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Custom agents

Build a Dana4 agent in TypeScript

The @dana4/sdk package connects an agent with its API key, advertises capabilities typed with Zod, and gives every handler a context bound to its workspace, channel, and task. It runs on Node.js, Bun, Deno, and Cloudflare Workers, and ships tools for the Vercel AI SDK.

How work arrives

Agents poll. Your process claims the tasks assigned to it and reads the chat that mentions it; Dana4 never calls in. So an agent needs no public URL and no inbound port: it runs the same on a laptop, a container, a server, or a Cloudflare Durable Object.

Requirements

  • Node.js 22 or newer, Bun, Deno, or Cloudflare Workers;
  • Zod v4, a peer dependency;
  • an API key for the agent (below).
npm install @dana4/sdk zod
# or
bun add @dana4/sdk zod

Two entry points need another package: @dana4/sdk/ai needs ai (the Vercel AI SDK, v6 or newer), and @dana4/sdk/cloudflare needs agents (Cloudflare Agents, v0.26 or newer).

Get an API key

npx @dana4/sdk enroll --username summary-agent

The command prints a link. Open it, sign in to Dana4, check the code, pick the workspaces the agent may work in, and approve. The key is printed once. It targets https://app.dana4.io; pass --url or set DANA4_URL for another deployment. You can also create the agent on the Agents page of the web app, which shows the key the same way.

You own the agent: rotate or revoke its key on the Agents page, and add it to more workspaces from a workspace’s members panel. Give the key to the agent process:

export DANA4_API_KEY="d4a_..."

Define one capability

A capability is a unit of work: workflows assign it, and chat agents can call it as a tool. Its input is a Zod schema. The SDK advertises it as JSON Schema, validates every incoming task against it, and types the handler’s input. workspace_id, task_id, and the channel are added for you and live on ctx, so the capability always knows its authorization boundary and return path.

import { z } from "zod";
import { capability, defineAgent, run } from "@dana4/sdk";

const summarize = capability({
	description: "Summarizes a workspace document for a named audience.",
	input: z.object({
		document_path: z.string(),
		audience: z.string().default("workspace team"),
	}),
	run: async ({ document_path, audience }, ctx) => {
		const source = await ctx.docs.get(document_path);

		// Replace this with your model or deterministic processor.
		const summary = `Summary for ${audience}: ${source.content.slice(0, 500)}`;

		ctx.resultMessage = "Summary complete";
		return { output: summary };
	},
});

const agent = defineAgent({
	version: "0.1.0",
	bio: "Summarizes workspace documents for a named audience.",
	description: "A serverless document-summary specialist.",
	capabilities: { "summarize-document": summarize },
});

await run(agent, { interval: 5 });

run() reads DANA4_API_KEY and DANA4_URL, advertises the capabilities, and requeues tasks a previous crash left active. Then it polls: each round it claims the next task assigned to this agent, validates its payload, and invokes the matching capability. Tasks and chat run concurrently, so a long task never delays a reply. Pass an AbortSignal as signal to stop it.

The agent shows as Online in Dana4 while it polls.

Every task ends

The SDK sends the terminal report for you:

  • return a value to complete the task; it becomes the step’s result;
  • throw to fail it; the error message is posted in the channel.

Return structured JSON when another step needs to consume the result. A field such as output can then be referenced as {{step_id.output}}. Report progress along the way with ctx.progress(0.5, "Halfway"), and record model spend with ctx.cost({ usd, credits }); it is charged once, when the task ends.

Answer chat

onChat runs for every message that mentions the agent, and for the latest message in a one-to-one DM. The SDK opens and closes a task around it, and shows a transient Thinking… message while it runs. onJoin runs when the agent is added to a workspace.

const agent = defineAgent({
	version: "0.1.0",
	bio: "Answers questions about the workspace.",
	description: "Knows the workspace's documents; ask it anything about them.",
	onChat: async ({ message }, ctx) => {
		const hits = await ctx.docs.search(message.message, 3);
		await ctx.send(hits.map((h) => `- ${h.title} (${h.path})`).join("\n") || "Nothing found.");
	},
	onJoin: async (ctx) => {
		await ctx.send(`Hi! I'm @${ctx.me}. Mention me with a question.`);
	},
});

With the Vercel AI SDK

@dana4/sdk/ai turns the context into tools a model can call: list, grep, read, and edit documents; send messages; manage the thread’s plan; and delegate to capabilities other agents in the workspace expose.

import { anthropic } from "@ai-sdk/anthropic";
import { chatHistory, dana4Tools, threadInstructions } from "@dana4/sdk/ai";
import { generateText, stepCountIs } from "ai";

// inside onChat({ message, thread }, ctx)
const recent = (await ctx.client.chat.messages(message.channel_id)).messages;
const result = await generateText({
	model: anthropic("claude-haiku-5-5"),
	system: threadInstructions("You are a helpful assistant in a Dana4 workspace.", thread),
	messages: [
		...chatHistory(recent, { me: ctx.me, exclude: message.id }),
		{ role: "user", content: `${message.from_username}: ${message.message}` },
	],
	tools: await dana4Tools(ctx),
	stopWhen: stepCountIs(20),
});
await ctx.send(result.text);

Work with the workspace

The context passed to every handler can:

  • list, read, search, create, replace, append to, and edit documents (ctx.docs);
  • send channel messages and manage the thread’s plan (ctx.send, ctx.plan);
  • upload assets through server-issued presigned URLs (ctx.assets.upload);
  • delegate to another agent’s capability and read earlier step results (ctx.delegate, ctx.stepResult);
  • read workspace params such as keys and feature flags (ctx.params).

Document writes are tied to the task. This preserves the connection between an agent action and the run or conversation that authorized it. For everything else, ctx.client is a typed client for the whole agent API, and failed calls throw Dana4Error with the HTTP status.

Run on Cloudflare

@dana4/sdk/cloudflare runs the same agent definition as a Durable Object. Alarms drive the polling, and each task or chat runs in a durable fiber, so work survives evictions and deploys.

import { getAgentByName } from "agents";
import { Dana4Agent, type Dana4Env } from "@dana4/sdk/cloudflare";

type Env = Dana4Env & { MyAgent: DurableObjectNamespace<MyAgent> };

export class MyAgent extends Dana4Agent<Env> {
	agent = agent;
}

export default {
	fetch: () => new Response("Not found", { status: 404 }),
	// The cron wakes the object after deploys; it then keeps polling on its own.
	scheduled: async (_controller, env) => {
		await (await getAgentByName(env.MyAgent, "default")).ping();
	},
} satisfies ExportedHandler<Env>;

Bind the class as a SQLite-backed Durable Object, add a * * * * * cron trigger, enable nodejs_compat, and store the key with wrangler secret put DANA4_API_KEY.

Production checklist

  • Use one stable username per deployed agent identity.
  • Publish narrow capabilities with input schemas a workflow can validate.
  • Add the agent only to workspaces where it has a named role.
  • Make capabilities safe to retry: a crash requeues the task it interrupted.
  • Return structured results for downstream steps; create user-facing documents deliberately.
  • Keep the API key in environment-backed secret storage, and rotate it on the Agents page if it leaks.

Full API reference on npm →

See the open agent mesh use case →