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azure-ai-agents-persistent-java

azure-ai-agents-persistent-java

github.com/sickn33Agent workflows48.7k104 installsUpdated today
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About azure-ai-agents-persistent-java

A low-level Java SDK for creating and managing persistent AI agents through Azure AI Agents. It provides synchronous and asynchronous clients for agent administration, threads, messages, and runs.

Key features

  1. 01Create and manage persistent agents
  2. 02Use sync or async clients
  3. 03Create threads and messages
  4. 04Start runs and inspect status
  5. 05Delete threads and agents

Use cases

  1. 01Build Java applications with persistent agents
  2. 02Manage agent conversations across sessions
  3. 03Handle agent runs and their outcomes

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill azure-ai-agents-persistent-java

After installing, type/azure-ai-agents-persistent-javain Claude Code to confirm it loaded.

Details

From the project README

Azure AI Agents Persistent SDK for Java

Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.

Installation

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-agents-persistent</artifactId>
    <version>1.0.0-beta.2</version>
</dependency>

Environment Variables

export PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
export MODEL_DEPLOYMENT_NAME="gpt-4o-mini"

Authentication

import com.azure.ai.agents.persistent.PersistentAgentsClient;
import com.azure.ai.agents.persistent.PersistentAgentsClientBuilder;
import com.azure.ai.agents.persistent.*;
import com.azure.ai.agents.persistent.models.*;
import com.azure.core.http.rest.PagedIterable;
import com.azure.identity.DefaultAzureCredentialBuilder;

String endpoint = System.getenv("PROJECT_ENDPOINT");
PersistentAgentsClient client = new PersistentAgentsClientBuilder()
    .endpoint(endpoint)
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildClient();
String modelDeploymentName = System.getenv("MODEL_DEPLOYMENT_NAME");
PersistentAgentsAdministrationClient administration = client.getPersistentAgentsAdministrationClient();
ThreadsClient threads = client.getThreadsClient();
MessagesClient messageClient = client.getMessagesClient();
RunsClient runs = client.getRunsClient();

Key Concepts

The Azure AI Agents Persistent SDK provides a low-level API for managing persistent agents that can be reused across sessions.

Client Hierarchy
ClientPurpose
PersistentAgentsClientSync client for agent operations
PersistentAgentsAsyncClientAsync client for agent operations

Core Workflow

1. Create Agent
// Create agent with tools
PersistentAgent agent = administration.createAgent(
    new CreateAgentOptions(modelDeploymentName)
        .setName("Math Tutor")
        .setInstructions("You are a personal math tutor.")
);
2. Create Thread
PersistentAgentThread thread = threads.createThread();
3. Add Message
messageClient.createMessage(
    thread.getId(),
    MessageRole.USER,
    "I need help with equations."
);
4. Run Agent
ThreadRun run = runs.createRun(new CreateRunOptions(thread.getId(), agent.getId()));

// Bound polling; a terminal non-success state is not a completed answer.
long deadline = System.nanoTime() + java.util.concurrent.TimeUnit.SECONDS.toNanos(60);
while (run.getStatus() == RunStatus.QUEUED || run.getStatus() == RunStatus.IN_PROGRESS) {
    if (System.nanoTime() > deadline) {
        throw new IllegalStateException("Run polling timed out; inspect/cancel the owned run separately");
    }
    Thread.sleep(500);
    run = runs.getRun(thread.getId(), run.getId());
}

Check the resulting status before reading a final response: requires_action needs an authorized tool-output handler; failed, cancelled, expired or other non-success states must be reported explicitly. A polling timeout does not cancel the cloud run.

5. Get Response
PagedIterable<ThreadMessage> messages = messageClient.listMessages(thread.getId());
for (ThreadMessage message : messages) {
    System.out.println("Message role: " + message.getRole()); // Do not log retained prompt/response payloads.
}
6. Cleanup
threads.deleteThread(thread.getId());
administration.deleteAgent(agent.getId());

Best Practices

  1. Use DefaultAzureCredential for production authentication
  2. Poll with appropriate delays — 500ms recommended between status checks
  3. Clean up resources — Delete threads and agents when done
  4. Handle all run statuses — Check for RequiresAction, Failed, Cancelled
  5. Use async client for better throughput in high-concurrency scenarios

Error Handling

import com.azure.core.exception.HttpResponseException;

try {
    PersistentAgent exampleAgent = administration.createAgent(
        new CreateAgentOptions(modelDeploymentName).setName("Example").setInstructions("Answer briefly"));
} catch (HttpResponseException e) {
    System.err.println("Error: " + e.getResponse().getStatusCode() + " - " + e.getMessage());
}

Reference Links

ResourceURL
Maven Packagehttps://central.sonatype.com/artifact/com.azure/azure-ai-agents-persistent
GitHub Sourcehttps://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-agents-persistent

When to Use

Use when implementing or maintaining Java persistent-agent threads, messages, runs and administration with the preview SDK. The reviewed example uses beta.2 subclients, not direct CRUD methods on PersistentAgentsClient.

Limitations

  • Operations create billable cloud resources, send prompts and retain thread data. Use an authorized project/model and explicit budgets; delete only resources created for the current run, preserve IDs on failure, and redact message/credential logs.
  • Snippets are sequential fragments inside application code, not a standalone compiled program. API shape was checked against Microsoft’s beta.2 sample and preview reference; no cloud run was performed. Add compatible azure-identity and required dependencies using the project’s reviewed Azure BOM.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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