TabForge AI: a complete platform for building Java Web + AI apps

Modern AI UX — chat panels, tool-calling agents, assistants that remember context and even suggest your next step — has lived in JavaScript SaaS for years. The Java enterprise stack has been left doing it the hard way. TabForge AI closes that gap. It's a complete platform for building AI-powered web apps on Jakarta EE + PrimeFaces — from the multi-tab UI shell down to a clean, provider-agnostic AI layer. Library, live demo, starter project, and a drop-in UI template — all shipped. Here's the whole thing, top to bottom. ## 1. Tabs as annotated beans — DynTabs You describe a tab; the framework handles opening, closing, lifecycle, and state. Each open tab gets its own isolated CDI bean via a custom @TabScoped scope. @Named @TabScoped @DynTab(name = "OrdersDynTab", uniqueIdentifier = "Orders", title = "Orders", includePage = "/WEB-INF/orders.xhtml", trackActivity = true) public class OrdersBean extends BaseDyntabCdiBean { // open the same tab twice → two independent instances } java No manual navigation, no page-state juggling. Open a tab, get a bean; close it, it's gone. A clean AI layer — EasyAI One fluent entry point over LangChain4j. Chat, tools, agents, and structured extraction — provider-agnostic, so the model behind it is a config detail. // A typed assistant with a business service exposed as tools OrdersAssistant ai = EasyAI.assistant(OrdersAssistant.class) .withTools(orderService) .build(); String reply = ai.ask("cancel order ORD-002"); You opt methods in as tools explicitly — no accidental exposure: @EasyTool("Cancels an active order") public String cancelOrder(String orderId) { ... } Deterministic pipelines — flow() Agents are powerful but unpredictable. When you want a repeatable, testable process, flow() lets you own the steps and call the model only at the edges that actually need language: EasyAI.flow() .step("understand", ctx -> EasyAI.extract(OrderRequest.class).from(ctx.inputText())) .step("checkStock", ctx -> inventory.check(ctx.get("understand", OrderRequest.class))) .step("place", ctx -> orders.place(ctx.get("understand", OrderRequest.class))) .build() .run(userText); Your logic stays in plain Java. The LLM does one job: turn language into structure. Ambient Activity Memory The framework quietly records what the user does in the app — opening a record, running a search — and makes that timeline available to the assistant. So deixis just works: @ActivityTracked(type = BUSINESS_ACTION, verb = "view", entityType = "order", entityIdParams = "orderId") public String viewOrder(String orderId) { ... } Now the user can open an order and type "cancel this" — no id — and the assistant resolves "this" from what it just saw them do. The proactive assistant This is the piece you normally only see in Copilot, Gmail's Smart Compose, or Notion AI — and almost never as a first-class pattern in a Java web framework. Built on Ambient Memory, the app can offer the next useful step before you ask. Open two orders for the same customer, and a dismissible chip appears: "Looking at several Acme orders — want a quick account summary?" The important part: it's not a black-box agent watching you. A small, deterministic rule — plain Java you write and unit-test — decides if and what to suggest. The model only phrases the sentence. public interface SuggestionRule { Optional evaluate(List recent); } Detect synchronously (cheap, predictable), phrase-and-push asynchronously, with a per-user cooldown so it's helpful and never naggy. Deterministic code decides; the model is reserved for the one thing it's good at. The UI, handled — pf-modern-template A self-contained PrimeFaces template: responsive layout, light/dark/dim themes, a transport-agnostic AI panel (chat + live activity over SSE), a command palette, and now proactive suggestion chips. Drop-in — no build dependency. Getting started The fastest path is the starter — a pre-wired WAR you clone and deploy. Or add the library to an existing Jakarta EE 11+ project: io.github.tabforgeai tabforge-ai 3.1.0 Chat- and tools-only apps stay lean; RAG and vector-store integrations are optional add-ons you pull in only if you use them. The philosophy One idea runs through all of it: let deterministic code decide, and reserve the model for the irreducible — language. That's what makes AI in a serious enterprise app predictable, testable, and safe. Proactive UX just arrived, first-class, in the Java stack. The library demo app ready to use starter All OpenSource