Configuring Call Options

Call options allow you to pass type-safe structured inputs to your agent. Use them to dynamically modify any agent setting based on the specific request.

Why Use Call Options?

When you need agent behavior to change based on runtime context:

Add dynamic context - Inject retrieved documents, user preferences, or session data into prompts
Select models dynamically - Choose faster or more capable models based on request complexity
Configure tools per request - Pass user location to search tools or adjust tool behavior
Customize provider options - Set reasoning effort, temperature, or other provider-specific settings

Without call options, you'd need to create multiple agents or handle configuration logic outside the agent.

How It Works

Define call options in three steps:

Define the schema - Specify what inputs you accept using callOptionsSchema
Configure with prepareCall - Use those inputs to modify agent settings
Pass options at runtime - Provide the options when calling generate() or stream()

Basic Example

Add user context to your agent's prompt at runtime:

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

const supportAgent = new ToolLoopAgent({
  model: __MODEL__,
  callOptionsSchema: z.object({
    userId: z.string(),
    accountType: z.enum(['free', 'pro', 'enterprise']),
  }),
  instructions: 'You are a helpful customer support agent.',
  prepareCall: ({ options, ...settings }) => ({
    ...settings,
    instructions:
      settings.instructions +
      `\nUser context:
- Account type: ${options.accountType}
- User ID: ${options.userId}

Adjust your response based on the user's account level.`,
  }),
});

// Call the agent with specific user context
const result = await supportAgent.generate({
  prompt: 'How do I upgrade my account?',
  options: {
    userId: 'user_123',
    accountType: 'free',
  },
});

The options parameter is now required and type-checked. If you don't provide it or pass incorrect types, TypeScript will error.

Modifying Agent Settings

Use prepareCall to modify any agent setting. Return only the settings you want to change.

Dynamic Model Selection

Choose models based on request characteristics:

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

const agent = new ToolLoopAgent({
  model: __MODEL__, // Default model
  callOptionsSchema: z.object({
    complexity: z.enum(['simple', 'complex']),
  }),
  prepareCall: ({ options, ...settings }) => ({
    ...settings,
    model:
      options.complexity === 'simple' ? 'openai/gpt-4o-mini' : 'openai/o1-mini',
  }),
});

// Use faster model for simple queries
await agent.generate({
  prompt: 'What is 2+2?',
  options: { complexity: 'simple' },
});

// Use more capable model for complex reasoning
await agent.generate({
  prompt: 'Explain quantum entanglement',
  options: { complexity: 'complex' },
});

Dynamic Tool Configuration

Configure tools based on runtime context:

import { openai } from '@ai-sdk/openai';
import { ToolLoopAgent } from 'ai';
__PROVIDER_IMPORT__;
import { z } from 'zod';

const newsAgent = new ToolLoopAgent({
  model: __MODEL__,
  callOptionsSchema: z.object({
    userCity: z.string().optional(),
    userRegion: z.string().optional(),
  }),
  tools: {
    web_search: openai.tools.webSearch(),
  },
  prepareCall: ({ options, ...settings }) => ({
    ...settings,
    tools: {
      web_search: openai.tools.webSearch({
        searchContextSize: 'low',
        userLocation: {
          type: 'approximate',
          city: options.userCity,
          region: options.userRegion,
          country: 'US',
        },
      }),
    },
  }),
});

await newsAgent.generate({
  prompt: 'What are the top local news stories?',
  options: {
    userCity: 'San Francisco',
    userRegion: 'California',
  },
});

Provider-Specific Options

Configure provider settings dynamically:

import { openai, OpenAILanguageModelResponsesOptions } from '@ai-sdk/openai';
import { ToolLoopAgent } from 'ai';
import { z } from 'zod';

const agent = new ToolLoopAgent({
  model: 'openai/o3',
  callOptionsSchema: z.object({
    taskDifficulty: z.enum(['low', 'medium', 'high']),
  }),
  prepareCall: ({ options, ...settings }) => ({
    ...settings,
    providerOptions: {
      openai: {
        reasoningEffort: options.taskDifficulty,
      } satisfies OpenAILanguageModelResponsesOptions,
    },
  }),
});

await agent.generate({
  prompt: 'Analyze this complex scenario...',
  options: { taskDifficulty: 'high' },
});

Advanced Patterns

Retrieval Augmented Generation (RAG)

Fetch relevant context and inject it into your prompt:

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

const ragAgent = new ToolLoopAgent({
  model: __MODEL__,
  callOptionsSchema: z.object({
    query: z.string(),
  }),
  prepareCall: async ({ options, ...settings }) => {
    // Fetch relevant documents (this can be async)
    const documents = await vectorSearch(options.query);

    return {
      ...settings,
      instructions: `Answer questions using the following context:

${documents.map(doc => doc.content).join('\n\n')}`,
    };
  },
});

await ragAgent.generate({
  prompt: 'What is our refund policy?',
  options: { query: 'refund policy' },
});

The prepareCall function can be async, enabling you to fetch data before configuring the agent.

Combining Multiple Modifications

Modify multiple settings together:

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

const agent = new ToolLoopAgent({
  model: __MODEL__,
  callOptionsSchema: z.object({
    userRole: z.enum(['admin', 'user']),
    urgency: z.enum(['low', 'high']),
  }),
  tools: {
    readDatabase: readDatabaseTool,
    writeDatabase: writeDatabaseTool,
  },
  prepareCall: ({ options, ...settings }) => ({
    ...settings,
    // Upgrade model for urgent requests
    model: options.urgency === 'high' ? __MODEL__ : settings.model,
    // Limit tools based on user role
    activeTools:
      options.userRole === 'admin'
        ? ['readDatabase', 'writeDatabase']
        : ['readDatabase'],
    // Adjust instructions
    instructions: `You are a ${options.userRole} assistant.
${options.userRole === 'admin' ? 'You have full database access.' : 'You have read-only access.'}`,
  }),
});

await agent.generate({
  prompt: 'Update the user record',
  options: {
    userRole: 'admin',
    urgency: 'high',
  },
});

Using with createAgentUIStreamResponse

Pass call options through API routes to your agent:

import { createAgentUIStreamResponse } from 'ai';
import { myAgent } from '@/ai/agents/my-agent';

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

  return createAgentUIStreamResponse({
    agent: myAgent,
    messages,
    options: {
      userId,
      accountType,
    },
  });
}

Next Steps

Learn about loop control for execution management
Explore workflow patterns for complex multi-step processes