Running a LangGraph ReAct Agent in Production: OpenAI-Compatible API + Multi-Mod

<h1> Building Production AI Agents with LangGraph: Beyond the Toy Examples </h1> <p>Every AI tutorial shows you a chatbot that answers questions. That's not an agent. An agent

<p>Claude's tool use (function calling) API is what separates toy chatbots from actual agents. I've built production agents with it — here's what reliable tool use looks like when

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,

<p>You've built the demo. It works. The LLM responds, the tools fire, the output looks great.</p> <p>Then you push it to production — and everything breaks.</p> <p>API calls fail

<h1> LangGraph State Machine Patterns — How to Stop AI Agents from Going Off-Script </h1> <h2> The "Agent Does Something Unexpected" Problem </h2> <p>LLM-powered agents that run

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<h1> See Inside Your LangGraph Agents </h1> <p><em>Open-source LangChain/LangGraph tracing — drop in two lines, watch your agents run live in your browser.</em></p> <h2> The