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To build an AI agent in Java, connect a language model to a small set of application-defined tools, let the model request a tool when needed, execute that request in Java, and send the result back to the model. Add memory, retrieval, or multi-step orchestration only when your use case needs them. For a known sequence, a code-defined workflow is often the more predictable starting point; use a more dynamic agent when the next step genuinely depends on the model’s decision.
What counts as an AI agent in Java?
A model call takes input and returns output. An agent adds a way to act: the model can request one of the tools your application makes available, inspect the result, and continue until it can answer or reaches a limit you set. Java code—not the model—decides which tools exist and carries out their effects.
Google Developers Codelabs describes agentic AI as systems in which language models use tools, memory, and planning to pursue complex, multi-step goals. Those capabilities are options, not a checklist every Java agent must satisfy. A simple tool-using loop can be useful without persistent memory, a planner, or multiple agents.
That distinction matters for safety and for choosing the right design. The model can propose an action, but your application should validate the request, enforce permissions, call the relevant API, and decide whether approval is required. Avoid treating an agent as trusted Java code or giving a model unrestricted access to credentials and services.
Choose a Java framework that fits your application
There is no universally best Java agent framework established by the official documentation. Start with the framework your service already uses and compare the APIs against the orchestration and integration you need.
| Consideration | LangChain4j | Spring AI |
|---|---|---|
| Best initial fit | A Java-oriented library with integrations for Spring Boot, Quarkus, Helidon, and Micronaut. | Applications already built around Spring APIs and configuration. |
| Programming model | Low-level building blocks, AI Services, and a separate agentic module. | ChatClient and Advisors for composing model calls, tools, memory, and retrieval patterns. |
| Tool handling | Java objects or methods can be exposed as tools; documented agentic patterns include MCP tools. | Tool callbacks can be invoked through the application’s tool-calling flow. |
| Orchestration | AgenticScope can share outputs across documented workflow patterns. | Supports composing calls and tools; its guidance distinguishes known workflows from dynamically directed agents. |
| Interoperability | MCP tools can be wrapped for agentic systems. | MCP APIs support consuming servers or exposing Spring services. |
These are differences in documented approach, not a performance ranking. The available documentation does not establish a controlled comparison of latency, answer quality, cost, or reliability between the frameworks.
When to start with LangChain4j
Consider LangChain4j when you want Java-oriented abstractions that are not tied to Spring, or when AI Services and its agentic module fit your application. AI Services let you describe an interface and use a proxy to handle model interaction, input formatting, output parsing, chat memory, tools, and RAG. Its documentation describes Chains as legacy and says new Chains are not planned, so start with AI Services or the agentic abstractions rather than building a new design around Chains.
When to start with Spring AI
Consider Spring AI when your application already relies on Spring and you want its ChatClient, Advisors, tool integrations, or MCP support. Check the documentation for the Spring AI version you are using before copying an example: the tool loop and APIs can differ by version. In the 2.0.1 reference, ChatClient’s advisor chain can drive the tool loop. A direct ChatModel call does not automatically run that loop.
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Build a minimal tool-using agent loop in Java
The program below is a complete Java 17 example of the control flow. It uses a deterministic demo model so it runs without a provider account or framework dependency: the first model response requests a read-only tool, Java validates and runs it, and the next response provides a final answer. This is a learning skeleton, not a language model client. In a real service, replace DemoModel with a LangChain4j or Spring AI integration while keeping the application-controlled tool boundary and limits.
- Save the code as
AgentDemo.java. - Compile and run it with a JDK 17 or later:
javac AgentDemo.java && java AgentDemo. - Expect output reporting that the service is healthy. The demo tool is deliberately read-only and returns a fixed value.
import java.util.List;
import java.util.Map;
public class AgentDemo {
record ToolCall(String name, Map<String, String> args) {}
record Reply(String text, ToolCall call) {
static Reply finalAnswer(String text) { return new Reply(text, null); }
static Reply toolCall(String name, Map<String, String> args) {
return new Reply(null, new ToolCall(name, args));
}
}
interface Model {
Reply next(String userMessage, List<String> history);
}
interface Tool {
String name();
String run(Map<String, String> args);
}
static final class StatusTool implements Tool {
public String name() { return "service_status"; }
public String run(Map<String, String> args) {
String service = args.get("service");
if (!"catalog".equals(service)) {
throw new IllegalArgumentException("Unknown service");
}
return "catalog: healthy";
}
}
// Deterministic stand-in for a model API; it demonstrates the tool cycle.
static final class DemoModel implements Model {
public Reply next(String userMessage, List<String> history) {
if (history.isEmpty()) {
return Reply.toolCall("service_status", Map.of("service", "catalog"));
}
return Reply.finalAnswer("The catalog service is healthy.");
}
}
static String runAgent(String userMessage, Model model, List<Tool> tools) {
List<String> history = new java.util.ArrayList<>();
int maxSteps = 4;
for (int step = 0; step < maxSteps; step++) {
Reply reply = model.next(userMessage, List.copyOf(history));
if (reply.call() == null) return reply.text();
ToolCall call = reply.call();
Tool tool = tools.stream()
.filter(candidate -> candidate.name().equals(call.name()))
.findFirst()
.orElseThrow(() -> new SecurityException("Tool not allowed"));
String result = tool.run(call.args());
history.add("Tool " + call.name() + " returned: " + result);
}
throw new IllegalStateException("Agent reached its step limit");
}
public static void main(String[] args) {
String answer = runAgent("Is the catalog service healthy?",
new DemoModel(), List.of(new StatusTool()));
System.out.println(answer);
}
}The loop makes the important boundary visible: the model returns a named request and arguments; the application looks up an allowed tool and executes it. A production adapter must also serialize tool definitions and results using the selected framework and model provider’s expected format. Do not pass a Java object or unrestricted API client to the model and assume that makes its use safe.
Turn the skeleton into a real agent
- Choose one model integration. Configure its provider and credentials through the framework and keep secrets in environment or secret-management configuration, not source code. The Google Developers Codelab uses LangChain4j with Google GenAI; its tutorial prerequisites are JDK 17 or higher, Maven 3.5+, and a Gemini API key. Those requirements apply to that tutorial, not every Java agent.
- Expose a narrow tool. Begin with one read-only operation, such as looking up a record by an authorized identifier. Define the accepted arguments and reject missing, malformed, or out-of-scope values.
- Use the framework’s tool-calling path. In Spring AI 2.0.1, configure the ChatClient advisor path that drives tool calls; calling ChatModel directly does not execute the tool loop automatically. In LangChain4j, use its Java tool and AI Services or agentic APIs appropriate to the workflow.
- Set explicit limits. Bound the number of model/tool turns, request duration, input size, and tool output size. Add rate limits and logging suitable for your service without recording secrets or unnecessary personal data.
- Return only what the caller needs. Prefer a typed, validated result for application logic over parsing free-form prose. Structured POJO outputs are also part of the Google Developers LangChain4j tutorial.
Decide whether you need a workflow, memory, RAG, or multiple agents
Use a workflow for a known sequence
If the task always follows the same stages—validate an input, fetch a record, then summarize it—write those stages in Java. The model can still help with language tasks, but the application decides the sequence. Spring AI’s reference guidance notes that workflows often provide better predictability and consistency for well-defined tasks; that is project guidance, not a measured guarantee.
Add dynamic tool selection for uncertain paths
A model-directed loop is useful when the next step depends on what the model learns from an earlier result. Keep the tool catalog focused and provide descriptions that distinguish tools clearly. If the action is consequential, require a user confirmation or a separate policy check before execution.
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Add chat memory only for continuity
Memory can preserve relevant conversational context across turns, but it introduces state-management decisions: what to retain, how long to retain it, how to isolate users, and how to clear or persist it. LangChain4j describes agent memory as optional; its AgenticScope state is transient unless persistence is configured. Do not assume a prior turn will be available unless your application or framework stores it.
Add RAG when answers need a private corpus
Retrieval-augmented generation can supply relevant documents from a private corpus to ground a response. It does not automatically make retrieved content authoritative: enforce access controls during retrieval, identify the source material in the result where appropriate, and handle empty or conflicting matches. LangChain4j and Spring AI document retrieval and vector-store patterns; choose the storage and embedding components according to your data and operational requirements.
Use multiple agents only when separation helps
Separate agents can divide a task, but they add coordination, state, and failure handling. First establish that a single workflow or agent cannot meet a concrete requirement. If you do split work, define each agent’s narrow responsibility and the format of its handoff instead of letting agents recursively delegate without bounds.
Give Java agents tools safely
Tool calling is application behavior, not permission for the model to call arbitrary services. Use a deliberate boundary between model-generated requests and Java operations.
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- Allowlist tools. Register only the operations the current task needs. Reject unknown tool names rather than improvising an execution path.
- Validate arguments. Apply schema, range, and business-rule checks in Java. Treat arguments as untrusted input even when they appear in a model-generated structured call.
- Scope identity and credentials. Authorize against the signed-in user and pass only the permissions required for a particular operation. Never expose API keys, database connections, or broad admin clients to a prompt.
- Gate side effects. For sending messages, changing records, purchases, or other consequential actions, use explicit confirmation or policy approval. Prefer read-only tools while prototyping.
- Control resource use. Cap turns, timeouts, concurrency, and output size. Handle provider errors, tool failures, and cancellation as normal outcomes.
- Keep an audit trail. Record the selected tool, validated arguments or a safe redacted form, outcome, and request correlation ID. Protect logs from sensitive data exposure.
Use MCP when tools need to be shared
The Model Context Protocol (MCP) is an interoperability option when tools should be exposed to or consumed by multiple clients. LangChain4j documents wrapping MCP tools for agentic systems, while Spring AI documents APIs for consuming MCP servers or exposing Spring services. Treat MCP as an integration choice, not a substitute for authorization: each operation still needs application-side validation and access control. Check the current versioned documentation for the exact API supported by your framework release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Optional: let an agent capture a webpage
A screenshot tool is an optional capability for agents that need a visual snapshot of a webpage; it is not required to build a Java agent. You could wrap a screenshot API call behind a narrow Java tool, validate the target URL against an allowlist, and return a screenshot or job result to the agent. Keep network access constrained to avoid turning a URL tool into an unrestricted server-side fetch.
Or skip the browser setup
ScreenshotNeo is a website screenshot API and MCP server. Its one-request API can return a PNG, JPEG, WebP, or PDF. For example, this cURL request saves a WebP screenshot; see the API documentation for parameters and response details.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://example.com -o shot.webpIt accepts cookie or consent banners before capture and removes more than 60 known consent platforms, newsletter popups, and chat widgets; each of those steps can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers indicate the page verdict and billing status. Its MCP server offers take_screenshot, get_page_info, and capture_pdf for AI agents. The free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000 shots.
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Troubleshoot common agent problems
- The model answers but never calls a tool: confirm that the selected framework path exposes tools to the model and that the prompt describes when to use them. In Spring AI 2.0.1, use the ChatClient tool-calling advisor path; a direct ChatModel call does not run the loop automatically.
- The model requests an unknown tool: fail closed, record a safe diagnostic, and inspect the registered names and tool descriptions. Do not execute a similarly named method as a fallback.
- A tool call fails validation: return a concise, non-sensitive error through the framework’s tool-result path so the model can revise its request, or stop if the error indicates an authorization failure. Never silently broaden permissions to make the call succeed.
- The agent loops or repeats a call: set a hard turn limit, ensure tool results are returned to the model, and detect repeated equivalent requests where appropriate. Return a controlled failure when the limit is reached.
- Context appears to disappear between messages: check whether memory is configured and whether the conversation identifier is stable and isolated per user. Transient agent scope is not a persistence mechanism.
- A Spring example does not match your project: verify its Spring AI version and API path. Do not apply 2.0.1 tool-loop instructions unchanged to an older 1.x application.
- Responses are slow or expensive: instrument model calls and tool calls separately, cap turns and output sizes, avoid unnecessary agent delegation, and choose a code-defined workflow when the sequence is already known. No universal framework performance ranking is established by the cited documentation.
Plan for reliability, performance, and cost
An agent can make multiple model calls and invoke tools, so its time and resource use depend on the number of turns, model/provider, context sent, and work performed by tools. Put a deadline and maximum step count around the loop; a request should not wait indefinitely for a model, remote service, or page load. Retry only failures that are safe to retry, and make side-effecting operations idempotent or protected against duplicate execution.
Measure the behavior of your own workload: calls per task, model and tool latency, failure rates, and tokens or provider usage where available. Keep tool output concise so irrelevant data does not consume context. Use retrieval to supply targeted context rather than repeatedly including a large corpus. There is no independently established benchmark here showing that LangChain4j or Spring AI is faster, cheaper, or more reliable in general.
Finally, treat model output as fallible. Test malformed arguments, empty retrieval results, unavailable tools, timeouts, duplicate requests, and user cancellation. A useful agent is one whose failure modes are bounded and visible, not one that is merely capable of chaining many actions.
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Does an AI agent need to plan before it uses a tool?
No. A straightforward model/tool/result loop is enough for many tasks; a separate planning stage is useful only when the task requires it.
Can an agent use a tool without giving the model my API credentials?
Yes. Keep credentials in the Java application and let the model request a narrowly defined operation; your code authorizes and performs it.
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