Split Getting Started into a focused four-page series

.changeset/quick-get-started.md

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+---
+"@agent-native/core": patch
+---
+
+Split Getting Started into a focused four-page series with visual improvements

packages/core/docs/content/getting-started-actions.mdx

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+---
+title: "Add an Action"
+description: "Define your first action and render its result as a UI component directly inside the chat transcript."
+---
+
+# Add an Action
+
+This is part two of the Getting Started series. In [Getting Started](/docs/getting-started) you created a Chat app and connected an AI engine. Here you'll define your first action and render its result inline in chat.
+
+## Add an action {#add-an-action}
+
+An action is a typed operation that both your agent and your UI can call. It's
+how the agent does things in your app. Actions live in the `actions/` directory
+and can be triggered from chat, from React components, from the CLI, or on a
+schedule. You define them once and call them from anywhere.
+
+### Try the starter action
+
+The Chat template includes a `hello` action at `actions/hello.ts`:
+
+```ts filename="actions/hello.ts"
+import { defineAction } from "@agent-native/core/action";
+import { z } from "zod";
+
+export default defineAction({
+  description: "Return a friendly greeting.",
+  schema: z.object({
+    name: z.string().default("world").describe("Name to greet"),
+  }),
+  http: { method: "GET" },
+  run: async ({ name }) => {
+    return { message: `Hello, ${name}!` };
+  },
+});
+```
+
+Run it from the terminal (inside your `my-app/` directory):
+
+```bash
+pnpm action hello --name Alice
+```
+
+Or open your app at `http://localhost:8080` and ask the agent in the chat there:
+
+> Use the hello action with the name Alice.
+
+### Add your own action
+
+Replace the starter action with the first real operation in your domain. This example
+counts words, sentences, and paragraphs in any text you pass it. It computes
+everything locally, so there's nothing to configure and no external service to connect.
+
+Create a new file called `analyze-text.ts` in your `actions/` directory:
+
+```ts filename="actions/analyze-text.ts"
+import { defineAction } from "@agent-native/core/action";
+import { z } from "zod";
+
+const textStatsSchema = z.object({
+  title: z.string(),
+  points: z.array(z.object({ label: z.string(), value: z.number() })),
+});
+
+export default defineAction({
+  description: "Count words, sentences, and paragraphs in a block of text.",
+  schema: z.object({
+    text: z
+      .string()
+      .default(
+        "The quick brown fox jumps over the lazy dog. Pack my box with five dozen liquor jugs.",
+      ),
+  }),
+  outputSchema: textStatsSchema,
+  chatUI: {
+    renderer: "text.stats-chart",
+    title: "Text stats",
+  },
+  readOnly: true,
+  run: async ({ text }) => ({
+    title: "Text statistics",
+    points: [
+      { label: "Characters", value: text.length },
+      { label: "Words", value: text.split(/\s+/).filter(Boolean).length },
+      {
+        label: "Sentences",
+        value: text.split(/[.!?]+/).filter(Boolean).length,
+      },
+      {
+        label: "Paragraphs",
+        value: text.split(/\n\n+/).filter(Boolean).length,
+      },
+    ],
+  }),
+});
+```
+
+Try it from the terminal:
+
+```bash
+pnpm action analyze-text --text "Hello world. How are you today?"
+```
+
+Or open your app at `http://localhost:8080` and ask the agent in the chat there:
+
+> Run the analyze-text action on "Hello world. How are you today?"
+
+#### Define once, call from anywhere
+
+This action is now reachable from chat, React hooks, CLI, HTTP, MCP, A2A,
+scheduled jobs, and webhooks.
+
+TIP: Any time you want the agent to call a specific action without ambiguity, phrasing it as "Run the `<action-name>` action" is most reliable. Natural-language prompts work well once the agent has enough context about your app's domain. For a brand-new app with no data or context yet, explicit is safer.
+
+## Render the result inline {#render-inline}
+
+When the agent runs `analyze-text`, it returns structured data: a title and an
+array of counts. By default the agent will describe that data in prose: "The
+text has 9 words, 2 sentences..." and so on. That works, but you can
+also render the result as a real UI component (a bar chart, a table, a card)
+directly inside the chat transcript, right where the agent responded.
+
+This is what `chatUI.renderer` in the action does. It's a label that says "when
+this action's result appears in chat, hand it to this React component instead of
+summarizing it in text." The component receives the validated action output as
+props and renders whatever you want.
+
+In the next step, you'll create `app/chat-renderers.tsx`, but first, add one import line
+to `app/root.tsx` so it runs on startup:
+
+```ts filename="app/root.tsx"
+import "./chat-renderers";
+```
+
+Add it alongside your other imports at the top of the file. That's the only
+change to `root.tsx`. The import just ensures the file runs and registers the
+renderer. Now create the renderer file:
+
+```tsx filename="app/chat-renderers.tsx"
+import {
+  registerActionChatRenderer,
+  type ToolRendererProps,
+} from "@agent-native/core/client/chat";
+
+type TextStatsResult = {
+  title: string;
+  points: Array<{ label: string; value: number }>;
+};
+
+const MAX_BAR_PX = 80;
+
+function TextStatsChart({ context }: ToolRendererProps) {
+  const result = context.resultJson as TextStatsResult;
+  const max = Math.max(...result.points.map((point) => point.value), 1);
+  return (
+    <section className="rounded-lg border bg-card p-4">
+      <h3 className="text-sm font-medium">{result.title}</h3>
+      <div className="mt-4 flex items-end gap-2">
+        {result.points.map((point) => (
+          <div
+            key={point.label}
+            className="flex flex-1 flex-col items-center gap-2"
+          >
+            <div
+              className="w-full rounded-t bg-blue-500"
+              style={{
+                height: `${Math.max(Math.round((point.value / max) * MAX_BAR_PX), 2)}px`,
+              }}
+            />
+            <span className="text-xs text-muted-foreground">{point.label}</span>
+          </div>
+        ))}
+      </div>
+    </section>
+  );
+}
+
+registerActionChatRenderer({
+  id: "text.stats-chart",
+  renderer: "text.stats-chart",
+  Component: TextStatsChart,
+});
+```
+
+Once the renderer is registered, the agent's response looks like this. Instead
+of a paragraph of text, your React component renders directly inside the chat
+transcript:
+
+<WireframeBlock id="doc-block-inline-result-wireframe">
+  <Screen
+    surface="desktop"
+    html={
+      "<div style='min-height:340px;box-sizing:border-box;padding:24px;display:flex;justify-content:center;align-items:center;background:var(--wf-bg)'><div style='width:min(640px,100%);display:flex;flex-direction:column;gap:14px'><div class='wf-card' data-rough style='align-self:flex-end;max-width:70%;padding:12px 14px'><strong>User</strong><p style='margin:6px 0 0'>Run the analyze-text action on \"Hello world. How are you today?\"</p></div><div class='wf-card' data-rough style='align-self:flex-start;width:min(480px,100%);padding:14px'><strong>Agent</strong><p class='wf-muted' style='margin:6px 0 12px'>Rendered with text.stats-chart.</p><section class='wf-card' data-rough style='padding:14px'><h3 style='margin:0 0 12px;font-size:14px'>Text statistics</h3><div data-rough='line:bottom' style='height:104px;display:flex;align-items:end;gap:8px;border-bottom:1.4px solid var(--wf-line);padding-bottom:4px'><div style='flex:1;display:flex;flex-direction:column;align-items:center;gap:6px'><div data-rough style='height:80px;width:100%;background:color-mix(in srgb, var(--wf-accent) 36%, transparent);border:1.4px solid var(--wf-accent);border-radius:8px 8px 3px 3px'></div><span class='wf-muted'>Characters</span></div><div style='flex:1;display:flex;flex-direction:column;align-items:center;gap:6px'><div data-rough style='height:18px;width:100%;background:color-mix(in srgb, var(--wf-accent) 30%, transparent);border:1.4px solid var(--wf-accent);border-radius:8px 8px 3px 3px'></div><span class='wf-muted'>Words</span></div><div style='flex:1;display:flex;flex-direction:column;align-items:center;gap:6px'><div data-rough style='height:2px;width:100%;background:color-mix(in srgb, var(--wf-accent) 24%, transparent);border:1.4px solid var(--wf-accent);border-radius:8px 8px 3px 3px'></div><span class='wf-muted'>Sentences</span></div><div style='flex:1;display:flex;flex-direction:column;align-items:center;gap:6px'><div data-rough style='height:2px;width:100%;background:color-mix(in srgb, var(--wf-accent) 24%, transparent);border:1.4px solid var(--wf-accent);border-radius:8px 8px 3px 3px'></div><span class='wf-muted'>Paragraphs</span></div></div></section></div></div></div>"
+    }
+  />
+</WireframeBlock>
+
+Use this step when the result belongs where the agent is speaking:
+
+- setup summaries
+- short reports
+- approvals
+- tables or charts small enough to inspect inline
+- links into durable app views
+
+For reusable generic outputs, the framework also ships built-in
+`data-chart` and `data-table` renderers, plus `data-insights` for combined
+summary/chart/table cards. See [Native Chat UI](/docs/native-chat-ui). For
+temporary controls the agent creates at runtime, see
+[Generative UI](/docs/generative-ui).
+
+## What's next {#next}
+
+<Cards>
+
+### [Persist Data in SQL](/docs/getting-started-database)
+
+Save action results to a database so the agent can reference them across
+conversations. Next in the series.
+
+### [Actions](/docs/actions)
+
+Schemas, auth, approvals, hooks, and transport — the full reference for what
+actions can do.
+
+### [Native Chat UI](/docs/native-chat-ui)
+
+Built-in renderers for tables, charts, and typed cards — beyond the custom
+renderer you just built.
+
+### [Key Concepts](/docs/key-concepts)
+
+The architecture underneath this tutorial: SQL, actions, live sync, and context
+awareness.
+
+</Cards>