Botmonster Tech
AI agents built on LangGraph run as stateful graphs, not linear prompts. The graph can loop, branch on tool output, retry after a failure, and save its progress. That structure is what lets one agent handle long, multi-step tasks reliably.
Key Takeaways
- LangGraph models an agent as a stateful graph, so it can loop, retry, and recover.
- The state schema you design up front decides how stable the agent turns out.
- Built-in checkpointing lets an agent crash, pause for approval, and resume without lost work.
- Conditional edges turn failures into retries instead of dead ends.
- One agent task can fire dozens of LLM calls, so plan for cost before you deploy.
Prerequisites
You should know Python 3.11+ and the LangChain basics: LLMs, tools, prompts. The code below uses these versions: