Feb 24
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In this quick, hands-on tutorial, you’ll learn how to safely let AI agents execute Python code in an isolated sandbox using an MCP server, Deno, and Pyodide — all orchestrated with LangGraph. Modern AI agents often need to run real code, but doing this naively is dangerous. In this video, we solve that problem by building a secure, sandboxed execution environment that allows AI agents to execute Python without risking your system, files, or credentials. You’ll see how to: - Set up an MCP (Model Context Protocol) server for tool-based code execution - Use Deno as a secure runtime for sandboxing - Run Python safely via Pyodide (WebAssembly-based Python) - Connect everything to a LangGraph-powered AI agent - Prevent dangerous operations while still enabling powerful agent workflows Whether you’re working with LangGraph, CrewAI, Gemini, OpenAI, Anthropic, or local LLMs with LMStudio or Ollama, this pattern lets you give AI agents real code execution capabilities — without risk. ⏱️ What You’ll Learn ✔️ Safe AI code execution ✔️ MCP tools explained ✔️ Python sandboxing with Pyodide and Deno ✔️ Secure agent architectures ✔️ LangGraph + MCP integration 🧠 Tech Stack Used - MCP (Model Context Protocol) - LangGraph - Deno - Pyodide (Python in WebAssembly) - PydanticAI 🔒 Why This Matters Giving AI agents the ability to run code is powerful — but dangerous without proper isolation. This video shows a production-safe pattern you can actually trust. 👍 If this helped you Like, subscribe, and comment if you want: - More AI agent tutorials - More MCP or A2A tutorials - Advanced LangGraph workflows - Multi-agent systems #MCP #ModelContextProtocol #LangGraph #Python #Pyodide #Deno #Sandbox #Security #CodeExecution #AI #AIAgents #LLM #AgenticAI #AItools #SafeCodeExecution #aws #bedrock #langchain