Erstellen und Verwenden von Arbeitsspeicher im Foundry Agent Service (Vorschau)

Installieren Sie die erforderlichen Pakete:

pip install "azure-ai-projects>=2.0.0" azure-identity

Installieren Sie die erforderlichen Pakete:

dotnet add package Azure.AI.Projects
dotnet add package Azure.AI.Projects.Agents
dotnet add package Azure.AI.Extensions.OpenAI
dotnet add package Azure.Identity

Installieren Sie die erforderlichen Pakete:

npm install @azure/ai-projects@2 @azure/identity

Installieren Sie die erforderlichen Pakete:

<dependency>
  <groupId>com.azure</groupId>
  <artifactId>azure-ai-agents</artifactId>
</dependency>
<dependency>
  <groupId>com.azure</groupId>
  <artifactId>azure-identity</artifactId>
</dependency>

Legen Sie Umgebungsvariablen für den Projektendpunkt und die Modellbereitstellungsnamen fest:

export FOUNDRY_PROJECT_ENDPOINT="https://{your-ai-services-account}.services.ai.azure.com/api/projects/{project-name}"
export MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME="<chat-model-deployment-name>"
export MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME="<embedding-model-deployment-name>"

Festlegen von Umgebungsvariablen für Ihren Projektendpunkt, Modellbereitstellungen, API-Version und Zugriffstoken:

FOUNDRY_PROJECT_ENDPOINT="https://{your-ai-services-account}.services.ai.azure.com/api/projects/{project-name}"
MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME="<chat-model-deployment-name>" # For example, gpt-5.2
MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME="<embedding-model-deployment-name>" # For example, text-embedding-3-small
API_VERSION="2025-11-15-preview"

# Get a short-lived access token using Azure CLI
ACCESS_TOKEN="$(az account get-access-token --resource https://ai.azure.com/ --query accessToken -o tsv)"

import os
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import MemoryStoreDefaultDefinition, MemoryStoreDefaultOptions
from azure.identity import DefaultAzureCredential

project_client = AIProjectClient(
    endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

memory_store_name = "my_memory_store"

# Specify memory store options
options = MemoryStoreDefaultOptions(
    chat_summary_enabled=True,
    user_profile_enabled=True,
    procedural_memory_enabled=True,
    default_ttl_seconds=30 * 24 * 60 * 60,
    user_profile_details="Avoid irrelevant or sensitive data, such as age, financials, precise location, and credentials"
)

# Create memory store
chat_model = os.environ["MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME"]
embedding_model = os.environ["MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME"]

definition = MemoryStoreDefaultDefinition(
    chat_model=chat_model,
    embedding_model=embedding_model,
    options=options
)

memory_store = project_client.beta.memory_stores.create(
    name=memory_store_name,
    definition=definition,
    description="Memory store with procedural memory and 30-day default TTL",
)

print(f"Created memory store: {memory_store.name}")

using System;
using Azure.AI.Projects;
using Azure.AI.Projects.Memory;
using Azure.Identity;

#pragma warning disable AAIP001

var projectEndpoint = Environment.GetEnvironmentVariable(
    "FOUNDRY_PROJECT_ENDPOINT");
var chatModel = Environment.GetEnvironmentVariable(
    "MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME");
var embeddingModel = Environment.GetEnvironmentVariable(
    "MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME");

AIProjectClient projectClient = new(
    new Uri(projectEndpoint),
    new DefaultAzureCredential());

var memoryStoreName = "my_memory_store";

// Specify memory store options
MemoryStoreDefaultDefinition memoryStoreDefinition = new(
    chatModel: chatModel,
    embeddingModel: embeddingModel
);
memoryStoreDefinition.Options = new(
    isUserProfileEnabled: true,
    isChatSummaryEnabled: true);
memoryStoreDefinition.Options.UserProfileDetails =
    "Avoid irrelevant or sensitive data, such as age, "
    + "financials, precise location, and credentials";

// Create memory store
MemoryStore memoryStore = projectClient.MemoryStores.CreateMemoryStore(
    name: memoryStoreName,
    definition: memoryStoreDefinition,
    description: "Memory store for customer support agent"
);

Console.WriteLine($"Created memory store: {memoryStore.Name}");

import { DefaultAzureCredential } from "@azure/identity";
import type {
  MemoryStoreDefaultDefinition,
  MemoryStoreDefaultOptions,
} from "@azure/ai-projects";
import { AIProjectClient } from "@azure/ai-projects";

const projectEndpoint =
  process.env["FOUNDRY_PROJECT_ENDPOINT"] ||
  "<project endpoint>";
const chatModelDeployment =
  process.env["MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME"] ||
  "<chat model deployment name>";
const embeddingModelDeployment =
  process.env["MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME"] ||
  "<embedding model deployment name>";

const memoryStoreName = "my_memory_store";

const project = new AIProjectClient(
  projectEndpoint,
  new DefaultAzureCredential(),
);

const memoryOptions: MemoryStoreDefaultOptions = {
  user_profile_enabled: true,
  chat_summary_enabled: true,
  procedural_memory_enabled: true,
  default_ttl_seconds: 30 * 24 * 60 * 60,
  user_profile_details:
    "Avoid irrelevant or sensitive data, such as age, " +
    "financials, precise location, and credentials",
};

const definition: MemoryStoreDefaultDefinition = {
  kind: "default",
  chat_model: chatModelDeployment,
  embedding_model: embeddingModelDeployment,
  options: memoryOptions,
};

const memoryStore = await project.beta.memoryStores.create(
  memoryStoreName,
  definition,
  {
    description: "Memory store with procedural memory and 30-day default TTL",
  },
);

console.log(
  `Created memory store: ${memoryStore.name} (${memoryStore.id})`,
);

import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.MemoryStoresClient;
import com.azure.ai.agents.models.MemoryStoreDefaultDefinition;
import com.azure.ai.agents.models.MemoryStoreDefaultOptions;
import com.azure.ai.agents.models.MemoryStoreDetails;
import com.azure.identity.DefaultAzureCredentialBuilder;

String projectEndpoint = System.getenv("FOUNDRY_PROJECT_ENDPOINT");
String chatModel = System.getenv("MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME");
String embeddingModel =
  System.getenv("MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME");

MemoryStoresClient memoryStoresClient = new AgentsClientBuilder()
  .credential(new DefaultAzureCredentialBuilder().build())
  .endpoint(projectEndpoint)
  .buildMemoryStoresClient();

String memoryStoreName = "my_memory_store";

MemoryStoreDefaultDefinition definition =
  new MemoryStoreDefaultDefinition(chatModel, embeddingModel)
    .setOptions(new MemoryStoreDefaultOptions(true, true));

MemoryStoreDetails memoryStore = memoryStoresClient.createMemoryStore(
  memoryStoreName,
  definition,
  "Memory store for customer support agent",
  null);

System.out.println("Created memory store: " + memoryStore.getName());

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my_memory_store",
    "description": "Memory store with procedural memory and 30-day default TTL",
    "definition": {
      "kind": "default",
      "chat_model": "'"${MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME}"'",
      "embedding_model": "'"${MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME}"'",
      "options": {
        "chat_summary_enabled": true,
        "user_profile_enabled": true,
        "procedural_memory_enabled": true,
        "default_ttl_seconds": 2592000,
        "user_profile_details": "Avoid irrelevant or sensitive data, such as age, financials, precise location, and credentials"
      }
    }
  }'

# Update memory store properties
updated_store = project_client.beta.memory_stores.update(
    name=memory_store_name,
    description="Updated description"
)

print(f"Updated: {updated_store.description}")

// Update memory store properties
MemoryStore updatedStore = projectClient.MemoryStores.UpdateMemoryStore(
    name: memoryStoreName,
    description: "Updated description"
);

Console.WriteLine($"Updated: {updatedStore.Description}");

const updatedStore = await project.beta.memoryStores.update(
  memoryStoreName,
  {
    description: "Updated description",
  },
);

console.log(`Updated: ${updatedStore.description}`);

import com.azure.ai.agents.models.MemoryStoreDetails;

MemoryStoreDetails updatedStore = memoryStoresClient.updateMemoryStore(
  memoryStoreName,
  "Updated description",
  null);

System.out.println("Updated: " + updatedStore.getDescription());

MEMORY_STORE_NAME="my_memory_store"

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/${MEMORY_STORE_NAME}?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "description": "Updated description"
  }'

# List all memory stores
stores_list = list(project_client.beta.memory_stores.list())

print(f"Found {len(stores_list)} memory stores")
for store in stores_list:
    print(f"- {store.name} ({store.description})")

// List all memory stores
foreach (MemoryStore store in projectClient.MemoryStores.GetMemoryStores())
{
    Console.WriteLine(
        $"Memory store: {store.Name} ({store.Description})");
}

const storeList = project.beta.memoryStores.list();

console.log("Listing all memory stores...");
for await (const store of storeList) {
  console.log(`  - Memory Store: ${store.name} (${store.id})`);
}

import com.azure.ai.agents.models.MemoryStoreDetails;

System.out.println("Listing all memory stores...");
for (MemoryStoreDetails store : memoryStoresClient.listMemoryStores()) {
    System.out.println(
        "  - Memory Store: " + store.getName() + " (" + store.getId() + ")");
}

curl -X GET "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

from azure.ai.projects.models import MemorySearchPreviewTool, PromptAgentDefinition

# Set scope to associate the memories with
scope = "user_123"

openai_client = project_client.get_openai_client()

# Create memory search tool
tool = MemorySearchPreviewTool(
    memory_store_name=memory_store_name,
    scope=scope,
    update_delay=1, # Wait 1 second of inactivity before updating memories
    # In a real application, set this to a higher value like 300 (5 minutes, default)
)

# Create a prompt agent with memory search tool
agent = project_client.agents.create_version(
    agent_name="MyAgent",
    definition=PromptAgentDefinition(
        model=os.environ["MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME"],
        instructions="You are a helpful assistant that answers general questions",
        tools=[tool],
    )
)

print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

using Azure.AI.Projects.Agents;
using Azure.AI.Extensions.OpenAI;
using OpenAI.Responses;

#pragma warning disable OPENAI001

// Set scope to associate the memories with
string scope = "user_123";

// Create a prompt agent with memory search tool
DeclarativeAgentDefinition agentDefinition = new(model: chatModel)
{
    Instructions = "You are a helpful assistant that answers "
        + "general questions",
};
agentDefinition.Tools.Add(new MemorySearchPreviewTool(
    memoryStoreName: memoryStore.Name,
    scope: scope)
{
    UpdateDelayInSecs = 1, // Wait 1 second of inactivity before updating memories
    // In a real application, set this to a higher value
    // like 300 (5 minutes, default)
});

ProjectsAgentVersion agent =
    projectClient.AgentAdministrationClient.CreateAgentVersion(
        agentName: "MyAgent",
        options: new(agentDefinition));

Console.WriteLine(
    $"Agent created (id: {agent.Id}, name: {agent.Name}, "
    + $"version: {agent.Version})");

// Set scope to associate the memories with
const scope = "user_123";

const agent = await project.agents.createVersion(
  "memory-search-agent",
  {
    kind: "prompt",
    model: chatModelDeployment,
    instructions:
      "You are a helpful assistant that retrieves relevant " +
      "information from the user's memory store to answer their questions.",
    tools: [
      {
        type: "memory_search_preview",
        memory_store_name: memoryStoreName,
        scope: scope,
        update_delay: 1,
      },
    ],
  },
);

console.log(
  `Created agent with memory search tool, agent ID: ${agent.id}, ` +
    `name: ${agent.name}, version: ${agent.version}`,
);

import com.azure.ai.agents.AgentsClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.models.AgentVersionDetails;
import com.azure.ai.agents.models.MemorySearchPreviewTool;
import com.azure.ai.agents.models.PromptAgentDefinition;
import com.azure.identity.DefaultAzureCredentialBuilder;

String scope = "user_123";

AgentsClient agentsClient = new AgentsClientBuilder()
  .credential(new DefaultAzureCredentialBuilder().build())
  .endpoint(projectEndpoint)
  .buildAgentsClient();

MemorySearchPreviewTool memoryTool = new MemorySearchPreviewTool(
  memoryStoreName,
  scope).setUpdateDelaySeconds(1);

PromptAgentDefinition agentDefinition = new PromptAgentDefinition(chatModel)
  .setInstructions("You are a helpful assistant that answers general questions")
  .setTools(java.util.Collections.singletonList(memoryTool));

AgentVersionDetails agent =
  agentsClient.createAgentVersion("MyAgent", agentDefinition);

System.out.println(
  "Agent created (id: " + agent.getId() + ", name: " + agent.getName()
    + ", version: " + agent.getVersion() + ")");

# The agents API uses api-version=v1, which differs from the memory store API version
curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/agents?api-version=v1" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "MyAgent",
    "definition": {
        "kind": "prompt",
        "model": "gpt-5.2",
        "instructions": "You are a helpful assistant that answers general questions",
        "tools": [
            {
              "type": "memory_search_preview",
              "memory_store_name": "my_memory_store",
              "scope": "user_123",
              "update_delay": 1
            }
        ]
    }
}'

import time

# Create a conversation with the agent with memory tool enabled
conversation = openai_client.conversations.create()
print(f"Created conversation (id: {conversation.id})")

# Create an agent response to initial user message
response = openai_client.responses.create(
    input="I prefer dark roast coffee",
    conversation=conversation.id,
    extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
    # To scope memories to an end user, uncomment:
    # extra_headers={"x-memory-user-id": "<user-id>"},
)

print(f"Response output: {response.output_text}")

# After an inactivity in the conversation, memories will be extracted from the conversation and stored
print("Waiting for memories to be stored...")
time.sleep(65)

# Create a new conversation
new_conversation = openai_client.conversations.create()
print(f"Created new conversation (id: {new_conversation.id})")

# Create an agent response with stored memories
new_response = openai_client.responses.create(
    input="Please order my usual coffee",
    conversation=new_conversation.id,
    extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
)

print(f"Response output: {new_response.output_text}")

using System.Threading;

#pragma warning disable OPENAI001

// Get a response client scoped to the agent
ProjectResponsesClient responseClient =
    projectClient.ProjectOpenAIClient
        .GetProjectResponsesClientForAgent(agent.Name);

// Create an agent response to initial user message
ResponseItem request = ResponseItem.CreateUserMessageItem(
    "I prefer dark roast coffee");
ResponseResult response = responseClient.CreateResponse([request]);
// To scope memories to an end user, uncomment:
// var options = new CreateResponseOptions();
// options.InputItems.Add(request);
// var requestOptions = new RequestOptions();
// requestOptions.AddHeader("x-memory-user-id", "<user-id>");
// ClientResult result = responseClient.CreateResponse(
//     BinaryContent.Create(options), requestOptions);
// ResponseResult response = ModelReaderWriter.Read<ResponseResult>(
//     result.GetRawResponse().Content);

Console.WriteLine($"Response output: {response.GetOutputText()}");

// After inactivity, memories are extracted and stored
Console.WriteLine("Waiting for memories to be stored...");
Thread.Sleep(65_000);

// Create a new response to demonstrate cross-session recall
ResponseItem newRequest = ResponseItem.CreateUserMessageItem(
    "Please order my usual coffee");
ResponseResult newResponse = responseClient.CreateResponse(
    [newRequest]);

Console.WriteLine(
    $"Response output: {newResponse.GetOutputText()}");

import { setTimeout } from "timers/promises";

const openaiClient = project.getOpenAIClient();

// Create a conversation with the agent with memory tool enabled
const conversation = await openaiClient.conversations.create();
console.log(`Created conversation (id: ${conversation.id})`);

// Create an agent response to initial user message
const response = await openaiClient.responses.create(
  {
    conversation: conversation.id,
    input: "I prefer dark roast coffee",
  },
  {
    body: {
      agent: { name: agent.name, type: "agent_reference" },
    },
    // To scope memories to an end user, uncomment:
    // headers: { "x-memory-user-id": "<user-id>" },
  },
);

console.log(`Response output: ${response.output_text}`);

// After inactivity, memories are extracted and stored
console.log("Waiting for memories to be stored...");
await setTimeout(65_000);

// Create a new conversation to demonstrate cross-session recall
const newConversation = await openaiClient.conversations.create();
console.log(`Created new conversation (id: ${newConversation.id})`);

// Create an agent response with stored memories
const newResponse = await openaiClient.responses.create(
  {
    conversation: newConversation.id,
    input: "Please order my usual coffee",
  },
  {
    body: {
      agent: { name: agent.name, type: "agent_reference" },
    },
  },
);

console.log(`Response output: ${newResponse.output_text}`);

import com.azure.ai.agents.ResponsesClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.models.AgentReference;
import com.azure.ai.agents.models.AzureCreateResponseOptions;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

ResponsesClient responsesClient = new AgentsClientBuilder()
  .credential(new DefaultAzureCredentialBuilder().build())
  .endpoint(projectEndpoint)
  .buildResponsesClient();

AgentReference agentReference = new AgentReference(agent.getName())
  .setVersion(agent.getVersion());

Response response = responsesClient.createAzureResponse(
  new AzureCreateResponseOptions().setAgentReference(agentReference),
  ResponseCreateParams.builder()
    .input("I prefer dark roast coffee"));

System.out.println("Response output: " + response.output());

System.out.println("Waiting for memories to be stored...");
Thread.sleep(65_000);

Response newResponse = responsesClient.createAzureResponse(
  new AzureCreateResponseOptions().setAgentReference(agentReference),
  ResponseCreateParams.builder()
    .input("Please order my usual coffee"));

System.out.println("Response output: " + newResponse.output());

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/openai/v1/conversations" \
    -H "Authorization: Bearer ${ACCESS_TOKEN}" \
    -H "Content-Type: application/json" \
    -d '{}'

# Copy the "id" field from the previous response
# To scope memories to an end user, add -H "x-memory-user-id: <user-id>" to the following request
curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/openai/v1/responses" \
    -H "Authorization: Bearer ${ACCESS_TOKEN}" \
    -H "Content-Type: application/json" \
    -d '{
      "input": "I prefer dark roast coffee",
      "conversation": "{conversation-id}",
      "agent_reference": {
        "type": "agent_reference",
        "name": "MyAgent"
      }
    }'

openai_client = project_client.get_openai_client()

# Configure the memory search tool
tools = [
    {
        "type": "memory_search_preview",
        "memory_store_name": memory_store_name,
        "scope": scope,
    }
]

# Ask the agent to remember information
remember_response = openai_client.responses.create(
    model=os.environ["MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME"],
    tools=tools,
    input="Remember that my preferred seat is aisle.",
)

for item in remember_response.output:
    if getattr(item, "type", None) == "memory_command_call":
        print(item.type)       # memory_command_call
        print(item.arguments)  # {"action": "remember", "content": "..."}
        print(item.status)     # completed

# Ask the agent to forget information
forget_response = openai_client.responses.create(
    model=os.environ["MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME"],
    tools=tools,
    input="Forget my preferred seat.",
)

for item in forget_response.output:
    if getattr(item, "type", None) == "memory_command_call":
        print(item.type)
        print(item.arguments)  # {"action": "forget", "content": "..."}
        print(item.status)

// This code snippet is currently unavailable.

const openaiClient = project.getOpenAIClient();

// Configure the memory search tool
const tools = [
  {
    type: "memory_search_preview",
    memory_store_name: memoryStoreName,
    scope: scope,
  },
];

// Ask the agent to remember information
const rememberResponse = await openaiClient.responses.create({
  model: chatModelDeployment,
  input: "Remember that my preferred seat is aisle.",
  tools: tools as any,
});

for (const item of rememberResponse.output) {
  const outputItem = item as Record<string, unknown>;
  if (outputItem["type"] === "memory_command_call") {
    console.log(outputItem["type"]);       // memory_command_call
    console.log(outputItem["arguments"]);
    // {"action": "remember", "content": "..."}
    console.log(outputItem["status"]);     // completed
  }
}

// Ask the agent to forget information
const forgetResponse = await openaiClient.responses.create({
  model: chatModelDeployment,
  input: "Forget my preferred seat.",
  tools: tools as any,
});

for (const item of forgetResponse.output) {
  const outputItem = item as Record<string, unknown>;
  if (outputItem["type"] === "memory_command_call") {
    console.log(outputItem["type"]);
    console.log(outputItem["arguments"]);
    // {"action": "forget", "content": "..."}
    console.log(outputItem["status"]);
  }
}

import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

Response rememberResponse = responsesClient.createAzureResponse(
  new AzureCreateResponseOptions().setAgentReference(agentReference),
  ResponseCreateParams.builder()
    .input("Remember that my preferred seat is aisle."));

System.out.println(rememberResponse.output());

Response forgetResponse = responsesClient.createAzureResponse(
  new AzureCreateResponseOptions().setAgentReference(agentReference),
  ResponseCreateParams.builder()
    .input("Forget my preferred seat."));

System.out.println(forgetResponse.output());

# Reuse the {conversation-id} from the previous section
# To scope memories to an end user, set x-memory-user-id in each request
curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/openai/v1/responses" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -H "x-memory-user-id: <user-id>" \
  -d '{
    "input": "Remember that my preferred seat is aisle.",
    "conversation": "{conversation-id}",
    "agent_reference": {
      "type": "agent_reference",
      "name": "MyAgent"
    }
  }'

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/openai/v1/responses" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -H "x-memory-user-id: <user-id>" \
  -d '{
    "input": "Forget my preferred seat.",
    "conversation": "{conversation-id}",
    "agent_reference": {
      "type": "agent_reference",
      "name": "MyAgent"
    }
  }'

# Set scope to associate the memories with
scope = "user_123"

user_message = {
  "role": "user",
  "content": "I prefer dark roast coffee and usually drink it in the morning",
   "type": "message"
}

update_poller = project_client.beta.memory_stores.begin_update_memories(
    name=memory_store_name,
    scope=scope,
    items=[user_message], # Pass conversation items that you want to add to memory
    update_delay=0, # Trigger update immediately without waiting for inactivity
)

# Wait for the update operation to complete, but can also fire and forget
update_result = update_poller.result()
print(f"Updated with {len(update_result.memory_operations)} memory operations")
for operation in update_result.memory_operations:
    print(
        f"  - Operation: {operation.kind}, Memory ID: {operation.memory_item.memory_id}, Content: {operation.memory_item.content}"
    )

# Extend the previous update with another update and more messages
new_message = {
    "role":"user", 
    "content":"I also like cappuccinos in the afternoon", 
    "type":"message"}

new_update_poller = project_client.beta.memory_stores.begin_update_memories(
    name=memory_store_name,
    scope=scope,
    items=[new_message],
    previous_update_id=update_poller.update_id, # Extend from previous update ID
    update_delay=0, # Trigger update immediately without waiting for inactivity
)
new_update_result = new_update_poller.result()
for operation in new_update_result.memory_operations:
    print(
        f"  - Operation: {operation.kind}, Memory ID: {operation.memory_item.memory_id}, Content: {operation.memory_item.content}"
    )

#pragma warning disable OPENAI001

// Set scope to associate the memories with
string scope = "user_123";

MemoryUpdateOptions memoryOptions = new(scope)
{
    UpdateDelay = 0, // Trigger update immediately without waiting for inactivity
};
memoryOptions.Items.Add(ResponseItem.CreateUserMessageItem(
    "I prefer dark roast coffee and usually drink it "
    + "in the morning"));

// Wait for the update operation to complete
MemoryUpdateResult updateResult =
    projectClient.MemoryStores.WaitForMemoriesUpdate(
        memoryStoreName: memoryStore.Name,
        options: memoryOptions,
        pollingInterval: 500);

if (updateResult.Status == MemoryStoreUpdateStatus.Failed)
{
    throw new InvalidOperationException(
        updateResult.ErrorDetails);
}
Console.WriteLine(
    $"Updated with {updateResult.Details.MemoryOperations.Count} "
    + "memory operations");
foreach (var operation in updateResult.Details.MemoryOperations)
{
    Console.WriteLine(
        $"  - Operation: {operation.Kind}, "
        + $"Memory ID: {operation.MemoryItem.MemoryId}, "
        + $"Content: {operation.MemoryItem.Content}");
}

// Extend the previous update with another message
MemoryUpdateOptions newMemoryOptions = new(scope)
{
    PreviousUpdateId = updateResult.UpdateId,
    UpdateDelay = 0, // Trigger update immediately without waiting for inactivity
};
newMemoryOptions.Items.Add(ResponseItem.CreateUserMessageItem(
    "I also like cappuccinos in the afternoon"));

MemoryUpdateResult newUpdateResult =
    projectClient.MemoryStores.WaitForMemoriesUpdate(
        memoryStoreName: memoryStore.Name,
        options: newMemoryOptions,
        pollingInterval: 500);

if (newUpdateResult.Status == MemoryStoreUpdateStatus.Failed)
{
    throw new InvalidOperationException(
        newUpdateResult.ErrorDetails);
}
foreach (var operation in newUpdateResult.Details.MemoryOperations)
{
    Console.WriteLine(
        $"  - Operation: {operation.Kind}, "
        + $"Memory ID: {operation.MemoryItem.MemoryId}, "
        + $"Content: {operation.MemoryItem.Content}");
}

const scope = "user_123";

const userMessage: Record<string, unknown> = {
  type: "message",
  role: "user",
  content: [
    {
      type: "input_text",
      text: "I prefer dark roast coffee and usually drink it in the morning",
    },
  ],
};

console.log("\nSubmitting memory update request...");
const updatePoller = project.beta.memoryStores.updateMemories(
  memoryStoreName,
  scope,
  {
    items: [userMessage],
    updateDelayInSecs: 0,
  },
);

const updateResult = await updatePoller.pollUntilDone();
console.log(
  `Updated with ${updateResult.memory_operations.length} ` +
    `memory operation(s)`,
);
for (const operation of updateResult.memory_operations) {
  console.log(
    `  - Operation: ${operation.kind}, ` +
      `Memory ID: ${operation.memory_item.memory_id}, ` +
      `Content: ${operation.memory_item.content}`,
  );
}

// Extend the previous update with another message
const newMessage = {
  role: "user",
  content: "I also like cappuccinos in the afternoon",
  type: "message",
};

const newUpdatePoller = project.beta.memoryStores.updateMemories(
  memoryStoreName,
  scope,
  {
    items: [newMessage],
    updateDelayInSecs: 0,
  },
);

const newUpdateResult = await newUpdatePoller.pollUntilDone();
console.log(
  `Updated with ${newUpdateResult.memory_operations.length} ` +
    `memory operation(s)`,
);
for (const operation of newUpdateResult.memory_operations) {
  console.log(
    `  - Operation: ${operation.kind}, ` +
      `Memory ID: ${operation.memory_item.memory_id}, ` +
      `Content: ${operation.memory_item.content}`,
  );
}

import com.azure.ai.agents.models.MemoryStoreUpdateCompletedResult;
import com.azure.ai.agents.models.MemoryStoreUpdateResponse;
import com.azure.core.util.polling.SyncPoller;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseInputItem;
import java.util.Arrays;

ResponseInputItem userMessage = ResponseInputItem.ofEasyInputMessage(
  EasyInputMessage.builder()
    .role(EasyInputMessage.Role.USER)
    .content("I prefer dark roast coffee and usually drink it in the morning")
    .build());

SyncPoller<MemoryStoreUpdateResponse, MemoryStoreUpdateCompletedResult> updatePoller =
  memoryStoresClient.beginUpdateMemories(
    memoryStoreName,
    scope,
    Arrays.asList(userMessage),
    null,
    0);

updatePoller.waitForCompletion();
MemoryStoreUpdateCompletedResult updateResult = updatePoller.getFinalResult();
System.out.println(
  "Updated with " + updateResult.getMemoryOperations().size()
    + " memory operation(s)");
for (var operation : updateResult.getMemoryOperations()) {
  System.out.println(
    "  - Operation: " + operation.getKind() + ", Memory ID: "
      + operation.getMemoryItem().getMemoryId() + ", Content: "
      + operation.getMemoryItem().getContent());
}

ResponseInputItem newMessage = ResponseInputItem.ofEasyInputMessage(
  EasyInputMessage.builder()
    .role(EasyInputMessage.Role.USER)
    .content("I also like cappuccinos in the afternoon")
    .build());

// Pass null for previousUpdateId to start a fresh independent update.
// To chain from the previous update, pass the update ID from the
// intermediate poller response instead.
SyncPoller<MemoryStoreUpdateResponse, MemoryStoreUpdateCompletedResult> newUpdatePoller =
  memoryStoresClient.beginUpdateMemories(
    memoryStoreName,
    scope,
    Arrays.asList(newMessage),
    null,
    0);

newUpdatePoller.waitForCompletion();
MemoryStoreUpdateCompletedResult newUpdateResult = newUpdatePoller.getFinalResult();
for (var newOperation : newUpdateResult.getMemoryOperations()) {
  System.out.println(
    "  - Operation: " + newOperation.getKind() + ", Memory ID: "
      + newOperation.getMemoryItem().getMemoryId() + ", Content: "
      + newOperation.getMemoryItem().getContent());
}

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store:update_memories?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "scope": "user_123",
    "items": [
      {
        "type": "message",
        "role": "user",
        "content": [
          {
            "type": "input_text",
            "text": "I prefer dark roast coffee and usually drink it in the morning"
          }
        ]
      }
    ],
    "update_delay": 0
  }'

# Get add memory status by polling the update_id
# Use the "update_id" from previous response
UPDATE_ID=<your_update_id>
curl -X GET "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store/updates/${UPDATE_ID}?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

from azure.ai.projects.models import MemorySearchOptions

# Search memories by a query
query_message = {"role": "user", "content": "What are my coffee preferences?", "type": "message"}

search_response = project_client.beta.memory_stores.search_memories(
    name=memory_store_name,
    scope=scope,
    items=[query_message],
    options=MemorySearchOptions(max_memories=5)
)
print(f"Found {len(search_response.memories)} memories")
for memory in search_response.memories:
    print(f"  - Memory ID: {memory.memory_item.memory_id}, Content: {memory.memory_item.content}")

#pragma warning disable OPENAI001

// Search memories by a query
MemorySearchOptions searchOptions = new(scope)
{
    Items =
    {
        ResponseItem.CreateUserMessageItem(
            "What are my coffee preferences?")
    },
    ResultOptions = new() { MaxMemories = 5 },
};

MemoryStoreSearchResponse searchResponse =
    projectClient.MemoryStores.SearchMemories(
        memoryStoreName: memoryStore.Name,
        options: searchOptions);

Console.WriteLine(
    $"Found {searchResponse.Memories.Count} memories");
foreach (MemorySearchItem item in searchResponse.Memories)
{
    Console.WriteLine(
        $"  - Content: {item.MemoryItem.Content}");
}

const queryMessage: Record<string, unknown> = {
  type: "message",
  role: "user",
  content: [
    { type: "input_text", text: "What are my coffee preferences?" },
  ],
};

console.log("\nSearching memories for stored preferences...");
const searchResponse =
  await project.beta.memoryStores.searchMemories(
    memoryStoreName,
    scope,
    {
      items: [queryMessage],
      options: { max_memories: 5 },
    },
  );

console.log(`Found ${searchResponse.memories.length} memory item(s)`);
for (const memory of searchResponse.memories) {
  console.log(
    `  - Memory ID: ${memory.memory_item.memory_id}, ` +
      `Content: ${memory.memory_item.content}`,
  );
}

import com.azure.ai.agents.models.MemorySearchItem;
import com.azure.ai.agents.models.MemorySearchOptions;
import com.azure.ai.agents.models.MemoryStoreSearchResponse;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseInputItem;
import java.util.Arrays;

ResponseInputItem queryMessage = ResponseInputItem.ofEasyInputMessage(
  EasyInputMessage.builder()
    .role(EasyInputMessage.Role.USER)
    .content("What are my coffee preferences?")
    .build());

MemorySearchOptions searchOptions = new MemorySearchOptions()
  .setMaxMemories(5);

MemoryStoreSearchResponse searchResponse = memoryStoresClient.searchMemories(
  memoryStoreName,
  scope,
  Arrays.asList(queryMessage),
  null,
  searchOptions);

System.out.println("Found " + searchResponse.getMemories().size() + " memories");
for (MemorySearchItem item : searchResponse.getMemories()) {
  System.out.println(
    "  - Memory ID: " + item.getMemoryItem().getMemoryId() + ", Content: "
      + item.getMemoryItem().getContent());
}

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store:search_memories?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "scope": "user_123",
    "items": [
      {
        "type": "message",
        "role": "user",
        "content": [
          {
            "type": "input_text",
            "text": "What are my coffee preferences?"
          }
        ]
      }
    ],
    "options": {
      "max_memories": 5
    }
  }'

# Create a memory item directly
created = project_client.beta.memory_stores.create_memory(
    name=memory_store_name,
    scope="defaultUser",
    content="User prefers concise changelogs with impact-first summaries.",
    kind="user_profile",
)

print(f"Memory ID: {created.memory_id}")
print(f"Content: {created.content}")
print(f"Kind: {created.kind}")

// This code snippet is currently unavailable.

// Create a memory item directly
const created = await project.beta.memoryStores.createMemory(
  memoryStoreName,
  "defaultUser",
  "User prefers concise changelogs with impact-first summaries.",
  "user_profile",
);

console.log(`Memory ID: ${created.memory_id}`);
console.log(`Content: ${created.content}`);
console.log(`Kind: ${created.kind}`);

import com.azure.ai.agents.models.MemoryItem;
import com.azure.ai.agents.models.MemoryItemKind;

MemoryItem created = memoryStoresClient.createMemory(
  memoryStoreName,
  "defaultUser",
  "User prefers concise changelogs with impact-first summaries.",
  MemoryItemKind.USER_PROFILE);

System.out.println("Memory ID: " + created.getMemoryId());
System.out.println("Content: " + created.getContent());
System.out.println("Kind: " + created.getKind());

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store/items?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "scope": "defaultUser",
    "content": "User prefers concise changelogs with impact-first summaries.",
    "kind": "user_profile"
  }'

# Retrieve a memory item by ID
item = project_client.beta.memory_stores.get_memory(
    name=memory_store_name,
    memory_id="<memory-item-id>",
)

print(f"Memory ID: {item.memory_id}")
print(f"Content: {item.content}")
print(f"Kind: {item.kind}")

// This code snippet is currently unavailable.

// Retrieve a memory item by ID
const item = await project.beta.memoryStores.getMemory(
  memoryStoreName,
  "<memory-item-id>",
);

console.log(`Memory ID: ${item.memory_id}`);
console.log(`Content: ${item.content}`);
console.log(`Kind: ${item.kind}`);

import com.azure.ai.agents.models.MemoryItem;

MemoryItem memItem = memoryStoresClient.getMemory(
  memoryStoreName,
  "<memory-item-id>");

System.out.println("Memory ID: " + memItem.getMemoryId());
System.out.println("Content: " + memItem.getContent());
System.out.println("Kind: " + memItem.getKind());

curl -X GET "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store/items/<memory-item-id>?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

# List all memory items in the store
memories = project_client.beta.memory_stores.list_memories(
    name=memory_store_name,
    scope="defaultUser",
)

count = 0
for item in memories:
    count += 1
    print(f"- {item.memory_id} [{item.kind}]: {item.content}")

print(f"Total memories: {count}")

// This code snippet is currently unavailable.

// List all memory items in the store
const memoriesList = project.beta.memoryStores.listMemories(
  memoryStoreName,
  "defaultUser",
);

let count = 0;
for await (const item of memoriesList) {
  count += 1;
  console.log(`- ${item.memory_id} [${item.kind}]: ${item.content}`);
}
console.log(`Total memories: ${count}`);

import com.azure.ai.agents.models.ListMemoriesOptions;
import com.azure.ai.agents.models.MemoryItem;

ListMemoriesOptions options = new ListMemoriesOptions(
  memoryStoreName,
  "defaultUser");

int count = 0;
for (MemoryItem memoryEntry : memoryStoresClient.listMemories(options)) {
  count++;
  System.out.println(
    "- " + memoryEntry.getMemoryId() + " [" + memoryEntry.getKind() + "]: "
      + memoryEntry.getContent());
}

System.out.println("Total memories: " + count);

curl -X GET "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store/items:list?scope=user_123&api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

# Update a memory item by ID
updated = project_client.beta.memory_stores.update_memory(
    name=memory_store_name,
    memory_id="<memory-item-id>",
    content="User prefers detailed technical explanations with examples.",
)

print(f"Updated: {updated.content}")

// This code snippet is currently unavailable.

// Update a memory item by ID
const updated = await project.beta.memoryStores.updateMemory(
  memoryStoreName,
  "<memory-item-id>",
  "User prefers detailed technical explanations with examples.",
);

console.log(`Updated: ${updated.content}`);

import com.azure.ai.agents.models.MemoryItem;

MemoryItem updated = memoryStoresClient.updateMemory(
  memoryStoreName,
  "<memory-item-id>",
  "User prefers detailed technical explanations with examples.");

System.out.println("Updated: " + updated.getContent());

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store/items/<memory-item-id>?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{"content": "User prefers detailed technical explanations with examples."}'

# Delete a memory item by ID
project_client.beta.memory_stores.delete_memory(
    name=memory_store_name,
    memory_id="<memory-item-id>",
)

print("Memory item deleted successfully")

// This code snippet is currently unavailable.

// Delete a memory item by ID
await project.beta.memoryStores.deleteMemory(
  memoryStoreName,
  "<memory-item-id>",
);

console.log("Memory item deleted successfully");

memoryStoresClient.deleteMemory(memoryStoreName, "<memory-item-id>");

System.out.println("Memory item deleted successfully");

curl -X DELETE "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store/items/<memory-item-id>?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

# Delete memories for a specific scope
project_client.beta.memory_stores.delete_scope(
    name=memory_store_name,
    scope="user_123"
)

print(f"Deleted memories for scope: user_123")

// Delete memories for a specific scope
MemoryStoreDeleteScopeResponse deleteScopeResponse =
    projectClient.MemoryStores.DeleteScope(
        name: memoryStore.Name,
        scope: "user_123");

Console.WriteLine(
    $"Deleted scope: {deleteScopeResponse.Name}, "
    + $"success: {deleteScopeResponse.IsDeleted}");

console.log("\nDeleting memories for scope...");
await project.beta.memoryStores.deleteScope(memoryStoreName, scope);

memoryStoresClient.deleteScope(memoryStoreName, "user_123");

System.out.println("Deleted memories for scope: user_123");

curl -X POST "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store:delete_scope?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "scope": "user_123"
  }'

# Delete the entire memory store
delete_response = project_client.beta.memory_stores.delete(memory_store_name)
print(f"Deleted memory store: {delete_response.deleted}")

// Delete the entire memory store
DeleteMemoryStoreResponse deleteResponse =
    projectClient.MemoryStores.DeleteMemoryStore(
        name: memoryStore.Name);

Console.WriteLine(
    $"Deleted memory store: {deleteResponse.Name}, "
    + $"success: {deleteResponse.IsDeleted}");

console.log("Deleting memory store...");
await project.beta.memoryStores.delete(memoryStoreName);

memoryStoresClient.deleteMemoryStore(memoryStoreName);

System.out.println("Deleted memory store: " + memoryStoreName);

curl -X DELETE "${FOUNDRY_PROJECT_ENDPOINT}/memory_stores/my_memory_store?api-version=${API_VERSION}" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

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