TypeScript browser agent library. AI drives the browser once — replay the navigation with zero LLM tokens.

@lightfeed/browser-agent is a TypeScript browser agent library built for cutting LLM token use on every rerun.

Most browser-agent work has two parts:

• Navigation — many clicks / types / scrolls to reach a target page. Most of the steps, most of the tokens, usually the same every run if the page structure is stable. Today's agents pay for these tokens every single time.
• Extraction — pull typed data out of whatever is on screen. Must re-run AI each time because the content is live.

This library lets you run navigation once with AI, save it as a plan, and replay it with zero LLM calls — no screenshots, no DOM map, no tokens. Then run a cheap .extract() on the result page for the dynamic tail. If the DOM drifts, optional aiFallback re-plans only the broken step, so you still pay tokens for a fraction of the flow instead of all of it.

Runs anywhere your browser lives — the same BrowserAgent API drives a local Chromium for dev, a serverless Chromium (AWS Lambda via @sparticuz/chromium) for scheduled jobs, or a remote CDP endpoint (Brightdata Scraping Browser, any browser farm, or your own). Swap backends by changing one config field; prompts, plans, and .extract() calls stay identical.

npm install @lightfeed/browser-agent

Go to the Hacker News Show section, click through to the next page, and grab the top 3 posts. Navigation (open Show, paginate) is the expensive-but-stable part; extraction is the live-data part.

import { BrowserAgent } from "@lightfeed/browser-agent";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import { z } from "zod";

const agent = new BrowserAgent({
  browserProvider: "Local",
  llm: new ChatGoogleGenerativeAI({ model: "gemini-2.5-flash" }),
});

const page = await agent.newPage();

// 1. AI navigation — recordable, replayable.
const nav = await page.ai(
  "Go to Hacker News show section, go to next page"
);
await agent.savePlan("hn show page 2", nav, "./hn.plan.json");

// 2. AI extraction — typed by a Zod schema, runs AI every call.
const { articles } = await page.extract(
  "The top 3 articles on this page",
  z.object({
    articles: z
      .array(
        z.object({
          title: z.string(),
          url: z.string(),
          points: z.number(),
          commentsUrl: z.string(),
        })
      )
      .max(3),
  })
);

Every subsequent run — navigation is free:

await agent.replay("./hn.plan.json", { page });   // zero tokens
const { articles } = await page.extract(/* ... */); // tokens only here

Everything above is available without writing code:

# Record while running
browser-agent-cli run --save-plan ./hn.plan.json \
  -c "Go to Hacker News show section, go to next page and find top 3 articles"

# Replay: deterministic navigation (no LLM), then one fresh AI pass on the
# result page to produce an up-to-date final response. The navigation part
# is free; only the final pass spends tokens.
browser-agent-cli replay ./hn.plan.json

# Pure replay — skip the final AI pass and just get the browser onto the
# result page (zero LLM calls end-to-end).
browser-agent-cli replay ./hn.plan.json --no-ai-finish

# Use a different finishing task (e.g. ask for a custom summary of the
# current page instead of re-running the recorded task).
browser-agent-cli replay ./hn.plan.json \
  --finish-task "Return the titles of the first 3 posts as a bullet list"

# Self-heal drifted steps during replay (independent of the finish pass).
browser-agent-cli replay ./hn.plan.json --ai-fallback

LLM auto-detected from GOOGLE_API_KEY / GEMINI_API_KEY → OPENAI_API_KEY → ANTHROPIC_API_KEY. Override the model with --llm-model or GEMINI_MODEL / OPENAI_MODEL / ANTHROPIC_MODEL. replay only needs an LLM with --ai-fallback. Interactive: ctrl+p pause, ctrl+r resume.

The same BrowserAgent API works against three backends.

const agent = new BrowserAgent({ browserProvider: "Local" });

const agent = new BrowserAgent({
  browserProvider: "Remote",
  remoteConfig: {
    browserWSEndpoint: "ws://your-remote-browser:9222/devtools/browser/ws",
  },
});

import { BrowserAgent } from "@lightfeed/browser-agent";
import chromium from "@sparticuz/chromium";

const agent = new BrowserAgent({
  browserProvider: "Serverless",
  serverlessConfig: {
    executablePath: await chromium.executablePath(),
    options: { args: chromium.args },
  },
});

All three drive the browser with AI, return the same TaskOutput, and can be recorded + replayed.

• agent.savePlan(task, result, path) writes a JSON plan with the action sequence and a stable xpath + cssPath for each clicked / typed element.
• agent.replay(path, { page }) re-runs those actions with no LLM calls, no screenshots, no DOM map.
• aiFallback: true re-plans only a drifted step with the LLM; the rest stays free.
• startingUrl (option, or --url on the CLI) retargets a plan at a different URL — useful for staging / preview deploys / different queries.
• Plans are human-readable and hand-editable (tweak an inputText value, reorder or delete steps).

The output string the model produced while recording is frozen in the plan — the programmatic agent.replay() does not regenerate it. The CLI's replay command, by default, runs one fresh AI pass (page.ai(plan.task, { maxSteps: 3 })) on the result page after navigation so every CLI run ends with an up-to-date response; pass --no-ai-finish to get pure token-free replay and fall back to the recorded output. If you're wiring this up programmatically, run your own .extract() / .ai() on the page after agent.replay() instead of relying on the recorded output.

MIT. Forked from HyperAgent (b49afe). Serverless browser support by @sparticuz/chromium.