jev-browser

Browser automation where an LLM plans and Jev decides.

Unofficial project, not affiliated with TypeSafe. It calls the TypeSafe System One API with your own API key.

The calling LLM (Claude, via MCP) says what outcome it wants, one step at a time, and hands over any text to type. For each round of a step, code describes the page. Then one ~300 ms Typesafe System One request asks Jev several questions at once: which element, which action, which value, and is the step done / blocked / showing an error / about to do something irreversible. Playwright performs the action. The LLM never reads page snapshots unless it chooses to take over.

Claude ── browser_do("Log in", {email, password}) ──▶ jev-browser
                                                       │  loop until done / stuck / needs confirmation
                                                       │   1. settle   (network + DOM quiet)
                                                       │   2. describe (elements, labels, state, visible text, diff, counts)
                                                       │   3. Jev      (done? error? irreversible? tool? target? value?)
                                                       │   4. act      (Playwright)
Claude ◀── { status: "done", url, actions[], done_score } ─┘

https://github.com/user-attachments/assets/2e688df9-4985-4854-8ebe-ba97c9d13d68

Jev only answers with probability distributions: yes/no (noul), pick one option (choice) or a rating (score). It never writes text. So everything free-form comes from the caller as candidates, and code turns disagreement or low confidence into a status the LLM can act on.

Results

42 tasks in 16 categories on live sites (see RESULTS.md):

• 40/42 correct in the latest run, 0 false "done" claims (38/41 twice before the latest fixes). Remaining misses: counting ("add until 3", flagged likely_done) and verifying a sort.

• ~300 ms per Jev call, 2–4 calls for most steps; a 5-step checkout takes ~14 s end to end.

• Pause before irreversible actions: across ~200 rounds it flagged only saucedemo's "Finish" (place order) button.

• Less page content for the LLM than a Playwright-MCP-style loop on the same tasks (estimate): median 5× per task, ≈8k vs ≈557k tokens in total. Big pages dominate the total: a Wikipedia article is ~149k snapshot tokens. On tiny pages there is no saving. The page reading moves to Jev.

Works on: forms, native and custom dropdowns, checkboxes and radios (including styled replacements), dynamic loading, modals, JS dialogs, hover, right-click, drag and drop, key presses, file upload, iframes, shadow DOM, new tabs, pages with 2,000+ elements (two-stage selection), and non-English UIs.

Known limits, so write steps around them:

• Ordered sub-goals in one step ("add two todos, complete one, clear completed") → split into one outcome per step.

• Open-ended goals ("scroll to load more") → make them measurable or check yourself.

• Judgements that compare many values (is this table sorted, did exactly one thing change) → verify with browser_check or browser_snapshot.

Related MCP server: playwright-mcp

Quick start (MCP, from npm)

npx playwright install chromium          # once
claude mcp add jev-browser -e TYPESAFE_API_KEY=your-key -- npx -y -p jev-browser jev-browser-mcp

Any MCP client works the same way: command npx, args -y -p jev-browser jev-browser-mcp, env TYPESAFE_API_KEY. Add JEV_BROWSER_HEADED=1 to watch it work.

Setup (from source)

git clone https://github.com/Ying-Kai-Liao/jev-browser && cd jev-browser
npm install
npm run setup                    # downloads Chromium for Playwright
cp .env.example .env             # add TYPESAFE_API_KEY
npm test                         # offline tests (no network, no key)
npm run test:e2e                 # MCP server end to end (network + key)

Use from Claude Code (MCP, from source)

claude mcp add jev-browser -- node /absolute/path/to/jev-browser/bin/jev-browser-mcp.mjs

From a source checkout the server reads TYPESAFE_API_KEY from the repo's .env.

Reading a page: browser_read

browser_snapshot is the take-over-and-act surface: numbered elements, plus the text in the viewport. That is the right trade for acting, and it is already small (99–6,900 tokens on every page measured, because elements are capped at 400 and the text at 2,500 chars). It is a poor way to read, though — a Wikipedia article is ~179k chars of text, so a snapshot shows about 1.4% of it and the rest costs a scroll-and-snapshot loop.

browser_read(question) is the understand-the-content surface. Code extracts the whole document's text and cuts it at headings and sections; Jev rates every block against the question in batches of 40; code returns the blocks that help, in document order, inside a character budget. Jev ranks, it never writes — the text you get back is the page's own.

When it pays. Long articles, documentation and reference pages. On the WW2 article, "When and why did Japan surrender?" returned 11.8k of 178.5k chars — 15× less — in 3.4 s over 5 Jev calls, keeping the atomic-bomb and surrender paragraphs.

When it doesn't. Short pages, apps and forms. Below a 8,000-char floor (and below your own max_chars) it returns the page whole and never calls Jev at all, because paying ~1 s to prune a 2k-token page is a loss. Hacker News, a BBC front page and saucedemo all land under the floor.

It is biased to keep: a block Jev cannot rate — or a whole batch whose request failed — is kept, and whatever is left out is counted and marked in place with [… N blocks dropped …]. Nothing is ever silently cut. Use browser_snapshot if you need the page as the caller normally sees it.

node examples/read-demo.mjs                                   # WW2 article, default question
node examples/read-demo.mjs "https://…" "what do you want?"   # needs a key

browser_do statuses:

browser_snapshot options. A snapshot is the one page the calling LLM reads in full, so every option narrows what gets rendered. All of them are pure formatting: nothing is collected differently and none of them costs a Jev call.

A typed password is never printed: input:password values render as [redacted], in a snapshot and in a diff. Measured on a Wikipedia article (1,840 elements), a default snapshot went from 26,919 to 9,926 characters (~6,700 → ~2,500 estimated tokens); filter brings it to ~800 and diff after a scroll to ~50.

Env: JEV_BROWSER_HEADED=1 shows the browser, JEV_BROWSER_PROFILE=/dir keeps a persistent profile (logins survive restarts), JEV_BROWSER_LOG=1 prints per-round decisions to stderr.

Library

// npm install jev-browser && npx playwright install chromium
import { JevBrowser } from "jev-browser";

const b = await JevBrowser.launch({ headed: true });
await b.open("https://www.saucedemo.com/");
await b.do("Log in", { values: { username: "standard_user", password: "secret_sauce" } });
await b.do("Add the Sauce Labs Backpack to the cart");
const p = await b.check("Does the cart badge show 1 item?");   // 0..1
await b.close();

Watch it

node examples/x-profile-demo.mjs     # headed, read-only x.com walkthrough with decision highlights

Each action is outlined in red with Jev's choice and scores before it happens (highlight: true, on by default in the MCP server when JEV_BROWSER_HEADED=1). Some sites, x.com included, serve a blank page to headless Chromium: use headed mode there.

CLI

node bin/jev-browser.mjs do https://the-internet.herokuapp.com/login "Log in" username=tomsmith 'password=SuperSecretPassword!'
node bin/jev-browser.mjs run examples/flows/todomvc.json --headed

Writing good steps

• One observable outcome per step: "Log in", "Open the Pull requests tab", "Mark 'buy milk' completed".

• Every string goes in values with a meaningful key (email, postal_code, file).

• Treat likely_done, needs_confirmation, ambiguous and stuck as your turn: check, snapshot or ask the user, don't just retry.

• After steps with side effects, browser_check what must not have changed.

Cost: what the caller pays and what Jev spends

bin/jev-cost.mjs measures both sides of the trade this project claims to make, on a page you name. Every snapshot, every tool result and every Jev call is measured as it happens — nothing is measured once and multiplied.

npm run cost                                              # the built-in saucedemo + wikipedia cases
npm run cost -- --url https://example.com/ --goal "Log in" --value user=ada --value pw=secret
npm run cost -- --json > run.json                         # machine-readable
npm run cost -- --report run.json                         # re-print the table from a saved run
node bin/jev-cost.mjs --help                              # every flag

Per scenario it reports caller tokens (the text crossing the MCP boundary: every tool result plus the arguments the caller had to write, at chars ÷ 4 — an approximation, and the report says so), Jev tokens and calls (the API's own usage.input_tokens, exact), and wall-clock seconds. The scenarios are run on the same page toward the same goal so they compare: a Playwright-MCP-style baseline that takes a fresh page.ariaSnapshot({ mode: "ai" }) after every action, browser_do, each browser_snapshot option against the default, and browser_read against dumping the whole document.

The net column is caller tokens saved minus Jev tokens spent, one for one, and it goes negative when a scenario loses — on a small page Jev's spend and latency can outweigh the saving, and the tool prints that as a loss. Measured numbers and what they say about the older estimate are in RESULTS.md.

A run needs network and TYPESAFE_API_KEY, and costs API credits. npm test stays offline: the ledger arithmetic is pure and tested in test/cost.test.mjs.

Benchmark

node bench/run.mjs --set all            # base, hard, guard (+ bench/tasks.local.mjs if present)
node bench/run.mjs --only ti-login,drag
node bench/context-cost.mjs bench/results/<run>.json   # older one-sided estimate; see npm run cost

Private sites and credentials go in bench/tasks.local.mjs (git-ignored), exporting LOCAL.

Layout

src/session.mjs      JevBrowser: settle, snapshot, decide (1 or 2 stages), resolve, act, do, check, choose
src/page-script.mjs  runs in each frame: elements, labels, state, visible text, metrics, dialogs
src/page-model.mjs   pure helpers: diff between pages, counts, compact rendering
src/prune.mjs        pure helpers: split page text into blocks, batch, rank, reassemble
src/jev.mjs          System One API client
src/flow.mjs         JSON flow runner
bin/                 CLI and MCP server
bin/jev-cost.mjs     two-sided token ledger: caller tokens vs Jev tokens, measured per scenario
bench/               tasks with ground-truth checks, runner, context-cost estimate
test/                offline fixture tests, MCP end-to-end test
NOTES.md             design notes: what works with Jev, what doesn't, and why

Releasing

CI runs the offline tests on every push and pull request. To publish, bump the version and push the tag; .github/workflows/release.yml tests, publishes to npm with provenance and creates a GitHub release:

npm version patch            # or minor / major: commits and tags vX.Y.Z
git push --follow-tags

Publishing uses npm trusted publishing, set up once with npx npm@latest trust github jev-browser --file release.yml --repo Ying-Kai-Liao/jev-browser --allow-publish.

License

MIT