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End-to-End Agentic AI Automation Lab MCP Server

@MDalamin5

代码仓库主页

基本信息

分类

推理

许可证

MIT

传输方式

stdio

作者

MDalamin5

关于 End-to-End Agentic AI Automation Lab MCP Server

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

#docker#aws#cicd#autogen#bentoml#a2a#agno#langchain#langsmith#crewai#langgraph#agentic-rag#agentic-ai#mcp-server

End-to-End Agentic AI Automation Lab MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "End-to-End-Agentic-Ai-Automation-Lab": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

End-to-End Agentic AI Automation Lab MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is End-to-End Agentic AI Automation Lab?

A comprehensive, production-grade repository for building, deploying, and managing intelligent AI agents, RAG pipelines, and automated workflows. It covers frameworks like LangGraph, AutoGen, MCP, n8n, and LangFlow, and is designed for developers transitioning from basic LLM calls to complex multi-agent autonomous systems.

How to use End-to-End Agentic AI Automation Lab?

Clone the repository, set up a Python 3.10+ virtual environment, navigate to a specific module folder, install its dependencies from requirements.txt, and configure a .env file with required API keys (e.g., OpenAI, Anthropic). Each module is self-contained and can be run independently.

Key features of End-to-End Agentic AI Automation Lab

  • 24 modules spanning foundations, agentic frameworks, and full-stack products.
  • Advanced agentic implementations with LangGraph, AutoGen, and MCP.
  • Production RAG Pipelines including hybrid search, BM25, and LlamaParse.
  • Zero-code orchestration using n8n and LangFlow.
  • LLM fine-tuning with LoRA/Unsloth and deployment via vLLM.
  • End-to-end projects: AI Interviewer, Applicant Tracking System, SynapseAI chatbot.

Use cases of End-to-End Agentic AI Automation Lab

  • Build multi-agent LangGraph workflows with memory and human-in-the-loop.
  • Implement production-grade RAG systems with hybrid search and re-ranking.
  • Fine-tune and deploy local LLMs with high-throughput inference endpoints.
  • Create full-stack AI applications like persistent chatbots and ATS systems.

常见问题

1

What agentic frameworks does the lab cover?

It covers LangGraph (state graphs, subgraphs, memory), AutoGen (round-robin, swarm, custom tools), and Anthropic's Model Context Protocol (MCP) for tool execution and web search.

2

What are the runtime requirements?

Python 3.10 or higher is required. Dependencies vary per module; each module contains its own `requirements.txt`. API keys for services like OpenAI, Anthropic, and Tavily are needed.

3

Is the code production-ready?

Yes, the lab emphasizes production-grade implementations, including full-stack projects, Alembic-backed databases, and high-throughput model serving via vLLM.

4

Can I run the examples in Google Colab?

Yes, a Colab badge is provided in the repository, indicating compatibility with Google Colab for certain modules.

5

What license applies?

The repository is distributed under the MIT License.

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