Browser Agent Protocol (BAP)

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.

<p align="center"> <img src="./assets/demos/blog-reader.gif" alt="BAP navigating a website, clicking through pages, and scrolling through a blog post" width="960" /> </p>

<p align="center"> <a href="https://www.npmjs.com/package/@browseragentprotocol/cli"><strong>CLI</strong></a>  ·  <a href="https://www.npmjs.com/package/@browseragentprotocol/mcp"><strong>MCP</strong></a>  ·  <a href="https://pypi.org/project/browser-agent-protocol/"><strong>Python</strong></a>  ·  <a href="./packages/cli/README.md"><strong>Docs</strong></a>  ·  <a href="./LICENSE"><strong>Apache-2.0</strong></a> </p>

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Why BAP

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.

What BAP gives your agent

	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

Get Started

npm i -g @browseragentprotocol/cli
bap install-skill

Or 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.

Quick Example

# 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"

How it works

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", @ref instead of brittle CSS
• Fused operations — goto --observe saves a roundtrip, act --observe chains 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

Interfaces

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

Benchmark

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.

When to use what

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

See It in Action

<p align="center"> <img src="./assets/demos/workflow.gif" alt="BAP navigating a bookstore, clicking Travel category, and viewing book details" width="960" /> <br/> <em>Navigate → click → browse: 3 commands, one workflow</em> </p>

<p align="center"> <img src="./assets/demos/skill-scorer.gif" alt="BAP browsing GitHub to find a SKILL.md, then scoring it on skills.menu" width="960" /> <br/> <em>Multi-site workflow: browse GitHub → open skills.menu → paste & score</em> </p>

Tips

• BAP defaults to headful Chrome with a persistent session.
• Use --headless for CI or background runs.
• Use --no-profile if your Chrome profile is busy.
• Use --slim mode to cut tool definitions to ~600 tokens (vs ~4,200 for Playwright MCP).
• Use bap close-all to stop the daemon and all sessions.

Docs

• CLI
• MCP
• TypeScript SDK
• Python SDK
• Browser tools decision guide

Contributing

• Release automation
• Contributing guide
• Code of conduct
• Security policy

License

Apache-2.0. See LICENSE for details.