I built a browser for AI agents, then benchmarked it against Playwright MCP and browser-use

Every agent I tried on the web had the same problem: it was reading too much. A browser tool hands the model a page snapshot, the model clicks, and then gets a fresh snapshot of the whole page again. Most of the tokens go on re-reading things that didn't change.

So I built AgentGo, a Chromium browser for Windows (Rust, on CEF) made to be driven by an agent over MCP. Then I measured it against the two tools most people use today.

The result

Same model (gpt-6-luna, medium reasoning), same 24 BrowserGym MiniWoB++ tasks, a fresh agent session for every task, only that browser's MCP tools, and the page's own reward decides.

About half the tokens, roughly half the turns, and the fastest median per task.

Why it uses fewer tokens

Three design choices do most of the work:

1. A compact outline instead of a snapshot. browser_open returns headings plus the things you can act on, one per line, as [ref] role "name". The refs stay stable while the page changes.
2. Only what changed. After browser_act, the reply is the diff: + for new elements, - for removed ones, ~ for changed values. The model doesn't re-read the page to find out whether its click worked.
3. The browser waits, not the model. Each action waits for the page to settle before answering, so the agent doesn't spend turns calling "wait" and reading again.

It's also a real browser rather than a test harness: its own pointer and keyboard (so date pickers, sliders and key-listening widgets behave), the agent can have its own signed-in accounts, and payments or final submissions pause until the person approves them in the browser window.

What it got wrong

Two tasks beat every setup, AgentGo included:

• form-sequence-2: the instruction never says to press Submit, and no agent did.
• click-pie: every agent opened the pie menu and clicked the item, and the page didn't count the click.

And the limits of the test itself: one machine, one run per task and setup, one model. MiniWoB++ pages are small; a different model or another run will move single tasks. The full method, every task's result, and a separate check on 20 real websites are in the benchmark write-up.

GoMini

AgentGo also ships GoMini, a small model (322M parameters) that runs on your own GPU and takes routine steps, like filling the values you supplied, so the big model doesn't have to read every page. On 13 held-out task types it went from 15 to 39 of 65 subgoals between versions. It's still in development: with it on, AgentGo solved the same 22/24, at slightly more tokens.

Try it

• AgentGo (Windows 11, free): https://github.com/Moha2005269/agentgo
• Claude Code: /plugin install agentgo --marketplace Moha2005269/agentgo-mcp
• Other MCP clients: the installer connects Claude Code, Codex or Droid, or run agentgo mcp. It's also on the official MCP Registry as io.github.Moha2005269/agentgo.

I built this solo. I'd love to hear what breaks, and which sites you want me to try next.