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Tools extend agent capabilities by allowing them to interact with external systems, perform computations, or access data. The agent framework is fully compatible with Vercel AI SDK tools.

​
Basic Tool Integration

Add tools to an agent using the tools configuration: 
import { openai } from '@ai-sdk/openai';
import { agent, execute } from '@deepagents/agent';
import { tool } from 'ai';
import { z } from 'zod';

const weatherTool = tool({
  description: 'Get weather for a location',
  parameters: z.object({
    location: z.string(),
  }),
  execute: async ({ location }) => {
    // Simulate weather API call
    return { temperature: 72, condition: 'sunny', location };
  },
});

const weatherAgent = agent({
  name: 'weather_agent',
  model: openai('gpt-4o'),
  prompt: 'You help users check the weather.',
  tools: { weather: weatherTool },
});

const stream = await execute(weatherAgent, 'What is the weather in Tokyo?', {});
console.log(await stream.text);

​
Tool Parameters

Define tool parameters using Zod schemas: 
import { tool } from 'ai';
import { z } from 'zod';

const searchTool = tool({
  description: 'Search for information on the web',
  parameters: z.object({
    query: z.string().describe('The search query'),
    limit: z.number().optional().describe('Maximum results to return'),
    includeImages: z.boolean().default(false),
  }),
  execute: async ({ query, limit = 10, includeImages }) => {
    // Perform search
    return {
      results: [/* search results */],
      count: 10,
    };
  },
});

​
Accessing Context in Tools

Tools can access context variables using toState(): 
import { tool } from 'ai';
import { z } from 'zod';
import { toState } from '@deepagents/agent';

interface AppContext {
  userId: string;
  apiKey: string;
  preferences: Record<string, string>;
}

const savePreferenceTool = tool({
  description: 'Save user preference',
  parameters: z.object({
    key: z.string(),
    value: z.string(),
  }),
  execute: async ({ key, value }, options) => {
    const ctx = toState<AppContext>(options);
    
    // Access context
    console.log('User ID:', ctx.userId);
    
    // Mutate context
    ctx.preferences[key] = value;
    
    return `Saved ${key}=${value} for user ${ctx.userId}`;
  },
});

const agent = agent<unknown, AppContext>({
  name: 'assistant',
  model: openai('gpt-4o'),
  prompt: 'Help the user manage their preferences.',
  tools: { savePreference: savePreferenceTool },
});

const context: AppContext = {
  userId: 'user123',
  apiKey: 'secret',
  preferences: {},
};

const stream = await execute(agent, 'Set my theme to dark', context);
await stream.text;

console.log(context.preferences); // { theme: 'dark' }

​
Multiple Tools

Agents can have multiple tools: 
import { openai } from '@ai-sdk/openai';
import { agent, execute } from '@deepagents/agent';
import { tool } from 'ai';
import { z } from 'zod';

const tools = {
  getCurrentTime: tool({
    description: 'Get the current time',
    parameters: z.object({}),
    execute: async () => {
      return new Date().toISOString();
    },
  }),
  
  calculateAge: tool({
    description: 'Calculate age from birth year',
    parameters: z.object({
      birthYear: z.number(),
    }),
    execute: async ({ birthYear }) => {
      const currentYear = new Date().getFullYear();
      return currentYear - birthYear;
    },
  }),
  
  convertCurrency: tool({
    description: 'Convert between currencies',
    parameters: z.object({
      amount: z.number(),
      from: z.string(),
      to: z.string(),
    }),
    execute: async ({ amount, from, to }) => {
      // Simulate currency conversion
      return amount * 1.1; // Mock exchange rate
    },
  }),
};

const utilityAgent = agent({
  name: 'utility_agent',
  model: openai('gpt-4o'),
  prompt: 'You are a helpful utility assistant with various tools.',
  tools,
});

​
Tool Choice

Control when tools are used: 
const agent = agent({
  name: 'assistant',
  model: openai('gpt-4o'),
  prompt: 'You are a helpful assistant.',
  tools: { /* ... */ },
  toolChoice: 'required', // 'auto' | 'none' | 'required'
});
​
toolChoice
string
default:"auto"
  • 'auto' - Model decides when to use tools
  • 'none' - Never use tools
  • 'required' - Always use at least one tool

​
Tool Execution Examples

​
File System Operations

import { tool } from 'ai';
import { z } from 'zod';
import { readFile, writeFile } from 'fs/promises';

const fileTools = {
  readFile: tool({
    description: 'Read contents of a file',
    parameters: z.object({
      path: z.string(),
    }),
    execute: async ({ path }) => {
      const content = await readFile(path, 'utf-8');
      return content;
    },
  }),
  
  writeFile: tool({
    description: 'Write content to a file',
    parameters: z.object({
      path: z.string(),
      content: z.string(),
    }),
    execute: async ({ path, content }) => {
      await writeFile(path, content);
      return `Wrote ${content.length} bytes to ${path}`;
    },
  }),
};

​
API Calls

import { tool } from 'ai';
import { z } from 'zod';

const githubTools = {
  getRepository: tool({
    description: 'Get information about a GitHub repository',
    parameters: z.object({
      owner: z.string(),
      repo: z.string(),
    }),
    execute: async ({ owner, repo }) => {
      const response = await fetch(
        `https://api.github.com/repos/${owner}/${repo}`
      );
      return await response.json();
    },
  }),
  
  searchRepositories: tool({
    description: 'Search GitHub repositories',
    parameters: z.object({
      query: z.string(),
      limit: z.number().default(10),
    }),
    execute: async ({ query, limit }) => {
      const response = await fetch(
        `https://api.github.com/search/repositories?q=${query}&per_page=${limit}`
      );
      const data = await response.json();
      return data.items;
    },
  }),
};

​
Database Operations

import { tool } from 'ai';
import { z } from 'zod';

const dbTools = {
  queryUsers: tool({
    description: 'Query users from database',
    parameters: z.object({
      email: z.string().optional(),
      limit: z.number().default(10),
    }),
    execute: async ({ email, limit }) => {
      // Simulate database query
      return [
        { id: 1, email: '[email protected]', name: 'John' },
        // ...
      ];
    },
  }),
  
  createUser: tool({
    description: 'Create a new user',
    parameters: z.object({
      email: z.string(),
      name: z.string(),
    }),
    execute: async ({ email, name }) => {
      // Simulate user creation
      return { id: 123, email, name, createdAt: new Date() };
    },
  }),
};

​
Real-World Example: Research Agent

From the source code examples:
research_bot.ts:56:76
import { openai } from '@ai-sdk/openai';
import { agent, execute } from '@deepagents/agent';

const research = agent({
  name: 'ResearchAgent',
  model: openai('gpt-4o'),
  prompt: `
    You are a research assistant. Given a search term, you search the web 
    for that term and produce a concise summary of the results.
  `,
  tools: {
    web_search: openai.tools.webSearch({
      searchContextSize: 'low',
    }),
  },
});

const stream = await execute(
  research,
  'Search term: artificial intelligence\nReason for searching: understanding AI trends',
  {}
);

const summary = await stream.text;
console.log(summary);

​
Best Practices

Write descriptive tool descriptions so the agent knows when to use each tool:
tool({
  description: 'Get current weather conditions for a specific location. Returns temperature, humidity, and forecast.',
  // ...
})
Add descriptions to Zod schema fields:
z.object({
  location: z.string().describe('City name or coordinates (lat,lng)'),
  units: z.enum(['celsius', 'fahrenheit']).default('celsius'),
})
Handle errors gracefully in tool execution:
execute: async ({ query }) => {
  try {
    const result = await apiCall(query);
    return result;
  } catch (error) {
    return { error: 'Failed to fetch data', message: error.message };
  }
}
Return structured data that’s easy for the agent to interpret:
return {
  success: true,
  data: { /* ... */ },
  message: 'Operation completed successfully',
};

​
Next Steps

Multi-Agent Systems

Coordinate agents with handoffs

Context Variables

Share state between agents and tools

Structured Output

Get type-safe responses

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