Choosing a JavaScript Agent Framework

As AI agents and assistants become increasingly table stakes for modern SaaS applications, developers face the challenge of selecting the right framework to build, manage, and deploy these intelligent systems.

For JavaScript developers, Mastra and LangGraph.js have emerged as leading options. Each offers distinct approaches to agent architecture, development workflows, and deployment strategies.

Mastra: a comprehensive, component-based framework

Mastra is a JavaScript framework designed for building, testing, and deploying AI agents. It offers an ecosystem that spans from local development to production deployment through a cloud platform (along with support for other serverless clouds). It's built by the team who previously built Gatsby.js.

Key Features

• Agent Management: Mastra provides a cohesive architecture for defining agents, tools, workflows, and memory systems.
• Workflow Orchestration: Built-in workflow capabilities for multi-step processes and complex agent interactions.
• Memory Systems: Sophisticated memory management with support for various storage backends (PostgreSQL, LibSQL, Upstash).
• Voice Capabilities: Native support for voice interfaces, including speech-to-text and text-to-speech functionality.
• Observability: Comprehensive tracing and monitoring tools, particularly robust in Mastra Cloud.
• Deployment Options: Multiple deployment paths from self-hosted to serverless to fully-managed with Mastra Cloud.

Development Experience

Mastra follows a declarative approach to agent definition, with TypeScript support providing strong type safety. Its architecture emphasizes component reusability and extension patterns:

import { Mastra, Agent } from '@mastra/core';
import { openai } from '@ai-sdk/openai';
import { getWeather } from './tools/weather';

const weatherAgent = new Agent({
  model: openai('gpt-4o')
  instructions: 'You are a helpful weather assistant...',
  tools: {
    getWeather,
  },
});

export const mastra = new Mastra({
  agents: {
    weatherAgent,
  }
});

LangGraph.js: a graph-based framework for agent orchestration

LangGraph.js focuses on representing agent behavior as graph-based workflows, allowing for complex decision trees and state management. It's the agent framework from the people who created Langchain.

Key Features

• Graph-Based Orchestration: Define agent behavior as nodes and edges in a computational graph.
• State Management: First-class support for managing agent states.
• LangChain Integration: Tight integration with the LangChain ecosystem.
• Tool Calling: Framework for connecting agents to external tools and APIs.

Development Experience

LangGraph.js uses a graph-construction API to define the flow of agent execution:

import { createGraph } from 'langgraph';

const graph = createGraph();

graph.addNode('understand', async (state) => {
  // Process user input
});

graph.addNode('toolCall', async (state) => {
  // Call external tools if needed
});

graph.addNode('respond', async (state) => {
  // Generate response
});

graph.addEdge('understand', 'toolCall');
graph.addEdge('toolCall', 'respond');

Framework Comparison

Choosing the Right Framework

Consider Mastra when:

• You want a local development environment with tracing built-in
• Memory management is important

Consider LangGraph.js when:

• You're already using other LangChain tools
• You like the graph-based workflow syntax

Conclusion

The choice between Mastra and LangGraph.js depends largely on your specific requirements as well as which you feel most comfortable in.

• Mastra offers both agent and workflow capabilities, and a workflow syntax that's familiar to JavaScript devs.
• LangGraph.js is good for graph-based agent orchestration and applications that already use LangChain.

As the agent ecosystem continues to evolve rapidly, each of these frameworks is likely to develop new capabilities and address current limitations. The best approach may be to prototype a simple agent in each framework to get a feel for their development experience and evaluate how they align with your team's workflow.

No matter which framework you choose, the most important factors will be your team's ability to understand, extend, and maintain the codebase as your agent's capabilities grow over time.