What MCP servers can YETR AI Agent connect to?
It connects to 50+ MCP servers including GitHub, GitLab, File Systems, Databases, Email, Calendar, Slack, Discord, Web Search, APIs, RSS feeds, OpenAI, Anthropic, and local AI models.
@yethikrishna
基本情報
🤖 YETR AI Agent - Your Enhanced Task Runner. Powerful AI agent system that connects to multiple MCP servers simultaneously, providing unified access to diverse tools and data sources through a modern web interface.
ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
YETR AI Agent is an advanced AI agent system that connects to multiple MCP (Model Context Protocol) servers simultaneously for intelligent task automation. It is designed for developers and teams needing multi-server orchestration, with a React/Next.js frontend, FastAPI backend, PostgreSQL database, and Redis cache.
You can try the live interactive demo at yethikrishna.github.io/yetr-ai-agent. For local development, clone the repository, then run npm install && npm run dev in the frontend directory, pip install -r requirements.txt && uvicorn app.main:app --reload in the backend directory, or use docker-compose up -d for the Docker setup. Configure MCP server connections and environment variables (e.g., DATABASE_URL, OPENAI_API_KEY) as described in the README.
It connects to 50+ MCP servers including GitHub, GitLab, File Systems, Databases, Email, Calendar, Slack, Discord, Web Search, APIs, RSS feeds, OpenAI, Anthropic, and local AI models.
Backend requires Python 3.11+, PostgreSQL, Redis, and a FastAPI server. Frontend requires Node.js with React 18, Next.js 14, TypeScript, and Tailwind CSS. A Docker setup is recommended for production-like environments.
Yes, an interactive chat demo is available at yethikrishna.github.io/yetr-ai-agent, where you can ask about GitHub analysis, deployment guidance, MCP integration, architecture, and features with real-time responses.
Authentication uses JWT with refresh tokens, authorization is role-based (RBAC), all communications are encrypted with TLS 1.3, and the system includes comprehensive input validation and rate limiting.
Response time is under 100 ms for simple queries, throughput supports 1,000+ concurrent conversations, and the architecture allows horizontal scaling with load balancing for 99.9% availability.

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