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In my previous post: Building persistent memory for multi-agent AI systems with Amazon S3 Vectors, we explored why memory engineering is the foundational discipline for production multi-agent systems. We showed how Amazon S3 Vectors, a capability of Amazon Simple Storage Service (Amazon S3), meets the architectural requirements for agent memory: semantic retrieval, rich metadata, strong consistency, and elastic scale. In this post, we move from architecture to implementation. We show you how to use Amazon S3 Vectors as the persistent memory layer within the NVIDIA NeMo Agent Toolkit (NAT), deployed on Amazon Elastic Kubernetes Service (Amazon EKS) for full operational control. By the end of this post, you will understand how NAT’s memory subsystem works and how to implement Amazon S3 Vectors as a custom memory provider. You will also learn how to deploy the stack on Amazon EKS, using a multi-agent investment research use case as the running example. What is NVIDIA NeMo Agent Toolkit? NVIDIA NeMo Agent Toolkit (NAT) is an open source framework for building, profiling, and optimizing AI agents. It’s framework-agnostic, working with Strands Agents, LangChain, LlamaIndex, CrewAI, and custom implementations. NAT provides four capabilities relevant to production agent systems: Agent orchestration – Define agents as composable workflows with configurable large language models (LLMs), tools, and prompts. You run them locally with nat run or as persistent services with nat serve. Profiling – Track token usage, latency, throughput, and run times across agents and individual tools to identify bottlenecks in multi-agent workflows. Evaluation – Built-in evaluators for answer accuracy, context relevance, response groundedness, and agent trajectory, with support for custom evaluators. Optimization – Automated hyperparameter tuning (temperature, top_p, max_tokens) that maximizes quality while minimizing cost and latency. NAT’s memory module NAT includes a dedicated memory subsystem designed to store and retrieve conversation history, user preferences, and long-term knowledge across agent invocations. The memory module is extensible: you create custom memory providers (backends) by implementing NAT’s plugin interface. Key components include: MemoryEditor – The abstract interface that all memory backends must implement. It defines three methods: add_items(), search(), and remove_items(). MemoryItem – The data model representing a piece of memory, containing fields for conversation history, tags, metadata, user_id, and an optional textual memory string. MemoryBaseConfig – A Pydantic base class that custom memory configurations extend. NAT discovers providers via the _type field in its YAML config. Automatic memory wrapper – The auto_memory_agent workflow type that wraps agents with automatic memory capture and retrieval, without requiring the LLM to explicitly invoke memory tools. NAT currently has these built-in memory providers: Mem0, MemMachine, Redis, and Zep. These cover common use cases. However, for production multi-agent systems that require elastic vector storage, strong write consistency, and cost-efficient scaling to billions of vectors, a custom provider backed by Amazon S3 Vectors is the right fit. Why Amazon S3 Vectors as the persistent memory backend Building persistent memory for multi-agent AI systems with Amazon S3 Vectors covers the architectural rationale in depth. The following table summarizes the properties that make S3 Vectors the right fit for NAT’s memory layer: Requirement S3 Vectors capability Semantic retrieval Vector similarity search with configurable distance metrics (cosine, euclidean) Scoped queries Filterable metadata on each vector (strings, numbers, Booleans, lists) Multi-agent coordination Strong write consistency. Memories are visible immediately after insertion Scale Up to 2 billion vectors per index with no capacity planning Cost efficiency Pay only for storage, writes, and queries with no idle compute Access control AWS Identity and Access Management (IAM) policies per bucket and index. Per-tenant indexes for hard isolation Prerequisites To follow along with the implementation in this post, you need: An AWS account with permissions to create Amazon S3 Vectors resources and Amazon EKS clusters. An existing Amazon EKS cluster (see Getting started with Amazon EKS). NVIDIA NeMo Agent Toolkit installed (tested with version 1.6). Python 3.11 or 3.12 (NAT requires >=3.11, list[float]: """Generate embeddings using Amazon Titan Text Embeddings V2.""" response = self._bedrock.invoke_model( modelId='amazon.titan-embed-text-v2:0', contentType='application/json', accept='application/json', body=json.dumps({ 'inputText': text, 'dimensions': 1024, 'normalize': True }) ) return json.loads(response['body'].read())['embedding'] async def add_items(self, items: list[MemoryItem], kwargs) -> None: """Store memory items as vectors in Amazon S3 Vectors.""" vectors = [] for item in items: # Build the text to embed from the memory content text = item.memory or json.dumps(item.conversation) embedding = self._get_embedding(text) # Use uuid4 to prevent key collisions within the same second key = f"mem_{item.user_id}_{uuid.uuid4().hex[:12]}" # Note: S3 Vectors metadata values have size limits. # Truncate content to 1024 characters for production use. content_for_metadata = text[:1024] mem_metadata = { 'user_id': item.user_id, 'memory_type': item.metadata.get('memory_type', 'episodic'), 'agent_id': item.metadata.get('agent_id', ''), 'team_id': item.metadata.get('team_id', ''), 'task_id': item.metadata.get('task_id', ''), 'confidence': item.metadata.get('confidence', 0.8), 'created_at_epoch': int(datetime.now(timezone.utc).timestamp()), 'is_shared': item.metadata.get('is_shared', True), 'source': item.metadata.get('source', 'agent'), 'content': content_for_metadata, } # Add domain-specific metadata if present if 'ticker' in item.metadata: mem_metadata['ticker'] = item.metadata['ticker'] vectors.append({ 'key': key, 'data': {'float32': embedding}, 'metadata': mem_metadata }) self._s3vectors.put_vectors( vectorBucketName=self._vector_bucket, indexName=self._index_name, vectors=vectors ) async def search(self, query: str, top_k: int = None, kwargs) -> list[MemoryItem]: """Retrieve semantically relevant memories from Amazon S3 Vectors.""" query_embedding = self._get_embedding(query) effective_top_k = top_k or self._default_top_k # Build metadata filter from kwargs filter_expr = {} for field in ('agent_id', 'memory_type', 'ticker', 'team_id', 'user_id'): if field in kwargs: filter_expr[field] = {'$eq': kwargs[field]} # Support boolean filter for is_shared if 'is_shared' in kwargs: filter_expr['is_shared'] = {'$eq': kwargs['is_shared']} response = self._s3vectors.query_vectors( vectorBucketName=self._vector_bucket, indexName=self._index_name, queryVector={'float32': query_embedding}, topK=effective_top_k, filter=filter_expr if filter_expr else None, returnMetadata=True ) results = [] for vec in response.get('vectors', []): results.append(MemoryItem( conversation=[], tags=[vec['metadata'].get('memory_type', '')], metadata=vec['metadata'], user_id=vec['metadata'].get('user_id', ''), memory=vec['metadata'].get('content', '') )) return results async def remove_items(self, **kwargs) -> None: """Remove memory items from Amazon S3 Vectors.""" keys = kwargs.get('keys', []) if keys: self._s3vectors.delete_vectors( vectorBucketName=self._vector_bucket, indexName=self._index_name, keys=keys ) # Register the memory provider so NAT can discover it @register_memory(config_type=S3VectorsMemoryConfig) async def build_s3vectors_memory(config: S3VectorsMemoryConfig, builder: Builder): yield S3VectorsMemoryEditor(config) The plugin generates embeddings with Amazon Titan Text Embeddings V2, stores each memory as a vector with scoped metadata, and translates search filters into Amazon S3 Vectors metadata queries. Step 3. Configure the NAT agent workflow With the plugin defined, configure it in NAT’s YAML config and wire it into an agent workflow: # config.yml - NAT agent configuration with Amazon S3 Vectors memory memory: agent_memory: _type: s3vectors_memory vector_bucket: "amzn-s3-demo-research-agent-memory" index_name: "agent-long-term-memory" aws_region: "us-west-2" functions: add_memory: _type: add_memory memory: agent_memory description: | Store important findings, patterns, or facts to long-term memory. Use this after discovering new information during research. get_memory: _type: get_memory memory: agent_memory description: | Retrieve relevant prior knowledge before starting research. Query with the current research topic to recall related findings. web_search: _type: web_search financial_data: _type: financial_data_api workflow: _type: auto_memory_agent inner_agent_name: research_agent memory_name: agent_memory llm_name: bedrock_llm save_user_messages_to_memory: true retrieve_memory_for_every_response: true save_ai_messages_to_memory: true llm: bedrock_llm: _type: bedrock model_id: "anthropic.claude-sonnet-4-20250514" temperature: 0.3 With the auto_memory_agent wrapper, you can capture and retrieve memory automatically. User messages and agent responses are stored, and relevant context is injected before each agent call. This design removes the need for the LLM to explicitly invoke memory tools. Responsible AI and data handling considerations Because this solution persists conversation history and user memory, plan for how that data is retained and accessed. Set a retention policy for stored memories, and use the memory consolidation and removal paths in this post to expire data you no longer need. Avoid storing personally identifiable information (PII) in memory metadata, and redact or tokenize sensitive fields before you embed them. Scope access with per-tenant indexes and least-privilege IAM policies so that each agent reads and writes only the memory it owns. Putting Amazon S3 Vectors in practice: Multi-agent investment research The following use case demonstrates this pattern in practice. Consider three specialized agents collaborating on an investment research tool: Research Agent – Gathers market data, earnings reports, and news. Analysis Agent – Performs quantitative analysis and identifies patterns. Synthesis Agent – Produces reports combining findings from both agents. With persistent memory, each agent builds on prior work. The Research Agent recalls previously collected data, avoiding redundant API calls. The Analysis Agent builds on patterns identified in prior sessions. The Synthesis Agent accesses cumulative findings to produce progressively richer reports. Multi-agent memory coordination Each agent uses the same S3 Vectors index but writes with its own agent_id in the metadata. Agents retrieve shared knowledge using metadata filters: # Analysis Agent retrieves Research Agent's shared findings research_findings = await memory_client.search( query=f"Recent research findings for {ticker}", top_k=10, team_id='investment-research', is_shared=True ) # Synthesis Agent queries all team knowledge all_team_knowledge = await memory_client.search( query=f"Complete analysis and research for {ticker} Q2 2026", top_k=20, team_id='investment-research' ) NAT’s built-in multi-tenant memory isolation uses user_id to scope memory per user. For multi-agent team coordination, the team_id metadata field provides an additional grouping dimension within the same S3 Vectors index. Memory consolidation Over time, episodic memories accumulate. Periodically consolidating them into semantic memories (generalized knowledge) keeps retrieval precise and efficient. You can trigger consolidation through a scheduled cron job, a vector count threshold, or an agent-initiated signal after a configurable number of research cycles: async def consolidate_memories(ticker: str, memory_client, workflow): """Distill episodic memori [truncated for AI cost control]