Mastra - Typescript AI Framework Training in Copenhagen, Denmark

The Mastra – TypeScript AI Framework Training is a developer-focused, hands-on program designed to teach how to build production-grade AI applications using Mastra with TypeScript.
The course covers AI app architecture, agent workflows, tool integration, LLM orchestration, state management, and deployment patterns, enabling teams to create scalable, maintainable, and enterprise-ready AI systems in a modern TypeScript stack.

This training is ideal for frontend, backend, and full-stack developers building AI-powered web and service applications.

Introduction to Mastra & AI App Architecture

• What is Mastra and where it fits

• AI application patterns in TypeScript

• Monolith vs microservice AI architectures

• Designing maintainable AI systems

Mastra Project Setup & Core Concepts

• Setting up a Mastra + TypeScript project

• Project structure and configuration

• Core abstractions in Mastra

• Environment and secrets management

LLM Integration & Providers

• Connecting LLM providers

• Managing API keys and configurations

• Handling responses and errors

• Cost and latency considerations

Prompting & Structured Outputs

• Prompt templates in Mastra

• Enforcing structured (JSON/schema) outputs

• Validation and error handling

• Reliable AI response design

Building AI Agents with Mastra

• Agent concepts and responsibilities

• Tool-using agents

• Task-based vs goal-based agents

• Human-in-the-loop agent patterns

Workflow & Orchestration Patterns

• Multi-step AI workflows

• Chaining and branching logic

• Conditional execution

• Orchestrating complex AI tasks

Tool & API Integration

• Connecting agents to external APIs

• Database and file system tools

• Internal service integration

• Secure and controlled tool usage

State, Memory & Context Management

• Short-term vs long-term memory

• Context persistence strategies

• Sharing state across workflows

• Avoiding context bloat

Error Handling & Reliability

• Retry and fallback strategies

• Timeout and circuit breaker patterns

• Observability for AI workflows

• Debugging AI-driven applications

Performance & Cost Optimization

• Token and request optimization

• Caching and reuse

• Parallelism and async patterns

• Scaling AI workloads efficiently

Security & Governance

• Protecting secrets and credentials

• Tool permissioning and sandboxing

• Audit trails and logging

• Responsible AI usage in apps

Testing AI Applications

• Unit testing AI components

• Mocking LLM responses

• Regression testing for prompts

• Quality and consistency checks

Deployment & Production Operations

• Deploying Mastra apps

• CI/CD for AI services

• Monitoring and alerting

• Versioning and rollout strategies

Hands-on Exercises

Summary and Conclusion