Semantic Browser

Version 1.6.0 (Beta) · PyPI · Changelog · License: MIT

Semantic Browser turns live Chromium pages into compact, numbered text views for LLM agents. The agent reads one short view (content and its options), replies with one command like click 12, and the runtime executes it on that exact element.

$ sb goto https://shop.example/search?q=anvil
navigated to … -> 'Search: anvil'

@ Search: anvil — https://shop.example/search?q=anvil   [screen 1/1]
# Catalogue
[1 input:search "Search catalogue"="anvil"] [2 button]Search
- [3]Anvil Classic £49.99 In stock
- [4]Anvil Pro £89.50 Low stock

$ sb click 4
clicked [4] 'Anvil Pro' -> now at 'Anvil Pro' (https://shop.example/p/anvil-pro)

Less confusion, less hallucination, dramatically less cost: on 8 live sites the model reads a median ~2.5k tokens per step (1.3k with --budget 2500) instead of ~15k for a raw accessibility snapshot, at ~1.4 s per two-step task with 16/16 success (benchmark report).

Give it to your agent (30 seconds)

pip install "semantic-browser[managed]" && semantic-browser install-browser
sb guide            # the playbook an agent should read: loop, view syntax, recovery, bot walls, safety (~1.5k tokens)

• Any LLM agent: paste the output of sb guide into its system prompt, or tell it: "To use the web, run sb guide, then drive sb."
• Claude / Cursor skills: mkdir -p ~/.claude/skills/semantic-browser && sb guide --skill > ~/.claude/skills/semantic-browser/SKILL.md
• Rules files (AGENTS.md, .cursorrules): add the same one-liner.
• The guide is tested against the code (every verb covered, size-capped) and also lives at docs/agent_guide.md.

What the agent gets: one outcome line plus a numbered page per command, batching with sb do, whole-page find, honest failure messages, bot-wall and CAPTCHA signalling, and rules that treat page text as untrusted input and default to read-only.

Why Semantic Browser

• One compact view — page content with inline [n] refs; shadow DOM, iframes (cross-origin too), custom div widgets and duplicate labels handled.
• Exact-element execution — a ref is bound to one element handle; stale refs fail loudly, never retarget (no silent <body> clicks).
• Fast — event-driven settling: ~0.3–0.5 s to a usable page; whole 3–5-step tasks in ~0.3–1 s on the local hard-pattern suite (v1.3.2: ~4 s).
• Built-in blockers — cookie banners and modals are detected with a ranked dismiss hint; bot walls and rate limits are flagged (! This looks like a bot/verification page) so an agent stops instead of hammering.
• CAPTCHA assist — sb captcha produces a numbered, annotated image (or PDF) for a vision model and clicks the tiles it names.
• Four interfaces — sb agent CLI (persistent daemon), Python API, legacy semantic-browser CLI, and HTTP service.

Install

pip install "semantic-browser[managed]"
semantic-browser install-browser

For service mode: pip install "semantic-browser[server]"

Quickstart

Agent CLI (sb, new in 1.4; do batches in 1.5)

sb goto news.ycombinator.com   # first call starts a background Chromium (headful); later calls reuse it
sb click 12                    # act on the [12] you see in the view (or: sb click "comments")
sb find "price"                # search the whole page (table rows come with their column headers)
sb do "type 3 boots --enter" "click Football" view   # several steps, ONE call (stops at the first failure)
sb captcha --pdf               # annotated image of a CAPTCHA for a vision model
sb stop

Full verb list, view syntax, sessions, --cdp attach and security model: docs/agent_cli.md.

Interactive portal

semantic-browser portal --url https://example.com --headless

Python

import asyncio
from semantic_browser import ManagedSession
from semantic_browser.models import ActionRequest

async def main() -> None:
    session = await ManagedSession.launch(headful=False)
    runtime = session.runtime

    await runtime.navigate("https://example.com")
    obs = await runtime.observe(mode="summary")
    print(obs.planner.room_text)

    first_link = next((a for a in obs.available_actions if a.op == "open"), None)
    if first_link:
        result = await runtime.act(ActionRequest(action_id=first_link.id))
        print(result.status, result.observation.page.url)

    await session.close()

asyncio.run(main())

LLM Agent Loop (Minimal)

async def agent_loop(url: str, task: str) -> None:
    session = await ManagedSession.launch(headful=False)
    runtime = session.runtime
    await runtime.navigate(url)
    obs = await runtime.observe(mode="summary")

    for step in range(25):
        action_id = call_your_llm(obs.planner.room_text, task)  # returns one action ID
        if action_id == "done":
            break
        result = await runtime.act(ActionRequest(action_id=action_id))
        obs = result.observation

    await session.close()

Full worked examples for OpenAI, Anthropic, and more: Integration Examples

Documentation

How It Works

Chromium page ──one JS walk per frame──▶ semantic nodes + reading-order flow ──▶ text view with inline [n] refs
      ▲                                                                                   │
      │                          agent reads the view, replies with one verb              ▼
      └── runtime executes on the exact element handle, settles, reports the outcome ◀── `click 12`

1. Observe — one DOM pass per frame (shadow DOM, same- and cross-origin iframes) builds nodes and a reading-order flow; occlusion and overlay detection mark what is really clickable.
2. Render — the view keeps content and controls together, collapses navigation, windows long pages and ranks overlay dismiss controls.
3. Act — a ref is bound to one element handle for the whole session, so a stale ref fails loudly instead of clicking something else. The runtime waits for the page to settle and prints one outcome line (-> now at …, page changed (+3/-1 lines), no visible change).
4. Repeat — or batch known steps with sb do. Architecture: docs/system_arch.md. The older planner/observe/act API (room text, action IDs) is still available for Python and service use: Planner Contract.

Benchmarks

Latest dogfood run (details, protocol, raw data and caveats: docs/benchmarks/2026-10-06-dogfood-v1.4.md):

Real model, live sites (me, Claude Sonnet 5.5, one command per call, journaled with scripts/dogfood/sbj.py; report): round 1 on 1.4.0 completed 11 of 13 tasks (median 4 calls; it found a wrong-price bug and several missing-control bugs); after the fixes the final round completed 7 of 7 (median 1 call, ~0.26k tokens read; part of that is the new do batching). Reddit and Hacker News blocked this IP in every mode and are reported as such.

The first table is a scripted stand-in for the model; the real-model numbers above are a handful of tasks on one machine. Neither is a universal guarantee. Protocol: docs/benchmark_protocol.md. Manifest: benchmarks/manifest.json.

CLI Reference

semantic-browser version                # Show version
semantic-browser doctor                 # Verify installation
semantic-browser install-browser        # Download Chromium
semantic-browser launch --headless      # Start a session
semantic-browser attach --cdp <ws-url>  # Attach to running Chrome
semantic-browser portal --url <url>     # Interactive exploration REPL
semantic-browser observe --session <id> --mode summary
semantic-browser act --session <id> --action <action_id>
semantic-browser inspect --session <id> --target <target_id>
semantic-browser navigate --session <id> --url <url>
semantic-browser back --session <id>
semantic-browser forward --session <id>
semantic-browser reload --session <id>
semantic-browser diagnostics --session <id>
semantic-browser export-trace --session <id> --out trace.json
semantic-browser serve --host 127.0.0.1 --port 8765 --api-token <token>

What's New in v1.6.0

• sb guide — the agent playbook ships inside the package (sb guide, sb guide --skill for a drop-in SKILL.md). Tested for verb coverage and size.
• Bot walls are named — Fastly/Cloudflare/DuckDuckGo-style gates and rate limits get a ! This looks like a bot/verification page line with the right next step, also on long pages when the controls are covered. Found live: PyPI "Client Challenge", DuckDuckGo "bots use DuckDuckGo too".
• Search boxes in headers stay search boxes — collapsed header/nav lines used to print an <input> like a link ([4]Search with DuckDuckGo); fillable controls now keep their full form and are never cut by the "(+N more)" cap.
• Better find — whole-word hits first (MIT no longer drowns in "commit"), and a short hit (a bare price) carries its card title.
• Unknown-verb errors list the verbs from the code and point at sb guide.

What's New in v1.5.0

Everything here came from driving sb as a real model (Claude Sonnet 5.5) on live sites, then fixing what hurt (report):

• sb do "step" "step" … — a whole GOV.UK visa wizard (8 steps) is one call; median task went from 4 calls to 1.
• Correct text on hard markup — Amazon prices no longer lose their decimal (£569 → £5.69); transparent radios/checkboxes (GOV.UK) have refs; delegated-handler widgets (jQuery UI datepicker Prev/Next) are controls.
• Honest overlays — the dismiss hint ranks close/reject/short-accept, never "Continue", sign-in or pay-to-reject; view --all shows the page behind.
• Better find — centred snippets, real <th> column headers, works behind an overlay. Labels you type (click Save settings) resolve, and ambiguous ones show where each candidate goes.
• CAPTCHA — the image is captured once the page is still (dynamic reCAPTCHA grids replace tiles after Verify).
• Stale background sessions restart themselves after an upgrade. Release tooling: scripts/publish.sh.

Full list: CHANGELOG.md. Note: 1.4.0 was never published to PyPI; 1.5.0 includes it.

Earlier releases

1.4.0 introduced the sb agent CLI, the one-pass view engine, fast settling, CAPTCHA assist and safe --cdp attach (never published on its own; 1.5.0 was the first release to include it). Full history: CHANGELOG.md.

Contributing

See CONTRIBUTING.md for development setup and PR expectations.

License

MIT — see LICENSE.