Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

🍒 Cherry Studio is a desktop client that supports for multiple LLM providers.

🚀 The fast, Pythonic way to build MCP servers and clients

A framework helps you quickly build AI Native IDE products. MCP Client, supports Model Context Protocol (MCP) tools via MCP server.

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 .

AWS MCP Servers — specialized MCP servers that bring AWS best practices directly to your development workflow

ChatMCP is an AI chat client implementing the Model Context Protocol (MCP).

mcp-use is the easiest way to interact with mcp servers with custom agents

The Simple Agent Development Kit.

AI as Workspace - A better AI (LLM) 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

Witsy: desktop AI assistant

Dive is an open-source MCP Host Desktop Application that seamlessly integrates with any LLMs supporting function calling capabilities. ✨

A powerful Neovim plugin for managing MCP (Model Context Protocol) servers

MCPSharp is a .NET library that helps you build Model Context Protocol (MCP) servers and clients - the standardized API protocol used by AI assistants and models.

The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research

A ReAct-Based Highly Robust Autonomous Agent Framework

A Desktop Chat App that leverages MCP(Model Context Protocol) to interface with other LLMs.

A type-safe solution to remote MCP communication, enabling effortless integration for centralized management of Model Context.

Fine-grained control over model context protocol (MCP) clients, servers, and tools. Context is God.

MCP server for understanding AWS spend