Building Production-Ready Agentic AI: From Tutorial to High-Performance Serving (vLLM vs SGLang Benchmark)

Building Production-Ready Agentic AI: From Tutorial to Real-World Serving Benchmark Hey devs 👋 If you’ve been building ReAct agents with LangGraph, you’ve probably faced the same question I did: “I can build a cool agent in a tutorial… but which serving engine should I actually use in production?” That’s why I connected my two repositories: Agentic-AI-Tutorial → Learn how to build a full ReAct agent from scratch concurrent-llm-serving → Benchmark vLLM vs SGLang under heavy agent load Now the two repos are linked: the exact same agent from the tutorial is included as simpleagent/ inside the benchmark repo. What’s Inside the Agentic AI Tutorial You start with a clean, production-style LangGraph ReAct Agent that has three nodes: Conversation – Handles multi-turn dialogue Act – Calls real tools (DuckDuckGo Search + Calculator) Summarize – Processes long document context (10k+ tokens) Everything is explained step-by-step: Tool calling Structured outputs Memory management Error handling Repo