The execution layer for browser agents. 10-25ms per action, zero token overhead, structured observations your LLM can actually use.
BAP sits between your AI agent and the browser. The agent decides what to do, BAP does it — instantly, reliably, with semantic selectors and session persistence.
CLI · MCP · Python · Docs · Apache-2.0
Every browser action in your agent pipeline has a cost — latency, tokens, and dollars.
per action per 20-action task 1000 tasks/day
Stagehand 800ms, $0.01 $0.20 $200
Browser Use 1500ms, $0.02 $0.40 $400
BAP (in-process) 20ms, $0.00 $0.00 $0
Stagehand and Browser Use send every click and fill through an LLM. BAP doesn't — your agent's LLM decides the action, BAP executes it directly via Playwright. The LLM call happens once (in your agent), not per-action (in the browser layer).
BAP is for teams that want to control the intelligence. You bring the LLM, BAP brings the browser.
| Without BAP | With BAP | |
|---|---|---|
| What the LLM sees | Raw HTML (10,000+ tokens) | @submit button: "Submit", @email textbox: "Email" (50 tokens) |
| Latency per action | 800-1500ms (LLM in the loop) | 10-25ms (direct execution) |
| Session state | Lost between turns | Persisted — browser stays warm |
| When selectors break | Agent fails | Self-healing via uSEID fallback |
| Reproducibility | Non-deterministic | DBAR deterministic replay |
npm i -g @browseragentprotocol/cli
bap install-skillOr run bap demo for a guided walkthrough.
Then give your agent a task:
Use BAP to open https://example.com, find the pricing page, and extract the plan names and prices.
# Navigate and observe — one fused call
bap goto https://example.com --observe
# Agent sees structured output:
# @navPricing link: "Pricing"
# @heroSignup button: "Get Started"
# @searchInput textbox: "Search..."
# Agent decides to click pricing — BAP executes in 15ms
bap act click:@navPricing --observe
# Extract structured data
bap extract --fields="plan,price,features"Your LLM Agent ← decides what to do (planning, reasoning)
↓
BAP (MCP or CLI) ← executes it (10-25ms, structured observations)
↓
Playwright ← handles the browser (auto-wait, smart inputs)
↓
Chrome/Firefox/WebKit ← renders the page
BAP is a thin protocol layer over Playwright. It adds:
- Structured observations — interactive elements with refs, roles, and action hints instead of raw HTML
- Semantic selectors —
role:button:"Submit",text:"Sign in",@refinstead of brittle CSS - Fused operations —
goto --observesaves a roundtrip,act --observechains action + observation - Session persistence — browser stays alive across agent turns, no re-launching
- Self-healing selectors (uSEID) — when elements change between page loads, BAP falls back to semantic identity matching
- Deterministic replay (DBAR) — record a browser session, replay it identically for testing and CI
| Interface | Install | Best for |
|---|---|---|
| MCP (in-process) | npx @browseragentprotocol/mcp --in-process |
Fastest — 10-25ms/action, zero WebSocket overhead |
| MCP (standalone) | npx @browseragentprotocol/mcp |
Standard MCP clients (Claude, Cursor, Codex) |
| CLI + SKILL.md | npm i -g @browseragentprotocol/cli |
Coding agents with shell access |
| TypeScript SDK | npm i @browseragentprotocol/client |
Apps and agent backends |
| Python SDK | pip install browser-agent-protocol |
Python agents and notebooks |
Measured on real websites (Wikipedia, Hacker News) — browserbench:
Action CDP-raw Playwright BAP MCP BAP CLI PW CLI
─────────────────────────────────────────────────────────────────────
navigate 68ms 593ms 26ms 1490ms 590ms
observe 14ms 7ms 8ms 145ms 591ms
fill 1ms 18ms 13ms 148ms 588ms
extract 0ms 8ms 5ms 164ms 604ms
─────────────────────────────────────────────────────────────────────
tokens/call 0 0 35 155 0
pass rate 100% 100% 100% 100% 100%
BAP MCP (in-process) is within 2x of raw CDP and matches Playwright's direct API — while giving your agent structured observations, semantic selectors, and session persistence.
| Use case | Recommendation |
|---|---|
| Known workflows on known sites (scraping, testing, data entry) | BAP MCP or Playwright — you know the selectors, AI per-action adds no value |
| Agent on unfamiliar sites (find pricing, navigate docs) | BAP for execution + your LLM for planning. Structured observations make the LLM cheaper and more accurate |
| Don't want to build the planning layer | Stagehand — bundles LLM + execution, but 40x slower and $200+/day at scale |
| Complex multi-step goals | Browser Use — highest abstraction, highest cost |
| Scale (100+ concurrent browsers) | Browserbase for infrastructure + BAP for the automation layer |
Navigate → click → browse: 3 commands, one workflow
Multi-site workflow: browse GitHub → open skills.menu → paste & score
- BAP defaults to headful Chrome with a persistent session.
- Use
--headlessfor CI or background runs. - Use
--no-profileif your Chrome profile is busy. - Use
--slimmode to cut tool definitions to ~600 tokens (vs ~4,200 for Playwright MCP). - Use
bap close-allto stop the daemon and all sessions.
Apache-2.0. See LICENSE for details.