Build durable AI agents with LangGraph and Amazon DynamoDB

This article explains how to build production-ready AI agents using LangGraph and Amazon DynamoDB with the new DynamoDBSaver checkpoint library for persistent state management.

• LangGraph enables complex, graph-based AI workflows with branching, merging, and looping capabilities
• DynamoDBSaver provides durable persistence layer for agent state across executions and failures
• Small checkpoints (<350KB) stored in DynamoDB; large payloads automatically offloaded to S3
• Supports time-to-live (TTL) for automatic checkpoint expiration and compression to reduce costs
• Enables human-in-the-loop review, failure recovery, and long-running conversation workflows
• Requires DynamoDB table with PK/SK keys and optional S3 bucket for large payloads
• Simple migration path from in-memory InMemorySaver to DynamoDBSaver for production deployment

DynamoDBSaver transforms LangGraph from a prototyping tool into a production-grade system by providing scalable, fault-tolerant state management for AI agents.