How to Build TypeScript Agents with Mastra AI Framework

Mastra is a battery-included, open-source TypeScript framework designed specifically for building AI applications and agents. Created by the team behind Gatsby, it addresses the fragmentation in the JavaScript AI ecosystem by providing a unified set of primitives for agents, workflows, RAG (Retrieval Augmented Generation), and evaluations. Unlike general-purpose libraries that require you to stitch together separate tools, Mastra offers a cohesive architecture. It treats "agents" not just as prompts, but as stateful entities capable of planning, executing tools, and maintaining memory. For TypeScript developers, this means you can build complex agentic systems using familiar patterns, strong typing, and the existing Node.js ecosystem without switching to Python. Mastra's architecture focuses on four main pillars:

• Agents: Autonomous entities with instructions, models, and tools.
• Workflows: Graph-based orchestration for deterministic and cyclic processes.
• RAG: Built-in pipelines for ingesting, chunking, and retrieving knowledge.
• Evals: Integrated evaluation frameworks to test agent performance.

Helpful references: Fast.io Workspaces, Fast.io Collaboration, and Fast.io AI.

Building with Mastra involves assembling several key components that work together to create intelligent behaviors. Understanding these primitives is essential for effective agent development.

1. Agents and Tools

In Mastra, an agent is defined by its model (supporting OpenAI, Anthropic, Gemini, etc.), its system instructions, and the tools it can access. Tools are TypeScript functions that you expose to the agent. Mastra handles the schema generation and parameter validation automatically, allowing the LLM to "call" your code safely. This makes integrating external APIs or internal business logic straightforward.

2. Workflow Graphs

While agents are autonomous, many business processes require structure. Mastra's Workflow system allows you to define directed graphs (DAGs) where steps can be agents, simple functions, or control flow logic. This is ideal for processes like "Research → Draft → Review," where you want to constrain the agent's path while allowing for intelligent decisions at each node.

3. Memory and RAG

Agents need context. Mastra provides a built-in memory system that persists conversation history and allows for semantic retrieval. Its RAG capabilities let you ingest documents (PDFs, Markdown, etc.), chunk them, store embeddings, and retrieve relevant context at query time, all within the same framework.

Choosing the right framework matters. Here is how Mastra compares to the other major players in the TypeScript AI space.

Mastra stands out when you need to build backend-heavy agents that perform complex tasks, rather than just simple conversational interfaces. Its opinionated structure reduces decision fatigue compared to LangChain's flexibility.

One common challenge in agent development is file management. Agents often need to generate reports, analyze datasets, or process images. Storing these files on the local filesystem of a containerized agent (like in Docker or serverless) is risky because the data vanishes when the instance spins down. Fast.io provides a practical storage option for Mastra agents. By connecting Fast.io, your agents get a persistent, cloud-native file system that allows them to:

• Store Outputs: Save generated PDFs, code files, or images permanently.
• Share State: Pass large files between agents in a workflow without bloating memory.
• Handoff to Humans: Save a file to a workspace that a human can instantly view and approve. Because Mastra runs in Node.js, you can easily use the Fast.io MCP server or standard API to give your agents these capabilities.

Give Your AI Agents Persistent Storage

Stop relying on ephemeral local storage. Get 50GB of persistent, cloud-native storage for your AI agents with Fast.io's free agent tier.

Get Free Agent Storage

Here is how to give your Mastra agent cloud storage capabilities using the Model Context Protocol (MCP).

First: Set up the Fast.io MCP Server Fast.io offers a free agent tier with 50GB of storage. First, sign up your agent to get credentials. Then, configure the Fast.io MCP server in your environment. This server exposes tools like list_files, upload_file, and search_files to your agent.

Next: Define the tool in Mastra In your Mastra agent configuration, you can import and register these tools. Since Mastra supports typed tool definitions, you can map the MCP tools directly to agent capabilities. ```typescript // Example pseudo-code for tool integration import { Agent } from '@mastra/core'; import { fastIoTools } from './mcp-config';

const researchAgent = new Agent({ name: 'Researcher', model: 'gpt-4', tools: { saveReport: fastIoTools.uploadFile, readDataset: fastIoTools.readFile }, instructions: 'You are a researcher. Read data from the dataset, analyze it, and save your final report to the "Reports" folder.' });

**Then: Execute the workflow**
When the agent runs, it can now decide to read a CSV file from your Fast.io workspace, perform analysis, and write a Markdown report back to storage. This file is immediately accessible to you via the Fast.io dashboard, making human-in-the-loop workflows easy.

To get the most out of Mastra and your AI agents, follow these development best practices:

• Type Everything: Use TypeScript's features. Define interfaces for your tool inputs and outputs. This prevents the LLM from hallucinating invalid parameters and ensures runtime safety.
• Use Evaluators: Don't guess if your agent is working. Implement Mastra's evaluation capabilities to run test cases against your prompts and logic whenever you make changes.
• Externalize State: Keep your agents stateless by offloading memory to a database and file storage to a service like Fast.io. This allows your agents to scale horizontally.
• Human Handoff: Design points in your workflow where the agent can pause and request human review. Using a shared storage workspace is an excellent way to manage this. The agent uploads a draft, notifies a human via webhook, and waits for approval.