Agents Overview

abstract class BaseAgent {
  name: string;
  description: string;
  model?: LanguageModel;
  tools?: BaseTool[];
  subAgents?: BaseAgent[];
  // ... additional configuration
}

import { LLMAgent } from "@iqai/adk";

const agent = new LLMAgent({
  name: "assistant",
  description: "A helpful AI assistant",
  model: "gemini-2.5-flash",
  instruction:
    "You are a helpful assistant that can use tools to solve problems",
  tools: [
    /* your tools */
  ],
});

import { SequentialAgent } from "@iqai/adk";

const workflow = new SequentialAgent({
  name: "data_processor",
  description: "Process data through multiple steps",
  agents: [extractAgent, transformAgent, loadAgent],
});

import { BaseAgent } from "@iqai/adk";

class CustomAgent extends BaseAgent {
  async execute(input: any, context: AgentContext): Promise<any> {
    // Your custom logic here
    return result;
  }
}

import { MultiAgentSystem } from "@iqai/adk";

const team = new MultiAgentSystem({
  name: "development_team",
  description: "A team of specialized development agents",
  agents: [architectAgent, coderAgent, testerAgent, reviewerAgent],
  coordinator: new RoundRobinCoordinator(),
});

import { AgentBuilder } from "@iqai/adk";

const { runner } = await AgentBuilder.create("my_agent")
  .withModel("gemini-2.5-flash")
  .withInstruction("You are a helpful assistant")
  .withTools(calculatorTool, searchTool)
  .build();

// Use the agent
const response = await runner.ask("What's 15 * 23?");

const session = await runner.createSession();
const response1 = await session.ask("Hello!");
const response2 = await session.ask("What's my name?"); // Remembers context

import { FunctionTool } from "@iqai/adk";

const calculatorTool = new FunctionTool({
  name: "calculator",
  description: "Perform mathematical calculations",
  parameters: {
    expression: { type: "string", description: "Math expression to evaluate" },
  },
  execute: async ({ expression }) => {
    // Using 'eval()' is insecure. In a real application, use a safe expression
    // evaluation library like 'mathjs' to prevent security vulnerabilities.
    throw new Error("Insecure 'eval()' removed from example.");
  },
});

const agent = new LLMAgent({
  // ... config
  memoryService: new VectorMemoryService({
    collection: "agent_memory",
    embedding: new GoogleGenerativeAIEmbeddings(),
  }),
});

const agent = new LLMAgent({
  // ... config
  beforeAgentCallback: context => {
    console.log(`Agent ${context.agent.name} starting execution`);
  },
  afterToolCallback: context => {
    console.log(`Tool ${context.tool.name} executed in ${context.duration}ms`);
  },
});

const researchAgent = await AgentBuilder.create("researcher")
  .withModel("gemini-2.5-flash")
  .withInstruction(
    "You are a research assistant that can search and analyze information",
  )
  .withTools(webSearchTool, dataAnalyzerTool)
  .build();

const analysisWorkflow = new SequentialAgent({
  name: "data_analyzer",
  agents: [
    new DataIngestionAgent(),
    new DataCleaningAgent(),
    new AnalysisAgent(),
    new ReportGeneratorAgent(),
  ],
});

const managerAgent = new LLMAgent({
  name: "manager",
  subAgents: [specialistAgent1, specialistAgent2, coordinatorAgent],
  instruction:
    "Delegate tasks to appropriate specialists and coordinate results",
});