Open Source Ruby Large Language Models (LLM)

Ruby Large Language Models (LLM)

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Browse free open source Ruby Large Language Models (LLM) and projects below. Use the toggles on the left to filter open source Ruby Large Language Models (LLM) by OS, license, language, programming language, and project status.

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  • 1
    Fast MCP

    Fast MCP

    A Ruby Implementation of the Model Context Protocol

    Fast MCP is a lightweight framework designed to simplify the development and deployment of servers that implement the Model Context Protocol. The Model Context Protocol enables AI assistants and applications to connect with external tools, services, and data sources through a standardized interface. Fast-mcp provides developers with a streamlined toolkit for building MCP servers that expose application functionality to AI agents. The framework focuses on ease of use, allowing developers to quickly define tools, endpoints, and integrations that can be accessed through MCP-compatible clients. By abstracting much of the underlying infrastructure, fast-mcp enables rapid prototyping of AI-enabled applications that can interact with external systems such as databases, APIs, or file systems. The project emphasizes performance and simplicity, making it suitable for both small prototypes and production deployments.
    Downloads: 5 This Week
    Last Update:
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  • 2
    RubyLLM

    RubyLLM

    One beautiful Ruby API for OpenAI, Anthropic, Gemini, Bedrock

    RubyLLM is an open-source Ruby library that provides a unified API for interacting with multiple large language model providers through a single, consistent interface. The library is designed to simplify the process of integrating AI capabilities into Ruby applications by abstracting away differences between model providers and API formats. Developers can use RubyLLM to communicate with a wide range of AI services including OpenAI, Anthropic, Google Gemini, Mistral, Ollama, and other compatible platforms through a single programming interface. The library supports advanced capabilities such as tool calling, structured responses, and schema-based outputs that enable developers to build more reliable AI-driven applications. RubyLLM also integrates smoothly with modern Ruby frameworks and development workflows, making it easier to embed AI functionality into web services, background jobs, and automation scripts.
    Downloads: 2 This Week
    Last Update:
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