Building Agents

The ToolLoopAgent provides a structured way to encapsulate LLM configuration, tools, and behavior into reusable components. It handles the agent loop for you, allowing the LLM to call tools multiple times in sequence to accomplish complex tasks. Define agents once and use them across your application.

Why Use the ToolLoopAgent Class?

When building AI applications, you often need to:

Reuse configurations - Same model settings, tools, and prompts across different parts of your application
Maintain consistency - Ensure the same behavior and capabilities throughout your codebase
Simplify API routes - Reduce boilerplate in your endpoints
Type safety - Get full TypeScript support for your agent's tools and outputs

The ToolLoopAgent class provides a single place to define your agent's behavior.

Creating an Agent

Define an agent by instantiating the ToolLoopAgent class with your desired configuration:

import { ToolLoopAgent } from 'ai';
__PROVIDER_IMPORT__;

const myAgent = new ToolLoopAgent({
  model: __MODEL__,
  instructions: 'You are a helpful assistant.',
  tools: {
    // Your tools here
  },
});

Configuration Options

The ToolLoopAgent accepts all the same settings as generateText and streamText. Configure:

Model and System Instructions

import { ToolLoopAgent } from 'ai';
__PROVIDER_IMPORT__;

const agent = new ToolLoopAgent({
  model: __MODEL__,
  instructions: 'You are an expert software engineer.',
});

Tools

Provide tools that the agent can use to accomplish tasks:

import { ToolLoopAgent, tool } from 'ai';
__PROVIDER_IMPORT__;
import { z } from 'zod';

const codeAgent = new ToolLoopAgent({
  model: __MODEL__,
  tools: {
    runCode: tool({
      description: 'Execute Python code',
      inputSchema: z.object({
        code: z.string(),
      }),
      execute: async ({ code }) => {
        // Execute code and return result
        return { output: 'Code executed successfully' };
      },
    }),
  },
});

Loop Control

By default, agents run for 20 steps (stopWhen: stepCountIs(20)). In each step, the model either generates text or calls a tool. If it generates text, the agent completes. If it calls a tool, the AI SDK executes that tool.

To let agents call multiple tools in sequence, configure stopWhen to allow more steps. After each tool execution, the agent triggers a new generation where the model can call another tool or generate text:

import { ToolLoopAgent, stepCountIs } from 'ai';
__PROVIDER_IMPORT__;

const agent = new ToolLoopAgent({
  model: __MODEL__,
  stopWhen: stepCountIs(20), // Allow up to 20 steps
});

Each step represents one generation (which results in either text or a tool call). The loop continues until:

A finish reasoning other than tool-calls is returned, or
A tool that is invoked does not have an execute function, or
A tool call needs approval, or
A stop condition is met

You can combine multiple conditions:

import { ToolLoopAgent, stepCountIs } from 'ai';
__PROVIDER_IMPORT__;

const agent = new ToolLoopAgent({
  model: __MODEL__,
  stopWhen: [
    stepCountIs(20), // Maximum 20 steps
    yourCustomCondition(), // Custom logic for when to stop
  ],
});

Learn more about loop control and stop conditions.

Tool Choice

Control how the agent uses tools:

import { ToolLoopAgent } from 'ai';
__PROVIDER_IMPORT__;

const agent = new ToolLoopAgent({
  model: __MODEL__,
  tools: {
    // your tools here
  },
  toolChoice: 'required', // Force tool use
  // or toolChoice: 'none' to disable tools
  // or toolChoice: 'auto' (default) to let the model decide
});

You can also force the use of a specific tool:

import { ToolLoopAgent } from 'ai';
__PROVIDER_IMPORT__;

const agent = new ToolLoopAgent({
  model: __MODEL__,
  tools: {
    weather: weatherTool,
    cityAttractions: attractionsTool,
  },
  toolChoice: {
    type: 'tool',
    toolName: 'weather', // Force the weather tool to be used
  },
});

Structured Output

Define structured output schemas:

import { ToolLoopAgent, Output, stepCountIs } from 'ai';
__PROVIDER_IMPORT__;
import { z } from 'zod';

const analysisAgent = new ToolLoopAgent({
  model: __MODEL__,
  output: Output.object({
    schema: z.object({
      sentiment: z.enum(['positive', 'neutral', 'negative']),
      summary: z.string(),
      keyPoints: z.array(z.string()),
    }),
  }),
  stopWhen: stepCountIs(10),
});

const { output } = await analysisAgent.generate({
  prompt: 'Analyze customer feedback from the last quarter',
});

Define Agent Behavior with System Instructions

System instructions define your agent's behavior, personality, and constraints. They set the context for all interactions and guide how the agent responds to user queries and uses tools.

Basic System Instructions

Set the agent's role and expertise:

const agent = new ToolLoopAgent({
  model: __MODEL__,
  instructions:
    'You are an expert data analyst. You provide clear insights from complex data.',
});

Detailed Behavioral Instructions

Provide specific guidelines for agent behavior:

const codeReviewAgent = new ToolLoopAgent({
  model: __MODEL__,
  instructions: `You are a senior software engineer conducting code reviews.

  Your approach:
  - Focus on security vulnerabilities first
  - Identify performance bottlenecks
  - Suggest improvements for readability and maintainability
  - Be constructive and educational in your feedback
  - Always explain why something is an issue and how to fix it`,
});

Constrain Agent Behavior

Set boundaries and ensure consistent behavior:

const customerSupportAgent = new ToolLoopAgent({
  model: __MODEL__,
  instructions: `You are a customer support specialist for an e-commerce platform.

  Rules:
  - Never make promises about refunds without checking the policy
  - Always be empathetic and professional
  - If you don't know something, say so and offer to escalate
  - Keep responses concise and actionable
  - Never share internal company information`,
  tools: {
    checkOrderStatus,
    lookupPolicy,
    createTicket,
  },
});

Tool Usage Instructions

Guide how the agent should use available tools:

const researchAgent = new ToolLoopAgent({
  model: __MODEL__,
  instructions: `You are a research assistant with access to search and document tools.

  When researching:
  1. Always start with a broad search to understand the topic
  2. Use document analysis for detailed information
  3. Cross-reference multiple sources before drawing conclusions
  4. Cite your sources when presenting information
  5. If information conflicts, present both viewpoints`,
  tools: {
    webSearch,
    analyzeDocument,
    extractQuotes,
  },
});

Format and Style Instructions

Control the output format and communication style:

const technicalWriterAgent = new ToolLoopAgent({
  model: __MODEL__,
  instructions: `You are a technical documentation writer.

  Writing style:
  - Use clear, simple language
  - Avoid jargon unless necessary
  - Structure information with headers and bullet points
  - Include code examples where relevant
  - Write in second person ("you" instead of "the user")

  Always format responses in Markdown.`,
});

Using an Agent

Once defined, you can use your agent in three ways:

Generate Text

Use generate() for one-time text generation:

const result = await myAgent.generate({
  prompt: 'What is the weather like?',
});

console.log(result.text);

Stream Text

Use stream() for streaming responses:

const result = await myAgent.stream({
  prompt: 'Tell me a story',
});

for await (const chunk of result.textStream) {
  console.log(chunk);
}

Respond to UI Messages

Use createAgentUIStreamResponse() to create API responses for client applications:

// In your API route (e.g., app/api/chat/route.ts)
import { createAgentUIStreamResponse } from 'ai';

export async function POST(request: Request) {
  const { messages } = await request.json();

  return createAgentUIStreamResponse({
    agent: myAgent,
    uiMessages: messages,
  });
}

Lifecycle Callbacks

<Note type="warning"> Experimental callbacks are subject to breaking changes in incremental package releases. </Note>

Agents provide lifecycle callbacks that let you hook into different phases of the agent execution. These are useful for logging, observability, debugging, and custom telemetry.

const result = await myAgent.generate({
  prompt: 'Research and summarize the latest AI trends',

  experimental_onStart({ model, functionId }) {
    console.log('Agent started', { model: model.modelId, functionId });
  },

  experimental_onStepStart({ stepNumber, model }) {
    console.log(`Step ${stepNumber} starting`, { model: model.modelId });
  },

  experimental_onToolCallStart({ toolCall }) {
    console.log(`Tool call starting: ${toolCall.toolName}`);
  },

  experimental_onToolCallFinish({ toolCall, durationMs, success }) {
    console.log(`Tool call finished: ${toolCall.toolName} (${durationMs}ms)`, {
      success,
    });
  },

  onStepFinish({ stepNumber, usage, finishReason, toolCalls }) {
    console.log(`Step ${stepNumber} completed:`, {
      inputTokens: usage.inputTokens,
      outputTokens: usage.outputTokens,
      finishReason,
      toolsUsed: toolCalls?.map(tc => tc.toolName),
    });
  },

  onFinish({ totalUsage, steps }) {
    console.log('Agent finished:', {
      totalSteps: steps.length,
      totalTokens: totalUsage.totalTokens,
    });
  },
});

The available lifecycle callbacks are:

experimental_onStart: Called once when the agent operation begins, before any LLM calls. Receives model info, prompt, settings, and telemetry metadata.
experimental_onStepStart: Called before each step (LLM call). Receives the step number, model, messages being sent, tools, and prior steps.
experimental_onToolCallStart: Called right before a tool's execute function runs. Receives the tool call object with tool name, call ID, and input.
experimental_onToolCallFinish: Called right after a tool's execute function completes or errors. Receives the tool call, durationMs, and a success discriminator (output when successful, error when failed).
onStepFinish: Called after each step finishes. Receives step results including usage, finish reason, and tool calls.
onFinish: Called when all steps are finished and the response is complete. Receives all step results, total usage, and telemetry metadata.

Constructor vs. Method Callbacks

All lifecycle callbacks can be defined in the constructor for agent-wide tracking, in the generate()/stream() call for per-call tracking, or both. When both are provided, both are called (constructor first, then the method callback):

const agent = new ToolLoopAgent({
  model: __MODEL__,
  onStepFinish: async ({ stepNumber, usage }) => {
    // Agent-wide logging
    console.log(`Agent step ${stepNumber}:`, usage.totalTokens);
  },
});

// Method-level callback runs after constructor callback
const result = await agent.generate({
  prompt: 'Hello',
  onStepFinish: async ({ stepNumber, usage }) => {
    // Per-call tracking (e.g., for billing)
    await trackUsage(stepNumber, usage);
  },
});

End-to-end Type Safety

You can infer types for your agent's UIMessages:

import { ToolLoopAgent, InferAgentUIMessage } from 'ai';

const myAgent = new ToolLoopAgent({
  // ... configuration
});

// Infer the UIMessage type for UI components or persistence
export type MyAgentUIMessage = InferAgentUIMessage<typeof myAgent>;

Use this type in your client components with useChat:

tsx

'use client';

import { useChat } from '@ai-sdk/react';
import type { MyAgentUIMessage } from '@/agent/my-agent';

export function Chat() {
  const { messages } = useChat<MyAgentUIMessage>();
  // Full type safety for your messages and tools
}

Next Steps

Now that you understand building agents, you can:

Explore workflow patterns for structured patterns using core functions
Learn about loop control for advanced execution control
See manual loop examples for custom workflow implementations