JavaScript catches up: 4 modern frameworks for multi-agent LLM orchestration

The rapid rise of LLM-powered agents has brought new attention to how we orchestrate complex, multi-agent systems—especially in AI-heavy domains like task automation, assistant chaining, and tool-based reasoning.

For a while, Python had a clear head start in this space. Frameworks like LangChain, CrewAI, and LangGraph made it the go-to choice for prototyping multi-agent architectures. But while Python remains dominant in research and backend experimentation, JavaScript has been catching up—fast.

In the past year, several JavaScript-native frameworks have emerged with serious capabilities: modular agent design, message-passing, memory, RAG support, and even live orchestration UIs.

These tools now offer everything needed to build:

• Autonomous LLM-driven agents
• Workflow engines with shared state
• Interactive agent-task boards
• Dev-friendly experiences for browser, Node.js, and edge runtimes

This article compares four of the most promising frameworks shaping the JavaScript multi-agent ecosystem:

• LangGraph.js – the JavaScript adaptation of LangGraph's powerful graph model
• Mastra – TypeScript-native pipelines for AI-native apps
• AutogenJS – a lightweight agent chat model for quick experimentation
• KaibanJS – a Kanban-style, role-driven orchestration system with a visual playground

LangGraph.js

• Type: Graph-based, stateful agent orchestration
• Language: JavaScript / TypeScript
• GitHub: langchain-ai/langgraphjs
• Best for: Complex workflows with branching logic and long-term state tracking

LangGraph.js is the JavaScript/TypeScript counterpart of LangGraph (Python). It lets you define agent workflows as graphs, where each node can represent an agent, tool, or memory unit, and edges define execution flow.

It’s part of the broader LangChain ecosystem, offering full integration with streaming, checkpoints, subgraphs, and memory across runs.

Pros

✅ Great for graph-like logic and control

✅ LangChain-compatible

✅ Good for production-grade orchestration with logging and visibility

✅ Dev UI with LangGraph Studio

Cons

❌ Tied to LangChain philosophy and APIs

❌ Higher learning curve (state machines, graph modeling)

Example usage:

// npm install @langchain-anthropic
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { ChatAnthropic } from "@langchain/anthropic";
import { tool } from "@langchain/core/tools";

import { z } from "zod";

const search = tool(
  async ({ query }) => {
    if (
      query.toLowerCase().includes("sf") ||
      query.toLowerCase().includes("san francisco")
    ) {
      return "It's 60 degrees and foggy.";
    }
    return "It's 90 degrees and sunny.";
  },
  {
    name: "search",
    description: "Call to surf the web.",
    schema: z.object({
      query: z.string().describe("The query to use in your search."),
    }),
  }
);

const model = new ChatAnthropic({
  model: "claude-3-7-sonnet-latest",
});

const agent = createReactAgent({
  llm: model,
  tools: [search],
});

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "what is the weather in sf",
    },
  ],
});

Mastra

• Type: AI-native pipelines (RAG, toolchains, evaluations)
• Language: TypeScript
• GitHub: mastra-ai/mastra
• Best for: Teams creating custom LLM workflows, retrieval pipelines, and fast evaluation loops

Mastra is a TypeScript-first framework built around the concept of AI-native workflow composition. It focuses on enabling fast development of things like:

• Retrieval-Augmented Generation (RAG)
• Prompt chains with memory and logging
• Evaluation pipelines and testing loops

It’s developer-friendly and flexible, supporting serverless and local runs, and works well for product teams iterating quickly.

Pros

✅ Built-in support for RAG and tool orchestration

✅ Declarative syntax with fast iteration

✅ Excellent for evaluation/test loops

✅ TypeScript native

Cons

❌ Less visual

❌ Pipeline-focused, not agent-first

❌ Still maturing in the community

Example usage:

import { Mastra } from "@mastra/core";
import { summaryClusteringWorkflow } from "./workflows/summaryClusteringWorkflow";
import { topicClusteringAgent } from "./agents/topicClusteringAgent";

import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { createGmailTool } from "./tools/gmailTool";

 const emailSummaryAgent = new Agent({
  name: "Email Summary Agent",
  instructions: `Process support emails for an AI video generation SaaS platform. IMPORTANT: Be thorough in identifying categories - if there's ANY indication of these issues, include the email:`,
  model: openai("gpt-4o"),
  tools: {
    gmailTool: createGmailTool({
      clientEmail: process.env.GMAIL_CLIENT_EMAIL || "",
      privateKey: process.env.GMAIL_PRIVATE_KEY || "",
      subject: process.env.GMAIL_SUBJECT || ""
    })
  },
});

// Create and export the Mastra instance
export const mastra = new Mastra({
  agents: {
    emailSummaryAgent,
    topicClusteringAgent
  },
  workflows: {
    summaryClusteringWorkflow
  },
});

OpenAI Agents JS

• Type: Plugin-based multi-agent orchestration
• Language: JavaScript / TypeScript
• GitHub: openai/openai-agents-js
• Best for: Agent handoffs, tool integrations, and traceable workflows

OpenAI Agents JS is a lightweight, provider-agnostic JavaScript SDK for orchestrating agents around OpenAI models. It includes abstractions for tool handoffs, conversational memory, safety guards, and built-in tracing.

Pros

• Agent orchestration with tool integration and handoffs
• Built-in logging, tracing, and error recovery
• Extensible middleware and memory system

Cons

• Requires OpenAI API access
• Minimal visual tooling or UI

Example usage:

import { z } from 'zod';
import { Agent, run, tool } from '@openai/agents';

const getWeatherTool = tool({
  name: 'get_weather',
  description: 'Get the weather for a given city',
  parameters: z.object({ city: z.string() }),
  execute: async (input) => {
    return `The weather in ${input.city} is sunny`;
  },
});

const agent = new Agent({
  name: 'Data agent',
  instructions: 'You are a data agent',
  tools: [getWeatherTool],
});

async function main() {
  const result = await run(agent, 'What is the weather in Tokyo?');
  console.log(result.finalOutput);
}

main().catch(console.error);

KaibanJS

• Type: Kanban-style, modular, role-based orchestration
• Language: JavaScript / TypeScript
• GitHub: kaiban-ai/kaibanjs
• Best for: Visual, role-driven orchestration with board-like task coordination

KaibanJS offers a completely different way to think about multi-agent systems.

Inspired by Kanban boards, it treats agents as roles and tasks as cards that flow through predefined stages. This model makes complex orchestrations easy to reason about, observe, and debug — especially when collaborating agents have different responsibilities.

KaibanJS provides:

• A fully interactive Playground to visualize agent flow live
• A clear separation of roles/stages/cards for readability
• Integration-ready APIs for React, Next.js, and browser apps

Pros

✅ Intuitive, human-readable orchestration model

✅ Built-in observability and agent logs

✅ Playground with live board view

✅ Seamless integration with frontend stacks

✅ Great for LLM agents with tools or async behavior

Cons

❌ Different mental model vs pipelines or graphs

❌ Not designed for RAG pipelines (but can be extended)

🔗 Try the Playground → https://www.kaibanjs.com/playground

Example usage:

import { Agent, Task, Team } from 'kaibanjs';

// Define agents with specific roles and goals
const profileAnalyst = new Agent({
    name: 'Zoe', 
    role: 'Profile Analyst', 
    goal: 'Extract structured information from conversational user input.', 
    background: 'Data Processor',
    tools: []
});

const resumeWriter = new Agent({
    name: 'Alex Mercer', 
    role: 'Resume Writer', 
    goal: `Craft compelling, well-structured resumes 
    that effectively showcase job seekers qualifications and achievements.`,
    background: `Extensive experience in recruiting, 
    copywriting, and human resources, enabling 
    effective resume design that stands out to employers.`,
    tools: []
});

// Define the tasks for each agent
const processingTask = new Task({ 
    description: `Extract relevant details such as name, 
    experience, skills, and job history from the user's 'aboutMe' input. 
    aboutMe: {aboutMe}`,
    expectedOutput: 'Structured data ready to be used for a resume creation.', 
    agent: profileAnalyst
});

const resumeCreationTask = new Task({ 
    description: `Utilize the structured data to create 
    a detailed and attractive resume. 
    Enrich the resume content by inferring additional details from the provided information.
    Include sections such as a personal summary, detailed work experience, skills, and educational background. Please use markdown format for the output.`,
    expectedOutput: `A professionally formatted resume in markdown format, 
    ready for submission to potential employers.`, 
    agent: resumeWriter 
});

// Create and start the team
const team = new Team({
    name: 'Resume Creation Team',
    agents: [profileAnalyst, resumeWriter],
    tasks: [processingTask, resumeCreationTask],
    inputs: { aboutMe: `My name is David Llaca. 
    JavaScript Developer for 5 years. 
    I worked for three years at Disney, 
    where I developed user interfaces for their primary landing pages
    using React, NextJS, and Redux. Before Disney, 
    I was a Junior Front-End Developer at American Airlines, 
    where I worked with Vue and Tailwind. 
    I earned a Bachelor of Science in Computer Science from FIU in 2018, 
    and I completed a JavaScript bootcamp that same year.` },
    env: {OPENAI_API_KEY: 'ENV_OPENAI_API_KEY'}
});

team.start();

🧠 Final Thoughts

Each of these frameworks has a distinct approach to multi-agent orchestration:

• LangGraph.js → Precise graph-based orchestration, ideal for LangChain users
• Mastra → Rapid iteration on AI pipelines, RAG-native and TypeScript friendly
• OpenAI Agents JS: Traceable, tool-integrated agent workflows
• KaibanJS → Role-first orchestration with Kanban clarity and dev tooling built-in

Whether you're building an LLM assistant network, a multi-agent research tool, or just exploring autonomous workflows — the JavaScript ecosystem now has serious tools to match Python’s early lead.

💬 What’s your preferred orchestration style?

Do you prefer graph edges, pipeline chains, or task boards? Are you already working with one of these tools—or rolling your own? I’d love to hear your approach 👇