5ire is a cross-platform desktop AI assistant and Model Context Protocol (MCP) client, designed for users who want to integrate various LLM providers and local data sources into a unified interface. It offers enhanced functionality through a standardized MCP protocol, enabling access to local knowledge bases and external tools for richer AI interactions.

How It Works

5ire leverages the Model Context Protocol (MCP) to standardize how applications interact with LLMs and external tools. This protocol acts as a universal adapter, allowing 5ire to connect to diverse LLM providers (OpenAI, Azure, Anthropic, Google, etc.) and integrate tools for file system access, database interaction, and more. For local knowledge, it uses the bge-m3 embedding model to vectorize documents (docx, pdf, etc.), enabling Retrieval-Augmented Generation (RAG) capabilities.

Quick Start & Requirements

• Installation: Requires Python, Node.js, and uv (Python package manager) for the MCP Server runtime. Detailed instructions are in the Installation Guide.
• Platform Packaging: Native dependencies necessitate packaging for the specific OS. macOS users may need Apple developer credentials for notarization.
• Resources: No specific hardware requirements are listed, but LLM usage will dictate resource needs.

Highlighted Details

• Supports a wide range of LLM providers including OpenAI, Azure, Anthropic, Google, Baidu, Mistral, and Ollama.
• Features an open marketplace for MCP Servers, facilitating tool discovery and sharing.
• Integrates local knowledge base capabilities with bge-m3 for multilingual RAG on various document types.
• Includes usage analytics for API spending, a versatile prompt library with variable support, conversation bookmarking, and quick search functionality.

Maintenance & Community

• The project has a presence on Twitter.
• A Development Setup Guide and Wiki are available for contributors and advanced users.
• An "open marketplace for MCP Servers" suggests community involvement in tool development.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project requires specific runtime components (Python, Node.js, uv) for full tool functionality. macOS users may face additional setup steps for notarization. The absence of an explicit license and compatibility details may pose challenges for commercial adoption.