Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
mcp-use is the easiest way to interact with mcp servers with custom agents
AWS MCP Servers — helping you get the most out of AWS, wherever you use MCP.
5ire is a cross-platform desktop AI assistant, MCP client. It compatible with major service providers, supports local knowledge base and tools via model context protocol servers .
Klavis AI (YC X25): Open Source MCP integration for AI applications
🐬DeepChat - A smart assistant that connects powerful AI to your personal world
A framework helps you quickly build AI Native IDE products. MCP Client, supports Model Context Protocol (MCP) tools via MCP server.
AI agent microservice
A CLI tool for building Go applications.
ChatMCP is an AI chat client implementing the Model Context Protocol (MCP).
Dive is an open-source MCP Host Desktop Application that seamlessly integrates with any LLMs supporting function calling capabilities. ✨
An MCP client for Neovim that seamlessly integrates MCP servers into your editing workflow with an intuitive interface for managing, testing, and using MCP servers with your favorite chat plugins.
Witsy: desktop AI assistant / universal MCP client
AI as Workspace - An elegant AI chat client. Full-featured, lightweight. Support multiple workspaces, plugin system, cross-platform, local first + real-time cloud sync, Artifacts, MCP | 更好的 AI 客户端
⛓️RuleGo is a lightweight, high-performance, embedded, next-generation component orchestration rule engine framework for Go.
The Simple Agent Development Kit.
A desktop MCP client designed as a tool unitary utility integration, accelerating AI adoption through the Model Context Protocol (MCP) and enabling cross-vendor LLM API orchestration.
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