cloudbase-agent-ts

Cloudbase Agent (TypeScript) TypeScript SDK for deploying AI agents as HTTP services using the AG-UI protocol. Note: This skill is for TypeScript/JavaScript projects only. When to use this skill Use this skill for AI agent development when you need to: Deploy AI agents as HTTP services with AG-UI protocol support Build agent backends using LangGraph or LangChain frameworks Create custom agent adapters implementing the AbstractAgent interface Understand AG-UI protocol events and message streaming Build web UI clients that connect to AG-UI compatible agents Build WeChat Mini Program UIs for AI agent interactions Do NOT use for: Simple AI model calling without agent capabilities (use ai-model-* skills) CloudBase cloud functions (use cloud-functions skill) CloudRun backend services without agent features (use cloudrun-development skill) How to use this skill (for a coding agent) Choose the right adapter Use LangGraph adapter for stateful, graph-based workflows Use LangChain adapter for chain-based agent patterns Build custom adapter for specialized agent logic Deploy the agent server Use @cloudbase/agent-server to expose HTTP endpoints Configure CORS, logging, and observability as needed Deploy to CloudRun or any Node.js hosting environment Build the UI client Use @ag-ui/client for web applications Use @cloudbase/agent-ui-miniprogram for WeChat Mini Programs Connect to the agent server's /send-message or /agui endpoints Follow the routing table below to find detailed documentation for each task Routing Task Read Deploy agent server (@cloudbase/agent-server) server-quickstart Use LangGraph adapter adapter-langgraph Use LangChain adapter adapter-langchain Build custom adapter adapter-development Understand AG-UI protocol agui-protocol Build UI client (Web or Mini Program) ui-clients Deep-dive @cloudbase/agent-ui-miniprogram ui-miniprogram Quick Start import { run } from "@cloudbase/agent-server" ; import { LanggraphAgent } from "@cloudbase/agent-adapter-langgraph" ; run ( { createAgent : ( ) => ( { agent : new LanggraphAgent ( { workflow } ) } ) , port : 9000 , } ) ;