📄️ Custom Agent Prompts
Learn how to customize agent prompts in KaibanJS to tailor AI agent behavior and responses for specific use cases.
📄️ Multiple LLMs Support
Leverage multiple language models to enhance the capabilities of your AI agents in KaibanJS.
📄️ Integrating with JavaScript Frameworks
Enhance your JavaScript projects with AI capabilities using KaibanJS.
📄️ Implementing a RAG tool
Learn to enhance your AI projects with the power of Retrieval Augmented Generation (RAG). This step-by-step tutorial guides you through creating the WebRAGTool in KaibanJS, enabling your AI agents to access and utilize web-sourced, context-specific data with ease.
📄️ Using Typescript
KaibanJS is type supported. You can use TypeScript to get better type checking and intellisense with powerful IDEs like Visual Studio Code.
📄️ API Key Management
Learn about the pros and cons of using API keys in browser-based applications with KaibanJS. Understand when it's acceptable, the potential risks, and best practices for securing your keys in production environments.
📄️ Deploying Your Kaiban Board
Learn how to deploy your Kaiban Board, a Vite-based single-page application, to various hosting platforms.
📄️ Using Team Insights
Learn how to leverage team insights in KaibanJS to provide agents with historical knowledge and context for better decision-making.
📄️ Kanban Tools
Learn how to use Kanban Tools in KaibanJS to enhance workflow control and task management.
📄️ Task Result Passing
How to pass and use results between tasks in KaibanJS.
📄️ Structured Output
Define the exact shape and format of your AI agent outputs to ensure consistent and predictable responses.
📄️ MCP Adapter Integration
Learn how to integrate Model Context Protocol (MCP) tools with KaibanJS using the LangChain MCP adapter.
📄️ Exporting Traces with OpenTelemetry
Learn how to enable distributed tracing and observability in KaibanJS using the @kaibanjs/opentelemetry package.
📄️ Image Processing Agent
Learn how to create an AI agent that can analyze and process images using multimodal language models in KaibanJS.
📄️ A2A Protocol Integration
Learn how to integrate KaibanJS teams with Google's Agent-to-Agent (A2A) protocol for seamless agent communication and collaboration.
📄️ Using WorkflowDrivenAgent
Learn how to create and use WorkflowDrivenAgent with complex workflows, LLM SDKs integration, suspension, and team integration.
📄️ Integrating with KaibanIO Platform
Learn how to integrate your KaibanJS teams with the KaibanIO platform using the A2A protocol for enterprise workflow automation and card-based task management.
📄️ OpenClaw Integration via OpenResponses API
Learn how to expose a KaibanJS multi-agent team as an OpenClaw agent backend using the OpenResponses API specification for WhatsApp, Telegram, Discord, and other messaging channels.
📄️ OpenClaw Native Plugin (@kaibanjs/kaibanjs-plugin)
Register a KaibanJS Team as the OpenClaw tool kaiban_run_team so the Gateway’s main agent can delegate multi-agent workflows without a separate HTTP adapter.