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The Mastra integration demonstrates how to use Mastra’s agent system with Zentry as the memory backend through custom tools. This enables agents to remember and recall information across conversations.

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Overview

In this guide, we’ll create a Mastra agent that:
  1. Uses Zentry to store information using a memory tool
  2. Retrieves relevant memories using a search tool
  3. Provides personalized responses based on past interactions
  4. Maintains context across conversations and sessions

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Setup and Configuration

Install the required libraries: 
npm install @mastra/core @mastra/Zentry @ai-sdk/openai zod
Set up your environment variables:
Remember to get the Zentry API key from Zentry Platform.
Zentry_API_KEY=your-Zentry-api-key
OPENAI_API_KEY=your-openai-api-key

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Initialize Zentry Integration

Import required modules and set up the Zentry integration: 
import { ZentryIntegration } from '@mastra/Zentry';
import { createTool } from '@mastra/core/tools';
import { Agent } from '@mastra/core/agent';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

// Initialize Zentry integration
const Zentry = new ZentryIntegration({
  config: {
    apiKey: process.env.Zentry_API_KEY || '',
    user_id: 'alice', // Unique user identifier
  },
});

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Create Memory Tools

Set up tools for memorizing and remembering information: 
// Tool for remembering saved memories
const ZentryRememberTool = createTool({
  id: 'Zentry-remember',
  description: "Remember your agent memories that you've previously saved using the Zentry-memorize tool.",
  inputSchema: z.object({
    question: z.string().describe('Question used to look up the answer in saved memories.'),
  }),
  outputSchema: z.object({
    answer: z.string().describe('Remembered answer'),
  }),
  execute: async ({ context }) => {
    console.log(`Searching memory "${context.question}"`);
    const memory = await Zentry.searchMemory(context.question);
    console.log(`\nFound memory "${memory}"\n`);

    return {
      answer: memory,
    };
  },
});

// Tool for saving new memories
const ZentryMemorizeTool = createTool({
  id: 'Zentry-memorize',
  description: 'Save information to Zentry so you can remember it later using the Zentry-remember tool.',
  inputSchema: z.object({
    statement: z.string().describe('A statement to save into memory'),
  }),
  execute: async ({ context }) => {
    console.log(`\nCreating memory "${context.statement}"\n`);
    // To reduce latency, memories can be saved async without blocking tool execution
    void Zentry.createMemory(context.statement).then(() => {
      console.log(`\nMemory "${context.statement}" saved.\n`);
    });
    return { success: true };
  },
});

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Create Mastra Agent

Initialize an agent with memory tools and clear instructions: 
// Create an agent with memory tools
const ZentryAgent = new Agent({
  name: 'Zentry Agent',
  instructions: `
    You are a helpful assistant that has the ability to memorize and remember facts using Zentry.
    Use the Zentry-memorize tool to save important information that might be useful later.
    Use the Zentry-remember tool to recall previously saved information when answering questions.
  `,
  model: openai('gpt-4o'),
  tools: { ZentryRememberTool, ZentryMemorizeTool },
});

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Key Features

  1. Tool-based Memory Control: The agent decides when to save and retrieve information using specific tools
  2. Semantic Search: Zentry finds relevant memories based on semantic similarity, not just exact matches
  3. User-specific Memory Spaces: Each user_id maintains separate memory contexts
  4. Asynchronous Saving: Memories are saved in the background to reduce response latency
  5. Cross-conversation Persistence: Memories persist across different conversation threads
  6. Transparent Operations: Memory operations are visible through tool usage

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Conclusion

By integrating Mastra with Zentry, you can build intelligent agents that learn and remember information across conversations. The tool-based approach provides transparency and control over memory operations, making it easy to create personalized and context-aware AI experiences.

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Help

  • For more details on Mastra, visit the Mastra documentation.
  • For Zentry documentation, refer to the Zentry Platform.
  • If you need further assistance, please feel free to reach out to us through the following methods:

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