Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors
aws ·
This article details the implementation of persistent memory for multi-agent AI systems by integrating NVIDIA NeMo Agent Toolkit (NAT) with Amazon S3 Vectors. It explains how to create a custom MemoryEditor plugin that utilizes Amazon Titan Text Embeddings V2 to store and retrieve semantic data within an Amazon EKS environment. The guide provides code examples for configuring vector buckets, indexing metadata, and registering the provider in NAT's YAML configuration. A practical use case involving investment research agents demonstrates how multiple agents share knowledge through scoped metadata filters like team_id and user_id. The post also covers operational aspects such as containerizing agents, deploying them via Kubernetes manifests, and managing access through IAM Roles for Service Accounts. Finally, it discusses evaluating performance metrics like groundedness and token usage, alongside strategies for memory consolidation and resource cleanup.
aws