Playwright AI Agent POM MCP ServerPlaywright AI Agent using Page Object Model (POM) architecture with MCP Server integration for automated web and mobile testing

The full-stack TypeScript framework to build, test, and deploy production-ready MCP servers and AI-native apps.

A TypeScript framework for building MCP servers.

Agent-native TypeScript framework for MCP servers.

MCP Server Framework and Tool Development library for building custom capabilities into agents.

Bug bounty agent framework for Claude Code, Codex, Gemini, Cursor, Windsurf, Copilot, and OpenClaw — 48 agents, 26 commands, 19 CLI tools, 2 MCP servers, autonomous hunt loops, exploit chain builder.

Model Context Protocol management suite/factory. An MCP that can generate and manage other local MCPs in multiple languages. Uses the official SDKs for code gen.

TypeScript-first framework for the Model Context Protocol (MCP). You write clean, typed code; FrontMCP handles the protocol, transport, DI, session/auth, and execution flow.

Rails Engine with MCP compliant Spec.

🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support

blazigly fast gRPC/MCP client & server implementation in zig

ScaledMCP is a horizontally scalabled MCP and A2A Server. You know, for AI.

The official Java SDK for Model Context Protocol servers and clients. Maintained in collaboration with Spring AI

Mastra is the modern TypeScript framework for AI-powered applications and agents.

AI said it finished. Flyto2 shows the proof.

A Lisp with first-class LLM primitives, implemented in Rust

Trigger.dev – build and deploy fully‑managed AI agents and workflows

Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support

Java test automation framework for web, mobile, API, CLI, database, and desktop E2E testing with a fluent API and built-in reporting.

A Model Context Protocol (MCP) client library and debugging toolkit in Rust. This foundation provides both a production-ready SDK for building MCP integrations and the core architecture for an interactive debugger.

🌋 Build AI agents that seamlessly combine LLM reasoning with real-world actions via MCP tools — in just a few lines of TypeScript.

The open-source memory and observability layer for AI agents — persistent memory, loop detection, hash-chained audit trails, and a live dashboard, automatic on pip install.

Prior direction, kept rather than deleted. Signed, offline-verifiable receipts for AI agent actions, and a reference implementation of the OWASP Agentic Skills Top 10 AST09 receipt pattern. Nobulex is now the independent reliability registry for agent tools: github.com/arian-gogani/nobulex-registry