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skills/ai-sdk-agents/assets/example_coordinator.ts

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+// example_coordinator.ts
+
+/**
+ * This file provides an example implementation of a coordinator agent using the AI SDK.
+ * Coordinator agents are responsible for routing requests to specialized agents and
+ * managing the overall workflow of a multi-agent system.
+ *
+ * This example demonstrates basic routing logic. You can customize this to fit your specific use case.
+ */
+
+import { Agent, AgentContext, AgentOutput, AgentConfig } from "@ai-sdk/core"; // Replace with actual import path if needed
+
+// Define the interface for the agent's input.  Customize this based on what your agents need.
+interface CoordinatorInput {
+  query: string;
+  // Add other relevant input parameters here
+}
+
+// Define the interface for the agent's output. Customize this based on your needs.
+interface CoordinatorOutput extends AgentOutput {
+  response: string;
+  routedToAgent?: string; // Optional: Indicate which agent handled the request
+}
+
+// Define the configuration options for the coordinator agent.
+interface CoordinatorConfig extends AgentConfig {
+  // Add any configuration options specific to the coordinator agent here, such as:
+  // - List of available agents and their descriptions
+  // - Routing rules
+  // - Error handling strategies
+  agent1: Agent; // Define agent1
+  agent2: Agent; // Define agent2
+}
+
+// Implement the coordinator agent class.
+class CoordinatorAgent implements Agent<CoordinatorInput, CoordinatorOutput, CoordinatorConfig> {
+  config: CoordinatorConfig;
+
+  constructor(config: CoordinatorConfig) {
+    this.config = config;
+  }
+
+  async execute(input: CoordinatorInput, context: AgentContext): Promise<CoordinatorOutput> {
+    const { query } = input;
+
+    // Implement your routing logic here.  This is a simplified example.
+    // Consider using more sophisticated methods like:
+    // - Natural language understanding to determine the intent of the query
+    // - A knowledge base to match the query to the appropriate agent
+    // - A machine learning model to predict the best agent to handle the request
+
+    let routedToAgent: Agent | null = null;
+    let agentName: string | undefined = undefined;
+
+    if (query.toLowerCase().includes("agent1")) {
+      routedToAgent = this.config.agent1;
+      agentName = "Agent1";
+    } else if (query.toLowerCase().includes("agent2")) {
+      routedToAgent = this.config.agent2;
+      agentName = "Agent2";
+    } else {
+      // Default routing logic - you should customize this
+      routedToAgent = this.config.agent1; // Example: Default to agent1
+      agentName = "Agent1";
+    }
+
+
+    if (!routedToAgent) {
+      return {
+        response: "Error: No suitable agent found to handle the request.",
+      };
+    }
+
+    // Execute the selected agent.
+    const agentOutput: any = await routedToAgent.execute({ query: query }, context); // Replace 'any' with the actual expected output type from the agent.
+
+    // Process the output from the selected agent and format it as the coordinator's output.
+    return {
+      response: `[${agentName ?? "Unknown Agent"}] ${agentOutput.response}`, // Customize formatting as needed
+      routedToAgent: agentName,
+      // Include any other relevant information in the output
+    };
+  }
+}
+
+export { CoordinatorAgent, CoordinatorInput, CoordinatorOutput, CoordinatorConfig };
+
+// Example Usage (in another file):
+// import { CoordinatorAgent, CoordinatorConfig } from './example_coordinator';
+// import { Agent1, Agent2 } from './your_agents'; // Assuming you have Agent1 and Agent2 defined
+
+// const config: CoordinatorConfig = {
+//   agent1: new Agent1(),
+//   agent2: new Agent2(),
+//   // ... other configuration options
+// };
+
+// const coordinator = new CoordinatorAgent(config);
+// const result = await coordinator.execute({ query: "Route this to agent1" }, context);
+// console.log(result.response);