A multi-agent AI development framework written in JavaScript / TypeScript, built by AI Champions. Its defining feature is that you assign roles and goals to multiple AI agents, let them divide the work, and watch their progress in real time on a Trello-style kanban board. Most agent frameworks are Python-based, but KaibanJS is designed to drop straight into the JavaScript ecosystem — React, Next.js, and Node.js. Its state management follows a Redux-inspired architecture, which will feel familiar to web front-end developers. It is published on GitHub as open source under the MIT license and installs as the npm package kaibanjs.
Key Features
- Workflow visualization on a kanban board: The bundled Kaiban Board displays agent tasks in lanes such as “to do / doing / done.” Because you can watch where an agent is stuck, it makes debugging agent behavior — normally a black box — far easier
- Role-based agent design: Each agent gets a name, role, goal, and background, so you can split work between, say, a researcher and a writer. The output of one task can be passed as the input to the next
- Combine multiple LLMs: Models from OpenAI, Anthropic Claude, Google Gemini, and others can be assigned per agent — a high-end model for hard reasoning, a cheaper one for simple formatting
- Integration with JavaScript frameworks: Runs in React, Vue, Angular, Next.js, and Node.js environments. Embedding agent capabilities directly into a front-end application is a strength Python frameworks do not offer
- LangChain-compatible tools and MCP support: LangChainJS tools (search, calculators, and so on) work as they are. It also supports MCP (Model Context Protocol) adapter integration via
@langchain/mcp-adapters, letting agents use tools exposed by external MCP servers (Node.js environments only) - Observability and cost tracking: Beyond execution logs, token usage and cost aggregation are built in, so you can see which agent is consuming the budget
- Deterministic workflow execution: Rather than leaving everything to the LLM’s judgment, WorkflowDrivenAgent lets you lay out sequential, parallel, and conditional steps explicitly, with support for suspending and resuming a run
Pricing
| Plan | Price | Key features |
|---|---|---|
| Open source | $0 (MIT license) | All features, including the framework and Kaiban Board. Self-hosted |
Pricing is current as of August 2026. No paid cloud service or managed plan has been announced. Note that although the framework itself is free, the API usage fees for the LLMs your agents call are paid separately to each provider. Check the official site for the latest information.
Pros & Cons
✅ Pros
- Everything is done in JavaScript / TypeScript, so you can start agent development without setting up a separate Python environment
- Kanban visualization makes agent-to-agent exchanges easy to follow, which helps both debugging and explaining the system to stakeholders
- MIT-licensed open source: no license fees and no vendor lock-in
- Redux-style state management lets you apply existing web front-end knowledge directly
- Token usage and cost are visible, making operating cost estimates easier
⚠️ Cons
- The community and third-party tool ecosystem are thinner than the major Python frameworks, so there are fewer case studies and less non-English material
- MCP adapter integration does not run in the browser and is limited to Node.js environments
- The version numbers are still in the 0.x range, so API changes are likely to continue
- Support runs mainly through Discord and GitHub Issues; no commercial support contract is offered
- Handling LLM API keys on the front end would expose them, so production use requires a server-side relay design
Comparison with Similar Services
| Criteria | KaibanJS | CrewAI | LangGraph | Microsoft AutoGen |
|---|---|---|---|---|
| Primary language | JavaScript / TypeScript | Python | Python (JS version available) | Python / .NET |
| Visualization | Kanban board included | CLI and logs | LangGraph Studio (separate tool) | Logs and extensions |
| Model configuration | Per agent | Per agent | Freely per node | Per agent |
| Best suited for | Embedding in web apps | Role-based team building | Controlling complex state transitions | Research and experimentation |
| License | MIT (open source) | Open source | Open source | Open source |
Who Is It For
- Front-end developers who want to embed AI agent features directly into a web app built with React or Next.js
- JavaScript engineers who want to try multi-agent systems without learning Python
- Teams uneasy about running agents whose behavior they cannot see, and who want progress visible during development
- Anyone building a division-of-labor AI workflow with separate roles such as researcher and writer
- Organizations that want everything to stay on their own infrastructure and prefer to avoid managed SaaS
Summary
KaibanJS answers the demand for building multi-agent systems in JavaScript head-on. Its kanban visualization is a clear response to a real practical frustration: agent behavior is hard to follow. On the other hand, its ecosystem is not as mature as the Python contenders, and with versions still in the 0.x range, production use should assume you will need to keep up with API changes. A good starting point is to run a small team configuration locally and watch the agents exchange work on the board.