Best AI Setups for Multi-Agent Workflows in KaibanJS

Introduction

Modern AI workflows demand more than a single model. Each Large Language Model (LLM) excels in different tasks—some handle reasoning, others specialize in retrieval, and some are optimized for automation.

Instead of relying on a one-size-fits-all AI, KaibanJS enables seamless multi-agent orchestration, allowing developers to combine multiple LLMs and tools to automate workflows.

In this article, we’ll explore the best AI setups for multi-agent workflows in KaibanJS, using GPT-4 Turbo, Claude Sonnet 3.5, Gemini 1.5, Mistral 7B, and tools like Firecrawl, Perplexity API, Tavily, Zapier, and more.

📌 Why Multi-Agent Workflows?

✔ Specialized models handle different parts of the workflow.

✔ More efficient LLM usage reduces cost and improves performance.

✔ Automates decision-making and repetitive tasks seamlessly.

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The Power of AI Agent Collaboration

Multi-agent AI systems work by distributing tasks among specialized AI agents that collaborate to achieve a common goal. KaibanJS makes this easy to implement and scale.

🛠️ Optimized AI Setups for KaibanJS Multi-Agent Workflows

Here are some powerful AI model and tool combinations you can use in KaibanJS:

Task	Best AI Model / Tool
General Reasoning	GPT-4 Turbo
Complex Decision Trees	Claude Sonnet 3.5
Web Scraping	Firecrawl
Technical Report Writing	Gemini 1.5
Real-Time Data Analysis	Perplexity API
Long-Form Document Processing	Mistral 7B
Search & Retrieval	Tavily, Serper, Exa
Workflow Automation	Zapier, Make

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🚀 Implementing a Multi-Agent Workflow in KaibanJS

Let’s see how to build a multi-agent system in KaibanJS to automate data collection, summarization, and report generation.

1️⃣ Define the AI Agents

KaibanJS lets us define agents with specific roles and responsibilities:

import { Agent } from 'kaibanjs';
import { TavilySearchResults } from '@langchain/community/tools/tavily_search';

// Research Agent: Retrieves AI trend data
const researchAgent = new Agent({
    name: 'Researcher',
    role: 'AI Knowledge Seeker',
    goal: 'Retrieve real-time AI trends from search tools.',
    background: 'Market Research',
    tools: [new TavilySearchResults({ maxResults: 3, apiKey: 'ENV_TRAVILY_API_KEY' })],
});

// Summarization Agent: Processes and summarizes key insights
const summaryAgent = new Agent({
    name: 'Summarizer',
    role: 'Report Generator',
    goal: 'Analyze and summarize retrieved information.',
    background: 'Technical Writing',
});

// Automation Agent: Handles workflow execution
const automationAgent = new Agent({
    name: 'Workflow Manager',
    role: 'Task Orchestrator',
    goal: 'Send final reports to an external API.',
    background: 'Automation',
    tools: ['zapier'],
});

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2️⃣ Define the AI Tasks

Each agent is assigned a specific task:

import { Task } from 'kaibanjs';

// Task 1: Collecting AI trend data
const researchTask = new Task({
    description: 'Find up-to-date information on AI advancements.',
    expectedOutput: 'A list of recent AI trends.',
    agent: researchAgent,
});

// Task 2: Summarizing AI trends
const summaryTask = new Task({
    description: 'Summarize retrieved AI trends into structured insights.',
    expectedOutput: 'A concise AI trends report.',
    agent: summaryAgent,
});

// Task 3: Automating workflow execution
const automationTask = new Task({
    description: 'Send reports to an external API using Zapier.',
    expectedOutput: 'Automated report submission.',
    agent: automationAgent,
});

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3️⃣ Run the AI Workflow in KaibanJS

Now, we assemble the agents and tasks into a fully automated team:

import { Team } from 'kaibanjs';

// Create an AI-powered research team
const aiTeam = new Team({
    name: 'AI Research Automation Team',
    agents: [researchAgent, summaryAgent, automationAgent],
    tasks: [researchTask, summaryTask, automationTask],
});

// Start the workflow
aiTeam.start()
    .then(output => console.log("Workflow finished:", output))
    .catch(error => console.error("Error:", error));

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🔹 How This Works

✔ The Researcher agent retrieves AI trends from web sources.

✔ The Summarizer agent processes and organizes the insights.

✔ The Workflow Manager automates report delivery via Zapier.

This multi-agent setup allows for scalable, AI-powered automation with minimal human intervention.

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📌 Why KaibanJS for Multi-Agent AI Workflows?

KaibanJS is built for multi-agent orchestration, enabling AI models and tools to work together seamlessly.

✅ Task-Specific Optimization – Assign tasks to the best AI model.

✅ Lower API Costs – Optimize LLM usage to minimize token consumption.

✅ Seamless Automation – Integrate AI models with real-world automation tools.

✅ Scalability – Expand workflows effortlessly with additional AI agents.

KaibanJS makes it easy to build, test, and scale AI-driven automation in JavaScript.

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Final Output: What You Get

By the end of this KaibanJS workflow, you will have:

✔ A structured AI trends report.

✔ Automated retrieval, summarization, and report delivery.

✔ A reusable AI-powered system for automating content workflows.

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💡 Get Started with KaibanJS

If you want to orchestrate AI agents efficiently, KaibanJS is the best JavaScript framework for multi-agent workflows.

🚀 Try KaibanJS now: Playground

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📢 Which AI tools and models do you use in your workflows? Let’s discuss in the comments! 👇