external applications. Instead of manually writing API endpoints, developers can use LangServe to automatically generate a server that exposes LangChain workflows through HTTP interfaces. The framework is built on top of FastAPI and uses Pydantic for request validation and structured data handling. It also includes client libraries that allow developers to interact with deployed chains from Python or JavaScript applications. LangServe is commonly used to deploy AI applications such as chatbots, document analysis pipelines, and agent-based systems that require scalable access through APIs.
    Downloads: 0 This Week
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  • 4
    Slack MCP Server

    Slack MCP Server

    The most powerful MCP Slack Server with no permission requirements

    ...Through this architecture, AI assistants can read message histories, interact with channels, and retrieve contextual information from Slack conversations in order to perform tasks such as automated analysis, collaboration assistance, or contextual code review. The server supports multiple communication transports, including standard input/output streams, HTTP, and Server-Sent Events, allowing flexible integration with different AI client environments.
    Downloads: 0 This Week
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