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When building AI agents, developers struggle with organizing memory across sessions, which leads to irrelevant context retrieval and security vulnerabilities. AI agents that remember context across sessions need more than only storage. They need organized, retrievable, and secure memory. In Amazon Bedrock AgentCore Memory, namespaces determine how long-term memory records are organized, retrieved, and who can access them. Getting the namespace design right is essential to building an effective memory system.

In this post, you will learn how to design namespace hierarchies, choose the right retrieval patterns, and implement AWS Identity and Access Management (IAM)-based access control for AgentCore Memory. If you’re new to AgentCore Memory, we recommend reading our introductory blog post first: Amazon Bedrock AgentCore Memory: Building context-aware agents.

What are namespaces?

Namespaces are hierarchical paths that organize long-term memory records within an AgentCore Memory resource. Think of them like directory paths in a file system. They provide logical structure, enable scoped retrieval, and support access control.

When AgentCore Memory extracts long-term memory records from your conversations, each memory record is stored under a namespace. For example, a user’s preferences might live under /actor/customer-123/preferences/, while their session summaries might be stored at /actor/customer-123/session/session-789/summary/. With this structure, you can retrieve memory records at exactly the right level of granularity.

If you’ve worked with partition keys in Amazon DynamoDB or folder structures in Amazon Simple Storage Service (Amazon S3), the mental model transfers well. Just as you think through access patterns before choosing a partition key or designing your S3 folder hierarchy, you should think through your retrieval patterns before designing your namespace structure. Determine:

• Who needs to access these memories: A single user? All users of an agent?
• Granularity of retrieval you need: Is it per-session summaries? Cross-session preferences?
• Isolation boundaries that matter: Should one user’s memories ever be visible to another? Agent-scoped memories?

The main difference from a partition key is that namespaces support hierarchical retrieval in addition to exact match. You can query at each level of the hierarchy, not only at the leaf level. You can use a well-designed namespace to retrieve memories scoped to a single session, a single user across sessions, or a broader grouping, from the same memory resource. Namespaces are logical groupings within the same underlying storage. They provide organizational structure and access control, but long-term memory records across different namespaces co-exist within the same memory resource. Your hierarchy is your primary tool for organizing data for effective retrieval patterns.

Namespace templates and resolution

When creating a memory resource, you define namespace templates using the namespaceTemplate field within each strategy configuration. Templates support three pre-defined variables:

• {actorId} – resolves to the actor identifier from the events being processed
• {sessionId} – resolves to the session identifier from the events
• {memoryStrategyId} – resolves to the strategy identifier

Here’s an example of creating a memory resource with namespace templates:

response = agentcore_client.create_memory(
    name=”CustomerSupportMemory”,
    description=”Memory for customer support agents”,
    eventExpiryDuration=30,
    memoryStrategies=[
        {
            “semanticMemoryStrategy”: {
                “name”: “customer-facts”,
                “namespaceTemplate”: “/actor/{actorId}/facts/”
            }
        },
        {
            “summaryMemoryStrategy”: {
                “name”: “session-summaries”,
                “namespaceTemplate”: “/actor/{actorId}/session/{sessionId}/summary/”
            }
        }
    ]
)

When events arrive for actorId=customer-456 in sessionId=session-789, the resolved namespaces become:

• /actor/customer-456/facts/
• /actor/customer-456/session/session-789/summary/

Namespace design per memory strategy

Each memory strategy has different scoping needs, and the namespace design should reflect how that data will be accessed. Below are some common namespace design patterns for different memory strategies.

1. Semantic and user preferences: Actor-scoped

Semantic memory captures facts and knowledge from conversations (for example, “The customer’s company has 500 employees”). User Preference Memory captures choices and styles (for example, “User prefers Python for development work”). Both memory types accumulate over time and are relevant across sessions. A fact learned in January should still be retrievable in March. For these strategies, scope the namespace to the actor:

/actor/{actorId}/facts/
/actor/{actorId}/preferences/

This means the facts and preferences for a given user are consolidated under a single namespace, regardless of which session they were extracted from. The consolidation engine merges related memories within the same namespace. Look at figure 1 for an example of how scoping impacts the consolidation logic.

The following diagram illustrates how actor-scoped semantic and preference memories are organized:

Memory Resource: CustomerSupportMemory
│
├── /actor/customer-123/
│   ├── facts/
│   │   ├── “Company has 500 employees across Seattle, Austin, Boston”
│   │   ├── “Currently migrating from on-premises to cloud”
│   │   └── “Primary contact is the VP of Engineering”
│   └── preferences/
│       ├── “Prefers email communication over phone”
│       └── “Usually prefers detailed technical explanations”
│
├── /actor/customer-456/
│   ├── facts/
│   │   ├── “Startup with 20 employees”
│   │   └── “Using serverless architecture”
│   └── preferences/
│       └── “Prefers concise, high-level summaries”

In some use cases, an admin might need to retrieve information across actors while keeping memories organized per actor. For example, a customer support agent might need to look up known issues reported by other customers, or a sales agent might need to find similar customer profiles across its user base. For such cases, structure the namespace with the actor identifier as a child of the memory type rather than as the parent:

/customer-issues/{actorId}/
/sales/{actorId}/

With this inverted structure, you can use namespacePath=”/customer-issues/” to retrieve common issues raised across all customers, while still maintaining a per-actor organization. A query scoped to namespace=”/customer-issues/customer-123/” returns only that actor’s reported issues, preserving isolation when needed.

2. Summary: Session-scoped

Summary memory creates running narratives of conversations, capturing main points and decisions. Instead of feeding an entire conversation history into the large language model’s (LLM) context window, you can retrieve a compact summary that preserves the key information while significantly reducing token usage. Because summaries are inherently tied to a specific conversation, they should include the session identifier:/actor/{actorId}/session/{sessionId}/summary/This scoping means that each session gets its own summary, while still being organized under the actor for cross-session retrieval when needed.

Memory Resource: CustomerSupportMemory
│
├── /actor/customer-123/
│   ├── session/session-001/summary/
│   │   └── “Customer inquired about enterprise pricing, discussed
│   │        implementation timeline, requested follow-up demo”
│   ├── session/session-002/summary/
│   │   └── “Follow-up on demo scheduling, confirmed Q3 timeline,
│   │        discussed integration requirements with existing CRM”
│   └── session/session-003/summary/
│       └── “Technical deep-dive on API integration, reviewed
│            authentication options, chose OAuth 2.0 approach”

3. Episodic: Session-scoped with reflection hierarchy

Episodic memory captures complete reasoning traces, including the goal, steps taken, outcomes, and reflections. Because each episode represents what happened during a specific interaction, episodes should be scoped to the session, similar to summaries. For example, a flight booking agent might store an episode capturing how it searched for flights, compared options, handled a fare class restriction, and ultimately rebooked the customer on an alternative route. That episode belongs to the session where it occurred. Reflections are cross-episode insights stored at a parent level. They generalize learnings across sessions, for instance “when a fare class restriction blocks a modification, immediately search for alternative flights rather than just explaining the policy.” The namespace for reflections must be a sub-path of the namespace for episodes:

Episodes:    /actor/{actorId}/session/{sessionId}/episodes/
Reflections: /actor/{actorId}/

Retrieval patterns

Retrieval APIs

AgentCore Memory provides three primary retrieval APIs for long-term memory, each suited to different access patterns. Choosing the right one is key to building effective agents.

1. Semantic search with RetrieveMemoryRecords

Use RetrieveMemoryRecords to find memories that are semantically relevant to a query. This is the primary retrieval method during agent interactions, surfacing the most relevant memories based on meaning, rather than exact text matching.

# Retrieve memories relevant to the current user query
memories = agentcore_client.retrieve_memory_records(
    memoryId=”mem-12345abcdef”,
    namespace=”/actor/customer-123/facts/”,
    searchCriteria={
        “searchQuery”: “What cloud migration approach is the customer using?”,
        “topK”: 5
    }
)

The search query can come from two sources:

• Directly from the user query – Pass the user’s question as-is when it naturally maps to the kind of information stored in memory. For example, if the user asks “What’s my budget?”, that query works well for retrieving preference or fact memories.
• LLM-generated query – For more complex scenarios, have your agent’s LLM formulate a targeted search query. This is useful when the user’s raw input doesn’t directly map to stored memories. For example, if the user says “Help me plan my next trip,” the LLM might generate a search query like “travel preferences, destination history, budget constraints” to retrieve the most relevant memories. Note that this adds latency.

2. Direct retrieval with ListMemoryRecords

Use ListMemoryRecords when you need to enumerate memories within a specific namespace such as, displaying a user’s stored preferences in a console UI, auditing what memories exist, or performing bulk operations.

# List all memories in a specific namespace
records = agentcore_client.list_memory_records(
    memoryId=”mem-12345abcdef”,
    namespace=”/actor/customer-123/preferences/”
)

3. GetMemoryRecord and DeleteMemoryRecord

When you know the specific memory record ID (for example, from a previous list or retrieve call), use GetMemoryRecord for direct lookup or DeleteMemoryRecord to remove a specific memory:

# Get a specific memory record
record = agentcore_client.get_memory_record(
    memoryId=”mem-12345abcdef”,
    memoryRecordId=”rec-abc123″
)

# Delete a specific memory record
agentcore_client.delete_memory_record(
    memoryId=”mem-12345abcdef”,
    memoryRecordId=”rec-abc123″
)

These are useful for memory management workflows that are used to help users view, correct, or delete specific memories through your application’s UI.

Namespace vs. NamespacePath: Exact match vs. hierarchical retrieval

AgentCore Memory provides two distinct fields for scoping retrieval, and understanding the difference is critical for correct behavior.

1. namespace — Exact match

The namespace field performs an exact match. It returns only memory records stored at that precise namespace path.

# Returns ONLY records stored at /actor/customer-123/facts/
records = agentcore_client.retrieve_memory_records(
    memoryId=”mem-12345abcdef”,
    namespace=”/actor/customer-123/facts/”,
    searchCriteria={
        “searchQuery”: “cloud migration”,
        “topK”: 5
    }
)

This is the right choice when you know exactly which namespace you want to query and need precise scoping. For example, retrieving only a user’s preferences without pulling in their facts or summaries.

2. namespacePath — Hierarchical retrieval

The namespacePath field performs a hierarchical match, returning the memory records whose namespace falls under the specified path.

# Returns records from
# /actor/customer-123/facts/,
# /actor/customer-123/preferences/,
# /actor/customer-123/session/*/summary/, etc.
records = agentcore_client.retrieve_memory_records(
    memoryId=”mem-12345abcdef”,
    namespacePath=”/actor/customer-123/”,
    searchCriteria={
        “searchQuery”: “cloud migration”,
        “topK”: 5
    }
)

This is useful when you want to search across the user’s memories regardless of type, or when building features like “show me everything we know about this customer.” Note that it’s important that you think through your isolation and retrieval patterns to make sure that tree traversal doesn’t expose unintended data.

When to use which

Writing IAM policies for namespace access control

Namespaces integrate with AWS Identity and Access Management (IAM) through condition keys that restrict which namespaces a principal can include in their Memory API requests.

1. Exact match policies

Use StringEquals with the bedrock-agentcore:namespace condition key to restrict access to a specific namespace:

{
  “Version”: “2012-10-17”,
  “Statement”: [
    {
      “Effect”: “Allow”,
      “Action”: [
        “bedrock-agentcore:RetrieveMemoryRecords”,
        “bedrock-agentcore:ListMemoryRecords”
      ],
      “Resource”: “arn:aws:bedrock-agentcore:us-east-1:123456789012:memory/mem-12345abcdef”,
      “Condition”: {
        “StringEquals”: {
          “bedrock-agentcore:namespace”: “/actor/${aws:PrincipalTag/userId}/preferences/”
        }
      }
    }
  ]
}

This policy makes sure that a user can only retrieve memories from their own preferences namespace, using the userId principal tag (injected) for dynamic scoping.

2. Hierarchical retrieval policies

Use StringLike with the bedrock-agentcore:namespacePath condition key for hierarchical access:

{
  “Version”: “2012-10-17”,
  “Statement”: [
    {
      “Effect”: “Allow”,
      “Action”: [
        “bedrock-agentcore:RetrieveMemoryRecords”,
        “bedrock-agentcore:ListMemoryRecords”
      ],
      “Resource”: “arn:aws:bedrock-agentcore:us-east-1:123456789012:memory/mem-12345abcdef”,
      “Condition”: {
        “StringLike”: {
          “bedrock-agentcore:namespacePath”: “/actor/${aws:PrincipalTag/userId}/*”
        }
      }
    }
  ]
}

With this, a user can perform hierarchical retrieval across their namespaces (facts, preferences, summaries) while helping prevent access to other users’ data.

Conclusion

Namespace design is foundational to building effective memory systems with AgentCore Memory. Much like designing a key schema for a database or a prefix structure in object storage, thinking through your access patterns upfront helps you create a namespace hierarchy that supports precise retrieval, clean isolation between users, and IAM-based access control.The key takeaways:

• Think through your access patterns and isolation boundaries before coming up with namespace templates
• Scope semantic and preference memories to the actor (/actor/{actorId}/) for cross-session consolidation
• Scope summaries to the session (/actor/{actorId}/session/{sessionId}/) since they’re conversation-specific (where needed such as summaries or episodes)
• Use namespace for exact match when you know the precise path, and namespacePath for hierarchical retrieval when you need to search across a subtree
• Use leading and trailing slashes in namespace paths to keep them consistent and help prevent prefix collisions
• Use IAM condition keys (bedrock-agentcore:namespace and bedrock-agentcore:namespacePath) to control what namespaces can be requested

To get started, visit the following resources:

About the authors

Noor Randhawa

Noor Randhawa is the Tech Lead for AgentCore Memory at Amazon Web Services (AWS), building systems that enable developers to create intelligent, context-aware agents powered by Memory. He previously worked across Amazon Retail and AWS EKS, designing highly scalable and distributed platforms.

Akarsha Sehwag

Akarsha Sehwag is a Generative AI Data Scientist with AgentCore Memory team. With over seven years of experience in AI/ML product development, she has delivered enterprise-grade solutions for customers across a wide range of industries. Outside of work, she enjoys learning new things and exploring the outdoors.

Piradeep Kandasamy

Piradeep Kandasamy is a Software Development Manager for AgentCore Memory. Over his career at Amazon, he has built and scaled systems across Amazon Alexa, Amazon ECS, and AWS CloudFormation, bringing deep expertise in distributed systems and large-scale cloud services to his current work on memory infrastructure for AI agents.

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Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale.

The latest version of NVIDIA FLARE addresses this reality with a federated computing runtime that moves the training logic to the data, while raw data stays put. In high-stakes environments, centrally aggregating data is often not possible or practical, so a modern federated platform must treat data isolation, compliance, and privacy-enhancing technologies as first-class requirements.

What has historically slowed adoption isn’t the concept of FL—it’s the developer experience. If the path from “my local script trains” to “my job runs across federated sites” requires deep refactoring, new class hierarchies, or brittle configuration, many projects stall after the pilot.

The FLARE API evolution targets exactly that: eliminating the refactoring overhead by splitting the work into two concrete steps that map cleanly onto how teams actually build and ship ML systems:

• Step 1 (client API): Turn an existing local training script into a federated client with ~5–6 lines of code, without changing your training loop structure.
• Step 2 (job recipes): Select the FL workflow and bind it to your client training script, then run the same job across simulation, PoC, and production by swapping only the execution environment.

‘No data copy’ as a system requirement

In regulated or high-sensitivity settings, “just centralize the dataset” is increasingly off the table. A practical federated computing platform needs to support:

• No data copy: Data stays local, and only model updates (or equivalent signals) move.
• Compliance posture: Deployment and governance controls that support sovereignty and audit requirements.
• Privacy-enhancing techniques: Multiple layers of defenses (examples include homomorphic encryption, differential privacy, and confidential computing).

Figure 1. Federated computing keeps data in place, enabling collaboration through model updates while supporting compliance and privacy-enhancing protections.

The refactoring cliff: Why FL projects stall

Teams typically hit one of two cliffs after the pilot:

• The code cliff: Converting working PyTorch/TensorFlow/Lightning training into FL can require invasive restructuring—new abstractions, messaging glue, and framework-specific scaffolding.
• The lifecycle cliff: Even when simulation works, moving to PoC and production triggers rewrites via job redefinition, reconfiguration, and environment-specific branching.

FLARE flattens both cliffs by standardizing the workflow into two steps: 

1. Make your script federated (client API)
2. Execute it as a portable job (job recipe)

The intended experience is explicitly to combine these so you can go from zero to an operational federated job quickly.

Step 1: Convert your local training script into a federated client (client API)

Who it’s for: Practitioners and ML engineers with existing training code who want the smallest possible difference.

The mental model is intentionally simple:

1. Initialize the client runtime
2. Loop while the job is running
3. Receive the current global model
4. Train locally (your code)
5. Send updated weights + metrics back

FLARE’s client API is designed for minimal code changes and avoids forcing you into heavy “Executor/Learner” inheritance—use the FLModel structure or simple data exchange to communicate with the runtime.

Example 1a: Convert PyTorch to FLARE

Below is a concrete pattern you can apply to many scripts. The key touchpoints are: flare.init(), flare.receive(), loading model weights, and flare.send() with updated weights and metrics.

We show the local training code on the left and the federated version on the right, highlighting: import, flare.init(), receive(), send().

train.py

# train.py

import torch
import torchvision
import torchvision.transforms as transforms

from model import Net

batch_size = 4
epochs = 1
lr = 0.01
model = Net()
device = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)

train_dataset = torchvision.datasets.CIFAR10(
root=”/tmp/data/cifar10″, transform=transform, download=True, train=True
)

trainloader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True
)

model.to(device)

for epoch in range(epochs):
running_loss = 0.0

for i, batch in enumerate(trainloader):
images, labels = batch[0].to(device), batch[1].to(device)

optimizer.zero_grad()

predictions = model(images)
cost = loss(predictions, labels)
cost.backward()
optimizer.step()

running_loss += cost.cpu().detach().numpy() / batch_size

if i % 3000 == 2999:
print(
f”Epoch: {epoch + 1}/{epochs}, batch: {i + 1}, Loss: {running_loss / 3000}”
)
running_loss = 0.0

print(
f”Epoch: {epoch + 1}/{epochs}, batch: {i + 1}, Loss: {running_loss / (i + 1)}”
)

print(“Finished Training”)

torch.save(model.state_dict(), “./cifar_net.pth”)

client.py

# client.py

# 1. Import client API
import nvflare.client as flare
import torch
import torchvision
import torchvision.transforms as transforms

from model import Net

batch_size = 4
epochs = 1
lr = 0.01
model = Net()
device = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)

train_dataset = torchvision.datasets.CIFAR10(
root=”/tmp/data/cifar10″, transform=transform, download=True, train=True
)

trainloader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True
)

# 2. Initialize FLARE
flare.init()

# At each round while FLARE is running
while flare.is_running():
# 3. Receive the global model
input_model = flare.receive()

# 4. Load global model
model.load_state_dict(input_model.params)
model.to(device)

for epoch in range(epochs):
running_loss = 0.0

for i, batch in enumerate(trainloader):
images, labels = batch[0].to(device), batch[1].to(device)

optimizer.zero_grad()

predictions = model(images)
cost = loss(predictions, labels)
cost.backward()
optimizer.step()

running_loss += cost.cpu().detach().numpy() / batch_size

if i % 3000 == 2999:
print(
f”Epoch: {epoch + 1}/{epochs}, batch: {i + 1}, Loss: {running_loss / 3000}”
)
running_loss = 0.0

print(
f”Epoch: {epoch + 1}/{epochs}, batch: {i + 1}, Loss: {running_loss / (i + 1)}”
)

print(“Finished Training”)

torch.save(model.state_dict(), “./cifar_net.pth”)

# 5. Send back the updated model
output_model = flare.FLModel(
params=model.cpu().state_dict(),
meta={“NUM_STEPS_CURRENT_ROUND”: len(trainloader) * epochs},
)
flare.send(output_model)

Example 1b: PyTorch Lightning client The Lightning integration keeps the same

The Lightning integration keeps the same intent—receive global model, train, send updates—but exposes it in a Lightning-friendly way: import the Lightning client adapter and patch the Trainer.

The typical flow is: import, patch, (optional) validate, train as usual.

# lightning_client.py
import pytorch_lightning as pl
from pytorch_lightning import Trainer

import nvflare.client.lightning as flare # Lightning Client API

from model import LitNet
from data import CIFAR10DataModule
def main():
model = LitNet()
dm = CIFAR10DataModule()

trainer = Trainer(max_epochs=1, accelerator=”gpu”, devices=1)

# Patch trainer to participate in FL
flare.patch(trainer)

while flare.is_running():
# Optional: validate current global model (useful for server-side selection flows)
trainer.validate(model, datamodule=dm)

# Train starting from received global model (handled internally after patch)
trainer.fit(model, datamodule=dm)

if __name__ == “__main__”:
main()

The point: Lightning users don’t have to drop into custom federated messaging—they keep the Trainer abstraction and still participate correctly in FL rounds. 

Step 2: Package and execute the federated job anywhere (job recipes)

Who it’s for: Data scientists and applied teams who want a code-first job definition that remains stable across environments.

After step 1, you have a federated client script. Step 2 makes it a federated job you can run repeatedly and move through the lifecycle cleanly.

Job recipes are designed to replace JSON-based job configuration with a Python-based job definition:

• Code-first: Define complete FL jobs in Python, not complex config files
• Write once, run anywhere: Same recipe runs in simulator, PoC, or production
• Speed to deployment: Go from experimentation to deployment without changing code structure

Example 2a: Execute a FedAvg recipe in simulation

The key linkage is that your recipe references the client training script you created in step 1 (e.g., train_script=”client.py”), then you execute it in an environment.

# job.py
from nvflare.app_common.workflows.job import FedAvgRecipe
from nvflare.job_config import SimEnv # exact import path can vary by NVFlare version

from model import SimpleNetwork

def main():
n_clients = 3
num_rounds = 5
batch_size = 32

recipe = FedAvgRecipe(
name=”hello-pt”,
min_clients=n_clients,
num_rounds=num_rounds,
model=SimpleNetwork(),
train_script=”client.py”, # <-- Step A script train_args=f"--batch_size {batch_size} --epochs 1", ) env = SimEnv(num_clients=n_clients, num_threads=n_clients) recipe.execute(env=env) if __name__ == "__main__": main()

This is the “write once” idea in practice: Once the recipe correctly references your client script, the rest becomes an execution concern.

Example 2b: Move from simulation to real-world with an environment swap. 

Job recipes formalize a progressive workflow by swapping the execution environment:

1. SimEnv (Simulation): Easy development, rapid debugging
2. PocEnv (Proof-of-Concept): Local runtime, multi-process, realistic testing
3. ProdEnv (Production): Distributed deployment on secure, scalable infrastructure

Figure 2. One JobRecipe, multiple execution environments: Debug in SimEnv, validate in PocEnv, and deploy in ProdEnv without rewriting the job definition

Getting started

• Start with a script you already trust.
• Step 1: Add the client API handshake (or patch your Lightning Trainer).
• Step 2: Wrap it in a job recipe and execute first in simulation, then PoC, then production by swapping environments.

FLARE in the News

FLARE is showing up in real deployments—from Eli Lilly TuneLab’s federated learning platform (built by Rhino Federated Computing using NVFlare) to Taiwan MOHW’s national healthcare federated learning initiative, and a Tri-labs (Sandia/LANL/LLNL) federated AI pilot across sensitive datasets.

Going further

Start with a script you already trust. Add the minimal FLARE client handshake (receive → train → send). Then scale from single-node simulation to multi-site deployment when you’re ready.

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