Build Your First Multi-Agent System in Python — 3 Patterns That Scale

Your single AI agent handles 5 tools. Then 10. Then 20. Somewhere around tool number 15, it starts picking the wrong one half the time. This is the single-agent ceiling. Every production team hits it. The fix is not a better prompt — it is splitting work across multiple specialized agents that coordinate. Multi-agent systems sound complex. They are not. If you have built one agent, you already know 80% of what you need. The remaining 20% is coordination — and LangGraph gives you 3 patterns to handle it. This tutorial covers each pattern with working code. By the end, you will have a multi-agent system where a supervisor routes questions to a research agent and a math agent, each doing what it does best. Why One Agent Is Not Enough A single agent with 20 tools faces two problems: Tool selection degrades. LLMs pick the right tool reliably from 5 options. At 20 options, accuracy drops — the model spends tokens reasoning about which tool to use instead of using it. Fewer tools per agent me