Mastra Integration

npm install @sentrial/mastra @sentrial/sdk @mastra/core

import { Agent } from '@mastra/core/agent';
import { instrumentAgent } from '@sentrial/mastra';

// Your existing Mastra agent
const agent = new Agent({
  name: 'My Agent',
  instructions: 'You are a helpful assistant.',
  model: 'openai/gpt-4o',
  tools: { /* your tools */ },
});

// Wrap with Sentrial — that's it
const trackedAgent = instrumentAgent(agent, {
  apiKey: process.env.SENTRIAL_API_KEY,
  agentName: 'my-agent',
});

// Use normally — everything is tracked automatically
const result = await trackedAgent.generate('What is the weather today?');
// Session created with: input, output, tokens, cost, tool calls, latency

const trackedAgent = instrumentAgent(agent, {
  apiKey: process.env.SENTRIAL_API_KEY,  // required
  apiUrl: 'https://api.sentrial.com',    // optional — defaults to production
  agentName: 'my-agent',                 // optional — defaults to agent.name or agent.id
  failSilently: true,                    // optional — true by default
  pii: true,                             // optional — auto-fetch PII redaction config
});

const THREAD_ID = 'convo-abc-123';
const USER_ID = 'user-42';

// Turn 1
await trackedAgent.generate('Hi, my name is Alice.', {
  memory: { resource: USER_ID, thread: THREAD_ID },
});

// Turn 2 — automatically linked to the same conversation
await trackedAgent.generate('What was my name again?', {
  memory: { resource: USER_ID, thread: THREAD_ID },
});

import { createTool } from '@mastra/core/tools';
import { z } from 'zod';

const lookupUser = createTool({
  id: 'lookup-user',
  description: 'Look up a customer by ID',
  inputSchema: z.object({ userId: z.string() }),
  execute: async ({ userId }) => {
    // Automatically traced: input args, return value
    return await db.users.find(userId);
  },
});

const agent = new Agent({
  name: 'Support Agent',
  model: 'openai/gpt-4o-mini',
  tools: { lookupUser },
  instructions: 'You help customers with account lookups.',
});

const tracked = instrumentAgent(agent, {
  apiKey: process.env.SENTRIAL_API_KEY,
  agentName: 'customer-support',
});

const result = await tracked.generate('Look up user USR-123', {
  maxSteps: 5,
});
// Session events: llm:openai:gpt-4o-mini → lookup-user → llm:openai:gpt-4o-mini

const stream = await trackedAgent.stream(
  'Give me a summary of my recent orders.',
  {
    memory: { resource: 'user-42', thread: 'convo-abc' },
    maxSteps: 5,
    onStepFinish: (step) => {
      // Your callback still works — Sentrial injects alongside it
      console.log('Step done:', step.toolCalls?.length, 'tool calls');
    },
  }
);

for await (const chunk of stream.textStream) {
  process.stdout.write(chunk);
}
// Session recorded when stream finishes: full text, tokens, cost, latency

const tracked = instrumentAgent(agent, {
  apiKey: process.env.SENTRIAL_API_KEY,
  agentName: 'support-agent',
  pii: true, // Auto-fetch redaction config from your Sentrial org
});

// User sends: "My email is john@example.com and SSN is 123-45-6789"
// Sentrial stores: "My email is [EMAIL] and SSN is [SSN]"

const result = await trackedAgent.generate('How can I help?', {
  sentrial: {
    userId: 'override-user-id',
    convoId: 'override-convo-id',
    metadata: {
      satisfaction: 5,
      source: 'web-chat',
      priority: 'high',
    },
  },
});

try {
  await trackedAgent.generate('Do something risky');
} catch (error) {
  // Error is recorded in Sentrial as a failed session
  // with error type and message, then re-thrown here
  console.error(error);
}

import { Agent } from '@mastra/core/agent';
import { createTool } from '@mastra/core/tools';
import { instrumentAgent } from '@sentrial/mastra';
import { z } from 'zod';

// Define tools
const lookupUser = createTool({
  id: 'lookup-user',
  description: 'Look up a customer by name or email',
  inputSchema: z.object({ query: z.string() }),
  execute: async ({ query }) => {
    return await db.users.search(query);
  },
});

const checkOrder = createTool({
  id: 'check-order',
  description: 'Check order status by order ID',
  inputSchema: z.object({ orderId: z.string() }),
  execute: async ({ orderId }) => {
    return await db.orders.find(orderId);
  },
});

// Create and instrument agent
const agent = new Agent({
  name: 'Customer Support',
  model: 'openai/gpt-4o-mini',
  tools: { lookupUser, checkOrder },
  instructions: `You are a friendly customer support agent.
Help customers with account lookups and order status checks.`,
});

const support = instrumentAgent(agent, {
  apiKey: process.env.SENTRIAL_API_KEY,
  agentName: 'customer-support',
  pii: true,
});

// Handle a multi-turn support conversation
async function handleConversation(userId: string, threadId: string) {
  // Turn 1: Account lookup
  const turn1 = await support.generate(
    'Hi, I need help with my account. My email is alice@example.com',
    {
      maxSteps: 5,
      memory: { resource: userId, thread: threadId },
    }
  );
  console.log(turn1.text);

  // Turn 2: Order status (same conversation)
  const turn2 = await support.generate(
    "What's the status of order ORD-12345?",
    {
      maxSteps: 5,
      memory: { resource: userId, thread: threadId },
    }
  );
  console.log(turn2.text);
}

handleConversation('user-42', `thread-${Date.now()}`);