GUIDE · CODING · #1376
How to use Amazon S3 Vectors as persistent memory for NVIDIA NeMo Agent Toolkit (NAT v1.6)
A how-to showing how to implement Amazon S3 Vectors as a custom memory provider for the open-source NVIDIA NeMo Agent Toolkit (NAT), including a MemoryEditor plugin, YAML configuration, and deployment on Amazon EKS; the post was tested with NAT v1.6 and uses Amazon Titan Text Embeddings V2 for embeddings. It demonstrates mapping NAT's memory model (MemoryItem/MemoryEditor) to S3 Vectors indexes and metadata, and explains required tooling and configuration for a multi-agent investment research example.
KEY POINTS
- A how-to showing how to implement Amazon S3 Vectors as a custom memory provider for the open-source NVIDIA NeMo Agent Toolkit (NAT), including a MemoryEditor plugin, YAML configuration, and deployment on Amazon EKS; the post was tested with NAT v1.6 and uses Amazon Titan Text Embeddings V2 for embeddings.
- It demonstrates mapping NAT's memory model (MemoryItem/MemoryEditor) to S3 Vectors indexes and metadata, and explains required tooling and configuration for a multi-agent investment research example.
- Using Amazon S3 Vectors as NAT's memory provider offers elastic, strongly consistent vector storage suitable for production multi-agent systems that need scalable persistent memory.
WHY IT MATTERS
Using Amazon S3 Vectors as NAT's memory provider offers elastic, strongly consistent vector storage suitable for production multi-agent systems that need scalable persistent memory.