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KaibanJS

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Basic Information

KaibanJS is a JavaScript-native framework for building, orchestrating, and visualizing multi-agent AI systems using a Kanban-inspired workflow. It is designed to let developers create autonomous agents, define tasks and teams, and run coordinated workflows in Node.js and browser environments. The project provides both a visual Kaiban Board for real-time observation and a programmatic API with core primitives like Agent, Task, and Team so teams can run agent workflows without a UI. The README documents a quick start using an initializer, manual installation via npm, and example code that shows how to configure agents, tasks, and environment keys. KaibanJS targets JavaScript developers who want a first-class, framework-native way to manage agent roles, pass results between steps, and integrate different LLM providers into orchestrated multi-step processes.

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App URL
https://github.com/kaiban-ai/KaibanJS
Github Repository
https://github.com/kaiban-ai/KaibanJS/blob/main/README.md

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App Details

Features
KaibanJS bundles a set of features for building multi-agent workflows and integrating them into JavaScript applications. The Kaiban Board visualizes agent workflows in real time and shows tasks moving through stages. Role-based agent design lets you create specialized agents with names, roles, goals, backgrounds, and tools. Tool integration supports LangchainJS-compatible tools and custom tools configured with API keys. Task result passing enables outputs from one task to become inputs for subsequent tasks using a placeholder syntax. Memory management can be enabled or disabled at the team level to control automatic access to previous results. Multiple LLM support lets different agents use different model providers and models. The framework includes a Redux-inspired state architecture for observability, hooks for useStore integration in React, and built-in monitoring of logs, token usage, and costs.
Use Cases
KaibanJS helps teams and developers orchestrate complex AI work by turning multi-step agent interactions into manageable, observable workflows. The Kanban-style board makes it easy to monitor progress and understand which agent is doing what at a glance, improving collaboration between human and AI participants. Role specialization and tool access allow more precise behavior and task delegation. Automatic task result passing and optional team memory simplify chained workflows and reduce boilerplate for multi-step processes. Multiple LLM support enables cost and capability optimization by assigning appropriate models to each task. Redux-like state management and built-in observability provide detailed logs, token and cost stats, and lifecycle hooks to react to status changes. The framework integrates with popular JavaScript frameworks so teams can embed agent orchestration into existing apps and run example workflows quickly.

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