/output-dev-agent-class

name: output-dev-agent-class
description: Use the Agent class for multi-step tool loops, conversation history, streaming progress, and reusable LLM agents. Use when building agents with skills, structured output, stateful conversations, or streaming callbacks.
allowed-tools: [Read, Write, Edit]

Using the Agent Class

Overview

The `Agent` class uses an internal AI SDK `ToolLoopAgent` through composition with Output prompt files and the skills system. It does not inherit from `ToolLoopAgent`. Use it when you need multi-step tool execution, conversation history, or a reusable agent instance. For single-shot LLM calls without tools, `generateText` is simpler.

When to Use This Skill

• Building multi-step agents that call tools in a loop
• Using skills (lazy-loaded instructions) with an agent
• Creating agents with structured output via `aiSdk.Output.object()`
• Implementing stateful conversations with `messageStore`
• Streaming Agent progress with `onChunk`
• Deciding between `Agent` and `generateText`

Import Pattern

import { Agent, aiSdk } from '@outputai/core/llm';
import type { MessageStore } from '@outputai/core/llm';
import { z } from '@outputai/core';

`Agent` comes from `@outputai/core/llm`. Use `aiSdk.Output` for structured output. Import `z` from `@outputai/core` (never from `zod` directly). `MessageStore` is the type for a pluggable `getMessages` / `addMessages` store; implement it yourself.

Construction

The prompt file is loaded and rendered at construction time. Variables and tools are fixed at construction. Skills and `maxSteps` come from the prompt file. The agent is ready to call `generate()`, `generateWithStreaming()`, or `stream()` immediately.

const agent = new Agent( {
  prompt: 'writing_assistant@v1',
  variables: {
    content_type: input.contentType,
    focus: input.focus,
    content: input.content
  },
  output: aiSdk.Output.object( { schema: reviewSchema } )
} );

Constructor Options

| Option | Type | Default | Description | |--------|------|---------|-------------| | `prompt` | `string` | *(required)* | Prompt file name (e.g. `'writing_assistant@v1'`) | | `promptDir` | `string` | - | Override the stack-resolved prompt directory | | `variables` | `PromptVariables` | - | Template variables rendered at construction | | `tools` | AI SDK tools | - | Caller tools; merged with prompt YAML tools (`load_skill` last) | | `stopWhen` | function or function[] | - | Custom stop condition (overrides prompt `maxSteps` when tools exist) | | `output` | `aiSdk.Output` | - | Structured output spec (e.g. `aiSdk.Output.object({ schema })`) | | `messageStore` | `MessageStore` | - | Pluggable store for multi-turn history |

generate()

Run the agent and return when complete:

const result = await agent.generate();
console.log( result.text );   // Generated text
console.log( result.output ); // Structured output (when using aiSdk.Output.object)
console.log( result.usage );  // Token counts

The result has the same shape as `generateText`: `text`, `result` (alias for `text`), `output`, `usage`, `finishReason`, `toolCalls`, etc.

Passing Additional Messages

Extend the conversation with extra messages:

const result = await agent.generate( {
  messages: [ { role: 'user', content: 'Focus on the introduction section.' } ]
} );

Messages are appended after the initial prompt messages (and any message-store history). You can also pass `abortSignal` and `toolChoice`.

generateWithStreaming()

Use `generateWithStreaming()` when you need progress callbacks and a complete result:

const result = await agent.generateWithStreaming( {
  onChunk( { chunk } ) {
    if ( chunk.type === 'text-delta' ) {
      process.stdout.write( chunk.text );
    }
  }
} );

The method behaves like `generate()` while using streaming internally. It returns the complete response, rejects on stream errors, and automatically appends messages to the configured message store. It accepts the same `messages`, `abortSignal`, and `toolChoice` as `generate()`, plus `onChunk`. Prefer it over `stream()` in Temporal activity steps unless direct access to the stream result is required.

stream()

Use `stream()` when direct control over `textStream` or `stream` is required. It accepts the same `messages`, `abortSignal`, and `toolChoice` as `generate()`, plus `onChunk`, `onEnd`, and `onError`:

const stream = await agent.stream();

for await ( const chunk of stream.textStream ) {
  process.stdout.write( chunk );
}

Like `streamText`, the stream result provides `textStream` and `stream` iterables, plus promise-based properties (`text`, `usage`, `finishReason`) that resolve on completion.

`stream()` appends messages to the message store in its wrapped `onEnd` when `finishReason` is not `'error'`. See `output-dev-llm-streaming` for streaming and error-handling guidance.

Structured Output

Use `aiSdk.Output.object()` to get typed responses:

const reviewSchema = z.object( {
  issues: z.array( z.string() ).describe( 'List of issues found' ),
  suggestions: z.array( z.string() ).describe( 'Actionable suggestions' ),
  score: z.number().describe( 'Quality score 0-100' ),
  summary: z.string().describe( 'Brief overall assessment' )
} );

const agent = new Agent( {
  prompt: 'writing_assistant@v1',
  variables: { content_type: 'documentation', focus: 'clarity', content: markdownContent },
  output: aiSdk.Output.object( { schema: reviewSchema } )
} );

const { output } = await agent.generate();
// output: { issues: string[], suggestions: string[], score: number, summary: string }

Use `.describe()` on schema fields instead of `.min()/.max()` for number constraints. Anthropic does not support `minimum`/`maximum` JSON Schema constraints in tool definitions.

Message Store

By default, Agent is stateless. Each `generate()` call starts fresh with only the initial prompt me