LangGraph Multi-Agent Patterns: How to Build Supervisor-Worker Graphs Safely

LangGraph allows developers to build multi-agent AI systems as explicit directed graphs, where nodes are Python functions, edges define transitions, and a shared typed state flows through every step. A common pattern is the supervisor-worker model, where a central supervisor routes tasks to specialized agents such as a researcher or coder before deciding when to finish. Developers are warned that a hard step-limit counter is essential, as a supervisor that never outputs a stop condition can silently exhaust API budgets. For tasks that can run independently, LangGraph's Send primitive enables true parallel execution, with results automatically merged via Python's operator.add annotation before an aggregator synthesizes them. The article highlights that using a plain list annotation instead of the annotated reducer silently drops parallel worker results, making correct state typing a critical pitfall to avoid.