Mastra Agents - Portkey Docs

Introduction

Mastra is a TypeScript framework for building AI agents with tools, workflows, memory, and evaluation scoring. Portkey enhances Mastra agents with observability, reliability, and production-readiness features. Portkey turns your experimental Mastra agents into production-ready systems by providing:

Complete observability of every agent step, tool use, and interaction
Built-in reliability with fallbacks, retries, and load balancing
Cost tracking and optimization to manage your AI spend
Access to 1600+ LLMs through a single integration
Guardrails to keep agent behavior safe and compliant
Version-controlled prompts for consistent agent performance

Mastra Official Documentation

Learn more about Mastra’s core concepts and features

Installation & Setup

Install the required packages

npm install @mastra/core
npm install --save-dev mastra

Generate API Key

Create a Portkey API key from the Portkey dashboard. You can attach optional budget/rate limits and configurations.

Set Up Provider Integration

In Portkey, set up your LLM provider integration:

Go to Integrations in Portkey
Connect your LLM provider (OpenAI, Anthropic, etc.)
Note your provider slug (e.g., openai-dev, anthropic-prod)

You’ll use this slug in your Mastra model configuration.

Configure Mastra Agent with Portkey

Configure your Mastra agent’s model to use Portkey as the gateway:

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

export const agent = new Agent({
  name: 'Assistant',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',  // Format: openai/@provider-slug@model-name
    url: 'https://api.portkey.ai/v1',
    apiKey: 'YOUR_PORTKEY_API_KEY',
    headers: {
      // Optional: Add Portkey configuration
      'x-portkey-trace-id': 'agent-session-123',
      'x-portkey-metadata': JSON.stringify({ 
        agent: 'assistant',
        env: 'production' 
      })
    }
  }
});

Model ID Format: Use openai/@provider-slug@model-name because Mastra uses OpenAI-compatible interfaces under the hood. The @provider-slug should match the slug from your Portkey integration.

Production Features

1. Enhanced Observability

Portkey provides comprehensive observability for your Mastra agents, helping you understand exactly what’s happening during each execution.

Traces
Logs
Metrics & Dashboards
Metadata Filtering

Traces provide a hierarchical view of your agent’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.

// Add tracing to your Mastra agents
export const agent = new Agent({
  name: 'Research Assistant',
  instructions: 'You are a helpful research assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-trace-id': 'unique_execution_trace_id', // Add unique trace ID
      'x-portkey-metadata': JSON.stringify({ 
        agent_type: 'research_agent' 
      })
    }
  }
});

Add custom metadata to your Mastra agent calls to enable powerful filtering and segmentation:

export const agent = new Agent({
  name: 'Assistant',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-metadata': JSON.stringify({
        agent_type: 'research_agent',
        environment: 'production',
        _user: 'user_123',  // Special _user field for user analytics
        team: 'engineering'
      })
    }
  }
});

This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific agent runs, users, or environments.

2. Reliability - Keep Your Agents Running Smoothly

When running agents in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey’s reliability features ensure your agents keep running smoothly even when problems occur. It’s this simple to enable fallback in your Mastra agents:

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

// Create a config with fallbacks
// It's recommended that you create the Config in Portkey App rather than hard-code the config JSON directly
const portkeyConfig = {
  strategy: {
    mode: 'fallback'
  },
  targets: [
    {
      provider: '@YOUR_OPENAI_PROVIDER',
      override_params: { model: 'gpt-4o' }
    },
    {
      provider: '@YOUR_ANTHROPIC_PROVIDER',
      override_params: { model: 'claude-3-opus-20240229' }
    }
  ]
};

export const agent = new Agent({
  name: 'Resilient Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_OPENAI_PROVIDER@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-config': JSON.stringify(portkeyConfig)
    }
  }
});

This configuration will automatically try Claude if the GPT-4o request fails, ensuring your agent can continue operating.

Automatic Retries

Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips.

Request Timeouts

Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.

Conditional Routing

Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.

Fallbacks

Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.

Load Balancing

Spread requests across multiple API keys or providers. Great for high-volume agent operations and staying within rate limits.

3. Prompting in Mastra Agents

Portkey’s Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your Mastra agents. Instead of hardcoding prompts or instructions, use Portkey’s prompt rendering API to dynamically fetch and apply your versioned prompts.

Prompt Playground
Using Prompt Templates
Prompt Versioning
Mustache Templating for variables

Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It’s where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:

Iteratively develop prompts before using them in your agents
Test prompts with different variables and models
Compare outputs between different prompt versions
Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your Mastra agent’s workflow.

The Prompt Render API retrieves your prompt templates with all parameters configured:

import { Portkey } from 'portkey-ai';
import { Agent } from '@mastra/core/agent';

// Initialize Portkey client
const portkey = new Portkey({
  apiKey: process.env.PORTKEY_API_KEY
});

// Retrieve prompt using the render API
const promptData = await portkey.prompts.render({
  promptID: 'YOUR_PROMPT_ID',
  variables: {
    user_input: 'Tell me about artificial intelligence'
  }
});

// Use the rendered prompt in your Mastra agent
export const agent = new Agent({
  name: 'Assistant',
  instructions: promptData.data.messages[0].content, // Use the rendered prompt as instructions
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY
  }
});

const result = await agent.generate('Tell me about artificial intelligence');
console.log(result.text);

You can:

Create multiple versions of the same prompt
Compare performance between versions
Roll back to previous versions if needed
Specify which version to use in your code:

// Use a specific prompt version
const promptData = await portkey.prompts.render({
  promptID: 'YOUR_PROMPT_ID@version_number',
  variables: {
    user_input: 'Tell me about quantum computing'
  }
});

Portkey prompts use Mustache-style templating for easy variable substitution:

You are an AI assistant helping with {{task_type}}.

User question: {{user_input}}

Please respond in a {{tone}} tone and include {{required_elements}}.

When rendering, simply pass the variables:

const promptData = await portkey.prompts.render({
  promptID: 'YOUR_PROMPT_ID',
  variables: {
    task_type: 'research',
    user_input: 'Tell me about quantum computing',
    tone: 'professional',
    required_elements: 'recent academic references'
  }
});

Prompt Engineering Studio

Learn more about Portkey’s prompt management features

4. Guardrails for Safe Agents

Guardrails ensure your Mastra agents operate safely and respond appropriately in all situations. Why Use Guardrails? Mastra agents can experience various failure modes:

Generating harmful or inappropriate content
Leaking sensitive information like PII
Hallucinating incorrect information
Generating outputs in incorrect formats

Portkey’s guardrails protect against these issues by validating both inputs and outputs. Implementing Guardrails

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

// Create a config with input and output guardrails
// It's recommended you create Config in Portkey App and pass the config ID in the headers
const guardrailConfig = {
  provider: '@YOUR_PROVIDER',
  input_guardrails: ['guardrails-id-xxx', 'guardrails-id-yyy'],
  output_guardrails: ['guardrails-id-xxx']
};

export const agent = new Agent({
  name: 'Safe Agent',
  instructions: 'You are a helpful assistant that provides safe responses.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-config': JSON.stringify(guardrailConfig)
    }
  }
});

Portkey’s guardrails can:

Detect and redact PII in both inputs and outputs
Filter harmful or inappropriate content
Validate response formats against schemas
Check for hallucinations against ground truth
Apply custom business logic and rules

Learn More About Guardrails

Explore Portkey’s guardrail features to enhance agent safety

5. User Tracking with Metadata

Track individual users through your Mastra agents using Portkey’s metadata system. What is Metadata in Portkey? Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special _user field is specifically designed for user tracking.

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

export const agent = new Agent({
  name: 'Personalized Agent',
  instructions: 'You are a personalized assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-metadata': JSON.stringify({
        _user: 'user_123',  // Special _user field for user analytics
        user_name: 'John Doe',
        user_tier: 'premium',
        user_company: 'Acme Corp'
      })
    }
  }
});

Filter Analytics by User With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:

This enables:

Per-user cost tracking and budgeting
Personalized user analytics
Team or organization-level metrics
Environment-specific monitoring (staging vs. production)

Learn More About Metadata

Explore how to use custom metadata to enhance your analytics

6. Caching for Efficient Agents

Implement caching to make your Mastra agents more efficient and cost-effective:

Simple Caching
Semantic Caching

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

const cacheConfig = {
  provider: '@YOUR_PROVIDER',
  cache: {
    mode: 'simple'
  }
};

export const agent = new Agent({
  name: 'Cached Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-config': JSON.stringify(cacheConfig)
    }
  }
});

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

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

const semanticCacheConfig = {
  provider: '@YOUR_PROVIDER',
  cache: {
    mode: 'semantic'
  }
};

export const agent = new Agent({
  name: 'Semantically Cached Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY,
    headers: {
      'x-portkey-config': JSON.stringify(semanticCacheConfig)
    }
  }
});

Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs.

7. Model Interoperability

With Portkey, you can easily switch between different LLMs in your Mastra agents without changing your core agent logic.

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

// Using OpenAI
const openaiAgent = new Agent({
  name: 'OpenAI Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_OPENAI_PROVIDER@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY
  }
});

// Using Anthropic
const anthropicAgent = new Agent({
  name: 'Anthropic Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_ANTHROPIC_PROVIDER@claude-3-opus-20240229',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY
  }
});

// Using Google Gemini
const geminiAgent = new Agent({
  name: 'Gemini Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_GOOGLE_PROVIDER@gemini-2.0-flash-exp',
    url: 'https://api.portkey.ai/v1',
    apiKey: process.env.PORTKEY_API_KEY
  }
});

Portkey provides access to over 200 LLMs through a unified interface, including:

OpenAI (GPT-4o, GPT-4 Turbo, etc.)
Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.)
Mistral AI (Mistral Large, Mistral Medium, etc.)
Google Vertex AI (Gemini 1.5 Pro, etc.)
Cohere (Command, Command-R, etc.)
AWS Bedrock (Claude, Titan, etc.)
Local/Private Models

Supported Providers

See the full list of LLM providers supported by Portkey

Set Up Enterprise Governance for Mastra Agents

Why Enterprise Governance? If you are using Mastra agents inside your organization, you need to consider several governance aspects:

Cost Management: Controlling and tracking AI spending across teams
Access Control: Managing which teams can use specific models
Usage Analytics: Understanding how AI is being used across the organization
Security & Compliance: Maintaining enterprise security standards
Reliability: Ensuring consistent service across all users

Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step. Enterprise Implementation Guide Portkey allows you to use 1600+ LLMs with your Mastra agents setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

Create Integration

To create a new LLM integration:Go to Integrations in the Portkey App. Set budget / rate limits, model access if required and save the integration.This creates a “Portkey Provider” that you can then use in any of your Portkey requests without having to send auth details for that LLM provider again.

Create Config

Configs in Portkey define how your requests are routed, with features like advanced routing, fallbacks, and retries.To create your config:

Go to Configs in Portkey dashboard
Create new config with:

{
  "provider": "@YOUR_PROVIDER_FROM_STEP1",
  "override_params": {
    "model": "gpt-4o" // Your preferred model name
  }
}

Save and note the Config ID for the next step

Configure Portkey API Key

Now create a Portkey API key and attach the config you created in Step 2:

Go to API Keys in Portkey and Create new API key
Select your config from Step 2
Generate and save your API key

Connect to Mastra

After setting up your Portkey API key with the attached config, connect it to your Mastra agents:

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

export const agent = new Agent({
  name: 'Enterprise Agent',
  instructions: 'You are a helpful assistant.',
  model: {
    id: 'openai/@YOUR_PROVIDER_SLUG@gpt-4o',
    url: 'https://api.portkey.ai/v1',
    apiKey: 'YOUR_PORTKEY_API_KEY'  // The API key with attached config from step 3
  }
});

Step 1: Implement Budget Controls & Rate Limits

Integrations enable granular control over LLM access at the team/department level. This helps you:

Set up budget limits
Prevent unexpected usage spikes using Rate limits
Track departmental spending

Setting Up Department-Specific Controls:

Navigate to Integrations in Portkey dashboard and create a new Integration
Provision this Integration for each department with their budget limits and rate limits
Configure model access if required

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like:

Access Control Features:

Model Restrictions: Limit access to specific models
Data Protection: Implement guardrails for sensitive data
Reliability Controls: Add fallbacks and retry logic

Example Configuration:

Here’s a basic configuration to route requests to OpenAI, specifically using GPT-4o:

{
  "strategy": {
    "mode": "single"
  },
  "targets": [
    {
      "provider": "@YOUR_OPENAI_PROVIDER",
      "override_params": {
        "model": "gpt-4o"
      }
    }
  ]
}

Create your config on the Configs page in your Portkey dashboard.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

Track usage per user/team with the help of metadata
Apply appropriate configs to route requests
Collect relevant metadata to filter logs
Enforce access permissions

Create API keys through:

Example using Node.js SDK:

import { Portkey } from 'portkey-ai';

const portkey = new Portkey({
  apiKey: 'YOUR_ADMIN_API_KEY'
});

const apiKey = await portkey.apiKeys.create({
  name: 'engineering-team',
  type: 'organisation',
  workspaceId: 'YOUR_WORKSPACE_ID',
  defaults: {
    configId: 'your-config-id',
    metadata: {
      environment: 'production',
      department: 'engineering'
    }
  },
  scopes: ['logs.view', 'configs.read']
});

For detailed key management instructions, see our API Keys documentation.

Step 4: Deploy & Monitor

After distributing API keys to your team members, your enterprise-ready Mastra setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls.Apply your governance setup using the integration steps from earlier sections. Monitor usage in Portkey dashboard:

Cost tracking by department
Model usage patterns
Request volumes
Error rates

Enterprise Features Now Available

Mastra agents now has:

Departmental budget controls
Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Frequently Asked Questions

How does Portkey enhance Mastra agents?

Portkey adds production-readiness to Mastra agents through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 1600+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications.

Can I use Portkey with existing Mastra agents?

Yes! Portkey integrates seamlessly with existing Mastra agents. You only need to update your agent’s model configuration to point to Portkey. The rest of your agent code remains unchanged.

Does Portkey work with all Mastra features?

Portkey supports all Mastra features, including tools, workflows, memory, and scoring. It adds observability and reliability without limiting any of the framework’s functionality.

Why does the model ID use 'openai/' prefix?

Mastra uses OpenAI-compatible interfaces under the hood, so the model ID format is openai/@provider-slug@model-name. This allows Mastra to work with any LLM provider through Portkey, not just OpenAI. The @provider-slug corresponds to your Portkey integration slug.

How do I filter logs and traces for specific agent runs?

Portkey allows you to add custom metadata and trace IDs to your agent runs through the model headers. Add fields like agent_name, agent_type, or session_id to easily find and analyze specific agent executions.

Can I use different LLM providers for different agents?

Yes! Simply change the provider slug in the model ID. For example, use openai/@openai-provider@gpt-4o for one agent and openai/@anthropic-provider@claude-3-opus-20240229 for another. All agents route through Portkey.

Resources

Mastra Docs

Official Mastra documentation

Mastra Examples

Example implementations for various use cases

Book a Demo

Get personalized guidance on implementing this integration

​Introduction

Mastra Official Documentation

​Installation & Setup

​Production Features

​1. Enhanced Observability

​2. Reliability - Keep Your Agents Running Smoothly

Automatic Retries

Request Timeouts

Conditional Routing

Fallbacks

Load Balancing

​3. Prompting in Mastra Agents

Prompt Engineering Studio

​4. Guardrails for Safe Agents

Learn More About Guardrails

​5. User Tracking with Metadata

Learn More About Metadata

​6. Caching for Efficient Agents

​7. Model Interoperability

Supported Providers

​Set Up Enterprise Governance for Mastra Agents

​Step 1: Implement Budget Controls & Rate Limits

​Setting Up Department-Specific Controls:

​Step 2: Define Model Access Rules

​Access Control Features:

​Example Configuration:

​Step 3: Implement Access Controls

​Step 4: Deploy & Monitor

​Enterprise Features Now Available

​Frequently Asked Questions

​Resources

Mastra Docs

Mastra Examples

Book a Demo

Introduction

Installation & Setup

Production Features

1. Enhanced Observability

2. Reliability - Keep Your Agents Running Smoothly

3. Prompting in Mastra Agents

4. Guardrails for Safe Agents

5. User Tracking with Metadata

6. Caching for Efficient Agents

7. Model Interoperability

Set Up Enterprise Governance for Mastra Agents

Step 1: Implement Budget Controls & Rate Limits

Setting Up Department-Specific Controls:

Step 2: Define Model Access Rules

Access Control Features:

Example Configuration:

Step 3: Implement Access Controls

Step 4: Deploy & Monitor

Enterprise Features Now Available

Frequently Asked Questions

Resources