Prompt: Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors

This guide explains how to implement Amazon S3 Vectors as a custom memory provider within the NVIDIA NeMo Agent Toolkit (NAT) for multi-agent AI systems. It covers the architecture of NAT's memory module, the implementation of a custom MemoryEditor plugin backed by S3 Vectors, and deployment on Amazon EKS. The approach enables persistent, scalable, and semantically retrievable agent memory using vector databases, supporting multi-agent collaboration through shared long-term memory.

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Implement Amazon S3 Vectors as a custom memory provider for the NVIDIA NeMo Agent Toolkit. Create a S3VectorsMemoryConfig class extending MemoryBaseConfig with vector_bucket, index_name, and aws_region. Implement an S3VectorsMemoryEditor that generates embeddings using Amazon Titan Text Embeddings V2, stores vectors in the specified S3 Vectors index, and supports semantic search with filters on agent_id, memory_type, ticker, team_id, and is_shared. Register the plugin via the builder and integrate it into a NAT agent workflow using the auto_memory_agent wrapper. Ensure the implementation handles metadata truncation (limit content to 1024 chars) and respects per-tenant isolation. Example use case: multi-agent investment research where agents share long-term memory indexed under a team_id.

Example input

Use the placeholders in the prompt with permitted, anonymized source data.

Expected output

Follow the output format requested in the prompt and verify the result against the source.

Tools

boto3, Python 3.11+, NVIDIA NeMo Agent Toolkit, Amazon EKS, Docker, kubectl, AWS CLI / credentials