nodetool-browser-agent

name	nodetool-browser-agent
description	Create browser automation agents that navigate websites, extract data, fill forms, and perform multi-step web tasks using natural language instructions. Use when user asks to automate browsing, scrape websites with AI, build a web agent, or perform complex browser interactions.

You help users create NodeTool agents configured for browser automation — AI-powered web agents that navigate, interact with, and extract data from websites using natural-language task descriptions.

Architecture

Browser automation in NodeTool uses the agent system. An agent equipped with the browser tool can navigate pages, capture screenshots, download files, and extract content as part of a planned, multi-step task.

Task description → Agent → (Plan steps → Use browser tools → Extract/interact → Report) → Result

Available Browser Tools

These are the web-related tools the agent belt offers. Pass them to nodetool agent run --tools to narrow the belt to just these:

Tool name	Description
browser	Fetch and render web page content (executes JavaScript), extract text/links
screenshot	Capture a screenshot of a page
download_file	Download a file from a URL into the workspace
web_search	Web search to discover URLs
google_news / google_images	News / image search
http_request	Raw HTTP GET/POST for APIs and simple fetches

There are no dom_examine, dom_search, or dom_extract tools. The browser tool handles rendering and content extraction; instruct the agent in natural language (e.g. "extract every product title and price") and pair it with write_file to save structured results.

CLI Usage

nodetool agent takes arguments only — there is no config file. Put the instructions in the objective, and narrow the toolbelt with --tools.

# Scrape a page
nodetool agent run -p openai -m gpt-5.4 \
  --tools browser,write_file \
  --objective "Go to example.com, extract every product name and price, and save them as JSON with write_file"

# Research a topic — final answer to stdout, trace to stderr
nodetool agent run -p openai -m gpt-5.4 \
  --tools web_search,browser,write_file \
  --objective "Research the latest developments in WebAssembly. Search for sources, browse the promising ones, and compile a structured report." \
  > research-report.md

# Compare prices
nodetool agent run -p openai -m gpt-5.4 \
  --tools web_search,browser,write_file \
  --objective "Compare the price, availability, and shipping of <product> across three retailers, then recommend the best deal"

# Objective via stdin
echo "Screenshot example.com and describe the layout" | \
  nodetool agent run -p openai -m gpt-5.4 --tools browser,screenshot

Omit --tools to give the agent the whole default belt.

Browser Agent as a Workflow Node

For visual workflows (and the DSL), there is a dedicated BrowserAgent node (agents.browserAgent in @nodetool-ai/dsl). It runs a browsing agent inside a graph and returns the extracted text. Use this when browsing is one step of a larger pipeline rather than a standalone CLI run.

Programmatic Usage (TypeScript)

import { Agent } from "@nodetool-ai/agents";
import { BrowserTool, ScreenshotTool, DownloadFileTool } from "@nodetool-ai/agents";
import { ProcessingContext, FileStorageAdapter } from "@nodetool-ai/runtime";

const agent = new Agent({
  name: "browser-agent",
  objective: "Extract product listings from example.com",
  provider: openaiProvider,
  model: "gpt-5.4",
  tools: [new BrowserTool(), new ScreenshotTool(), new DownloadFileTool()],
  workspace: "/tmp/browser-output",
  maxSteps: 15,
});

const ctx = new ProcessingContext({
  jobId: `browser-${Date.now()}`,
  userId: "1",
  workspaceDir: "/tmp/browser-output",
  workspaceStorage: new FileStorageAdapter("/tmp/browser-output"),
});

  ( message  agent.(ctx)) {
   (message. === ) process..(message.);
}

Tips

• Describe extraction in natural language — the browser tool returns page content; let the model parse it. Pair with write_file to persist results.
• Use screenshot to debug visual state or verify a page loaded.
• Set max_steps higher (15-20) for multi-page tasks.
• Combine with google_search when the agent needs to discover URLs first.
• Use http_request for JSON APIs — it's faster and cheaper than full page rendering.