Google Agent ADK 框架，20分钟，帮你做一个简单智能体！

server:
  port: 8901

# 可申请API秘钥；https://ai.google.dev/gemini-api
google:
  api:
    base-url: https://generativelanguage.googleapis.com
    key: AIzaSyDF6JnvFx7xWEsARS*******可以自己申请免费的
  cloud:
    project: xfg-google-adk

logging:
  level:
    root: info
  config: classpath:logback-spring.xml

import com.google.adk.agents.LlmAgent;
import com.google.adk.events.Event;
import com.google.adk.models.Gemini;
import com.google.adk.runner.InMemoryRunner;
import com.google.adk.sessions.Session;
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.HttpOptions;
import com.google.genai.types.Part;
import io.reactivex.rxjava3.core.Flowable;

publicclass ApiTest {

    /**
     * 可申请免费测试api
     * https://ai.google.dev/gemini-api/docs/quickstart?hl=zh-cn#apps-script
     */
    public static void main(String[] args) {
        LlmAgent agent = LlmAgent.builder()
                .name("test")
                .description("test agent help user do work")
                .model(Gemini.builder()
                        .apiClient(Client.builder()
                                .apiKey("AIzaSyDF6JnvFx7xWEsARSGosNmvTU3ZoCwo-mc")
                                .httpOptions(HttpOptions
                                        .builder()
                                        .baseUrl("https://generativelanguage.googleapis.com")
                                        .timeout(500000)
                                        .build())
                                .build())
                        .modelName("gemini-2.0-flash")
                        .build())
                .build();

        InMemoryRunner runner = new InMemoryRunner(agent);

        Session session = runner
                .sessionService()
                .createSession("test", "xiaofuge")
                .blockingGet();

        Flowable<Event> events = runner.runAsync("xiaofuge", session.id(), Content.fromParts(Part.fromText("hi agent can you help me")));

        System.out.print("\nAgent > ");
        events.blockingForEach(event -> System.out.println(event.stringifyContent()));

    }

}

Agent > Hi there! Yes, I'm here to help.  To best assist you, could you tell me what you need help with?  The more information you give me, the better I can understand your request and provide a useful response.

@Slf4j
@RunWith(SpringRunner.class)
@SpringBootTest
public class AutoAgentTest {

    @Value("${google.api.base-url}")
    private String baseUrl;

    @Value("${google.api.key}")
    private String apiKey;

    privatestaticfinal String USER_ID = "xiaofuge";
    privatestaticfinal String NAME = "multi_tool_agent";

    publicstatic BaseAgent agent;

    @Before
    public void init() {
        List<McpTool> mcpTools = new ArrayList<>();
        mcpTools.addAll(mcp_elk());
        mcpTools.addAll(mcp_filesystem());

        agent = LlmAgent.builder()
                .name(NAME)
                .model(Gemini.builder()
                        .apiClient(Client.builder()
                                .apiKey(apiKey)
                                .httpOptions(HttpOptions
                                        .builder()
                                        .baseUrl(baseUrl)
                                        .timeout(500000)
                                        .build())
                                .build())
                        .modelName("gemini-2.0-flash")
                        .build())
                .description("You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.")
                .instruction(
                        """
                                 You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.
                                
                                 可以在这里复制全部提示词；https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools/blob/main/Claude%20Code/claude-code-system-prompt.txt
                                """)
                .tools(mcpTools)
                .build();
    }

    /**
     * - 需要配置后，才能在单测控制台输入内容
     * IntelliJ IDEA Help -> Edit Custom VM Options -> -Deditable.java.test.console=true
     * <br/>
     * - <a href="https://ai.google.dev/api">ai.google.dev/api</a>
     */
    @Test
    public void test_agent() {
        InMemoryRunner runner = new InMemoryRunner(agent);

        Session session =
                runner
                        .sessionService()
                        .createSession(NAME, USER_ID)
                        .blockingGet();

        try (Scanner scanner = new Scanner(System.in, StandardCharsets.UTF_8)) {
            while (true) {
                System.out.print("\nYou > ");
                String userInput = scanner.nextLine();

                if ("quit".equalsIgnoreCase(userInput)) {
                    break;
                }

                Content userMsg = Content.fromParts(Part.fromText(userInput));
                Flowable<Event> events = runner.runAsync(USER_ID, session.id(), userMsg);

                System.out.print("\nAgent > ");
                events.blockingForEach(event -> System.out.println(event.stringifyContent()));
            }
        }

    }

    private List<McpTool> mcp_elk() {
        Map<String, String> env = new HashMap<>();
        env.put("ES_HOST", "http://127.0.0.1:9200");
        env.put("ES_API_KEY", "none");

        ServerParameters mcp_elk = ServerParameters.builder("npx")
                .args(List.of(
                        "-y",
                        "@awesome-ai/elasticsearch-mcp"
                ))
                .env(env)
                .build();

        CompletableFuture<McpToolset.McpToolsAndToolsetResult> futureResult =
                McpToolset.fromServer(mcp_elk, JsonBaseModel.getMapper());

        McpToolset.McpToolsAndToolsetResult result = futureResult.join();

        return result.getTools();
    }

    private List<McpTool> mcp_filesystem() {
        ServerParameters mcp_filesystem = ServerParameters.builder("npx")
                .args(List.of(
                        "-y",
                        "@modelcontextprotocol/server-filesystem",
                        "/Users/fuzhengwei/Desktop"
                ))
                .build();

        CompletableFuture<McpToolset.McpToolsAndToolsetResult> futureResult =
                McpToolset.fromServer(mcp_filesystem, JsonBaseModel.getMapper());

        McpToolset.McpToolsAndToolsetResult result = futureResult.join();

        return result.getTools();
    }

}

@Slf4j
@RestController
@RequestMapping("/trigger")
@CrossOrigin(origins = "*")
publicclass AgentController {

    privatefinal AgentService agentService;

    public AgentController(AgentService agentService) {
        this.agentService = agentService;
    }

    @PostMapping(path = "/session", consumes = MediaType.APPLICATION_JSON_VALUE, produces = MediaType.APPLICATION_JSON_VALUE)
    public CreateSessionResponse createSession(@RequestBody CreateSessionRequest req) {
        String sessionId = agentService.createOrGetSession(req.getUserId());
        log.info("创建会话ID:{}", sessionId);
        returnnew CreateSessionResponse(sessionId);
    }

    @PostMapping(path = "/chat", consumes = MediaType.APPLICATION_JSON_VALUE, produces = MediaType.APPLICATION_JSON_VALUE)
    public ChatResponse chat(@RequestBody ChatRequest req) {
        String sessionId = req.getSessionId();
        if (sessionId == null || sessionId.isEmpty()) {
            sessionId = agentService.createOrGetSession(req.getUserId());
        }
        log.info("使用会话ID:{}", sessionId);
        List<String> outputs = agentService.chat(req.getUserId(), sessionId, req.getMessage());
        returnnew ChatResponse(sessionId, String.join("\n", outputs));
    }
}