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docs: rewrite README with benchmark data and clear positioning
Position BAP as the execution layer for browser agents: - Lead with cost-per-action comparison (BAP vs Stagehand vs Browser Use) - Show what the LLM sees (structured refs vs raw HTML) - Include real benchmark numbers from browserbench - Add "when to use what" decision table - Highlight uSEID (self-healing) and DBAR (deterministic replay) - Recommend BAP MCP in-process as the fastest interface Key message: "You bring the LLM, BAP brings the browser." Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎README.md‎

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# Browser Agent Protocol (BAP)
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Give your AI agent a real browser — fast, semantic, and reliable.
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The execution layer for browser agents. 10-25ms per action, zero token overhead, structured observations your LLM can actually use.
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BAP keeps a browser session warm, observes pages in an AI-friendly way,
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and lets agents act with semantic selectors and fused operations instead
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of brittle CSS and endless roundtrips.
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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.
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<p align="center">
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<img src="./assets/demos/blog-reader.gif" alt="BAP navigating a website, clicking through pages, and scrolling through a blog post" width="960" />
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---
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## Why BAP
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Every browser action in your agent pipeline has a cost — latency, tokens, and dollars.
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```
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per action per 20-action task 1000 tasks/day
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Stagehand 800ms, $0.01 $0.20 $200
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Browser Use 1500ms, $0.02 $0.40 $400
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BAP (in-process) 20ms, $0.00 $0.00 $0
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```
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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).
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**BAP is for teams that want to control the intelligence.** You bring the LLM, BAP brings the browser.
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## What BAP gives your agent
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| | Without BAP | With BAP |
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|---|---|---|
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| **What the LLM sees** | Raw HTML (10,000+ tokens) | `@submit button: "Submit"`, `@email textbox: "Email"` (50 tokens) |
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| **Latency per action** | 800-1500ms (LLM in the loop) | 10-25ms (direct execution) |
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| **Session state** | Lost between turns | Persisted — browser stays warm |
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| **When selectors break** | Agent fails | Self-healing via uSEID fallback |
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| **Reproducibility** | Non-deterministic | DBAR deterministic replay |
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## Get Started
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```bash
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Then give your agent a task:
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```text
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Use BAP to open https://piyushvyas.com, go to Writing, find the
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"Introducing Browser Agent Protocol" post, and summarize it.
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Use BAP to open https://example.com, find the pricing page, and extract the plan names and prices.
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```
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Start a fresh agent session after installing the skill so it picks up
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the BAP guidance.
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## Quick Example
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## Why BAP
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```bash
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# Navigate and observe — one fused call
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bap goto https://example.com --observe
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# Agent sees structured output:
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# @navPricing link: "Pricing"
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# @heroSignup button: "Get Started"
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# @searchInput textbox: "Search..."
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# Agent decides to click pricing — BAP executes in 15ms
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bap act click:@navPricing --observe
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# Extract structured data
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bap extract --fields="plan,price,features"
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```
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## How it works
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| | |
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| ---------------------- | ---------------------------------------------------------------------------------------- |
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| **Real browser** | Prefers installed Chrome, keeps session state warm, stays close to a normal user browser |
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| **Semantic selectors** | `role:button:"Submit"` and `label:"Email"` instead of brittle CSS |
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| **Fewer roundtrips** | `goto --observe`, `act --observe`, stable refs, and structured extraction |
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| **Warm daemon** | Browser stays alive across commands — agents keep momentum |
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| **Token efficient** | `--slim` mode exposes 5 tools (~600 tokens) vs 70+ in competitors (~4,200 tokens) |
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| **Multiple surfaces** | CLI, MCP, TypeScript SDK, Python SDK — pick what fits your stack |
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```
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Your LLM Agent ← decides what to do (planning, reasoning)
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↓
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BAP (MCP or CLI) ← executes it (10-25ms, structured observations)
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↓
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Playwright ← handles the browser (auto-wait, smart inputs)
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↓
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Chrome/Firefox/WebKit ← renders the page
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```
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BAP is a thin protocol layer over Playwright. It adds:
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- **Structured observations** — interactive elements with refs, roles, and action hints instead of raw HTML
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- **Semantic selectors** — `role:button:"Submit"`, `text:"Sign in"`, `@ref` instead of brittle CSS
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- **Fused operations** — `goto --observe` saves a roundtrip, `act --observe` chains action + observation
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- **Session persistence** — browser stays alive across agent turns, no re-launching
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- **Self-healing selectors (uSEID)** — when elements change between page loads, BAP falls back to semantic identity matching
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- **Deterministic replay (DBAR)** — record a browser session, replay it identically for testing and CI
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## Interfaces
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| Interface | Install | Best for |
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|---|---|---|
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| **MCP (in-process)** | `npx @browseragentprotocol/mcp --in-process` | Fastest — 10-25ms/action, zero WebSocket overhead |
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| **MCP (standalone)** | `npx @browseragentprotocol/mcp` | Standard MCP clients (Claude, Cursor, Codex) |
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| **CLI + SKILL.md** | `npm i -g @browseragentprotocol/cli` | Coding agents with shell access |
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| **TypeScript SDK** | `npm i @browseragentprotocol/client` | Apps and agent backends |
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| **Python SDK** | `pip install browser-agent-protocol` | Python agents and notebooks |
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## Benchmark
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Measured on real websites (Wikipedia, Hacker News) — [browserbench](https://github.com/pyyush/browserbench):
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```
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Action CDP-raw Playwright BAP MCP BAP CLI PW CLI
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─────────────────────────────────────────────────────────────────────
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navigate 68ms 593ms 26ms 1490ms 590ms
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observe 14ms 7ms 8ms 145ms 591ms
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fill 1ms 18ms 13ms 148ms 588ms
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extract 0ms 8ms 5ms 164ms 604ms
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─────────────────────────────────────────────────────────────────────
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tokens/call 0 0 35 155 0
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pass rate 100% 100% 100% 100% 100%
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```
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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.
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## When to use what
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| Use case | Recommendation |
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|---|---|
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| **Known workflows on known sites** (scraping, testing, data entry) | BAP MCP or Playwright — you know the selectors, AI per-action adds no value |
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| **Agent on unfamiliar sites** (find pricing, navigate docs) | BAP for execution + your LLM for planning. Structured observations make the LLM cheaper and more accurate |
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| **Don't want to build the planning layer** | Stagehand — bundles LLM + execution, but 40x slower and $200+/day at scale |
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| **Complex multi-step goals** | Browser Use — highest abstraction, highest cost |
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| **Scale (100+ concurrent browsers)** | Browserbase for infrastructure + BAP for the automation layer |
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## See It in Action
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<em>Multi-site workflow: browse GitHub → open skills.menu → paste &amp; score</em>
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</p>
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## Quick Example
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```bash
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# Navigate and observe the page
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bap goto https://piyushvyas.com --observe
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# Click through to a blog post
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bap act click:text:"Writing" --observe
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bap act click:text:"Introducing Browser Agent Protocol" --observe
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# Scroll and extract
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bap scroll down --pixels=5000
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bap extract --fields="title,content"
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```
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## Interfaces
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| Interface | Install | Best for |
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| ------------------ | ------------------------------------ | ------------------------------- |
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| **CLI + SKILL.md** | `npm i -g @browseragentprotocol/cli` | Coding agents with shell access |
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| **MCP** | `npx -y @browseragentprotocol/mcp` | Tool-native MCP clients |
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| **TypeScript SDK** | `npm i @browseragentprotocol/client` | Apps and agent backends |
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| **Python SDK** | `pip install browser-agent-protocol` | Notebooks and Python agents |
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## Tips
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- BAP defaults to headful Chrome with a persistent session.
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- Use `--headless` for CI or background runs.
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- Use `--no-profile` if your Chrome profile is busy.
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- Use `--slim` mode to cut tool definitions to ~600 tokens (vs ~4,200 for Playwright MCP).
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- Use `bap close-all` to stop the daemon and all sessions.
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## Against Other Tools
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- **vs Playwright CLI** — BAP is built for agent workflows, not human shell scripting.
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- **vs Playwright MCP** — When shell access is available, BAP CLI solves the same job with fewer roundtrips. In MCP mode, `--slim` cuts tool definitions to ~600 tokens vs ~4,200 for Playwright MCP's 70+ tools.
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- **vs Chrome DevTools / CDP** — CDP is the low-level transport; BAP is the agent layer on top.
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## Docs
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- [CLI](./packages/cli/README.md)
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- [MCP](./packages/mcp/README.md)
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- [TypeScript SDK](./packages/client/README.md)
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- [Python SDK](./packages/python-sdk/README.md)
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- [Browser tools decision guide](./docs/browser-tools-guide.md)
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- [BAP and WebMCP comparison](./docs/webmcp-comparison.md)
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## Contributing
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