Amazon Web Services (AWS) announced the introduction of a production-ready solution that enables developers to build durable AI agents with persistent state management using LangGraph and Amazon DynamoDB. Leveraging the new DynamoDBSaver connector, a LangGraph checkpoint library maintained by AWS for DynamoDB, the solution provides a scalable persistence layer designed to store agent state with intelligent handling of payloads based on size.
AI agents are rapidly evolving from simple chatbots into sophisticated systems capable of reasoning, maintaining context, and making decisions across long workflows, but persistence of state has remained a critical challenge. AWS’s new integration addresses this limitation by combining LangGraph’s powerful graph-based workflow orchestration with DynamoDB’s low-latency, fault-tolerant storage.
Amazon DynamoDB is a serverless, fully managed, distributed NoSQL database that delivers single-digit millisecond performance at any scale. Built for high availability, DynamoDB is well-suited for storing session data, application state, and now state checkpoints for AI agents.
LangGraph, an open-source framework from LangChain® for building complex, graph-based AI workflows, enables developers to define nodes that branch, merge, and loop, with state maintained across executions. The DynamoDBSaver connector persists this state, enabling agents to scale across worker instances, recover from failures, and preserve historical checkpoints for analysis, review, and audit.
Also Read: Q*Bird secures €2.5M grant and €5M equity from EIC Accelerator to scale Europe’s quantum secure networks
The DynamoDBSaver connector intelligently manages checkpoint storage, placing smaller checkpoints directly in DynamoDB and offloading larger payloads to Amazon S3 while storing reference pointers in DynamoDB. This hybrid design delivers durability and scalability without encountering item size limits, and includes features to help manage cost and storage lifecycle such as automatic expiration of old checkpoints and optional compression of checkpoint data.
The solution simplifies the transition from prototype to production by allowing developers to replace in-memory state handling with DynamoDB persistence by simply switching the checkpointer configuration. This advancement supports real-world use cases such as human-in-the-loop review, workflow failure recovery, and long-running conversations spanning hours or days.
AWS encourages developers to start with in-memory checkpoints during prototyping and switch to DynamoDBSaver for production deployments to benefit from durable, scalable state management capabilities. Comprehensive documentation and libraries are publicly available, and AWS recommends considering Amazon Bedrock AgentCore Runtime for fully managed deployment of LangGraph agents.
SOURCE: AWS