AGENTIC BROWSER · MCP · CLI
Headed & headless browser automation, exposed via MCP or CLI. Connect any LLM and unlock web tasks — data extraction, workflow automation, research — with lower latency and fewer tokens than any tool on the market.
ALOHAJET VS THE FIELD
| Metric | Chrome DevTools | Playwright | AlohaJet |
|---|---|---|---|
| Tasks passed | 41 / 80 | 9 / 80 | 70 / 80 |
| Avg time per task | 84 s | 73 s | 39 s |
| Avg tokens per task | 340k | 114k | 158k |
| Best-case speedup | baseline | baseline | up to 10× |
WebArena benchmark, pooled GPT-5-mini + Qwen3-30B runs, 10 reps per task.
SEE IT RUN
Watch an agent extract structured data from a live site in seconds — no selectors, no scripts.
WHAT TEAMS BUILD WITH IT
Pull structured data from any site — reviews, prices, profiles, listings — without writing or maintaining selectors. The agent reads pages the way a model does.
Automate multi-step web workflows: admin panels, dashboards, form-filling, order lookups. Headless mode runs them quietly in your pipeline.
Let agents browse, compare, and summarize across sites. Lower token usage means longer sessions and more pages per dollar.
Drop AlohaJet into existing agent stacks as the web-action layer — via MCP for tool-calling models or CLI for scripted runs.
PLUGS INTO YOUR STACK
Add AlohaJet as an MCP server to any tool-calling LLM — or drive it from the CLI in scripts and CI.
Describe the web task in plain language. No selectors, no page objects, no brittle automation scripts.
The agent browses in headed mode for visibility or headless for speed — and returns structured results.
# via CLI
$ alohajet run "find the customer with phone +1 205 881 2302" \
--site shopping-admin --headless
# via MCP — add to your client config
{
"mcpServers": {
"alohajet": { "command": "alohajet", "args": ["mcp"] }
}
}
✓ done in 21s · 71.9k tokensYOUR DATA STAYS YOURS
AlohaJet runs entirely on your machine. Sessions, cookies, and credentials never leave it — pair it with a local LLM and nothing touches the network you don't control.
Deploy inside your own perimeter — VPC, on-prem, or air-gapped. You choose the model endpoint, the network policy, and where every byte of browsing data lives.
No mandatory SaaS relay, no telemetry of page content. The browser talks to your LLM and the sites you point it at — nothing else in between.
QUESTIONS
Any model that supports tool calling can drive AlohaJet over MCP. The benchmark numbers above were measured with GPT-5-mini and Qwen3-30B-Instruct — it isn't tuned to one vendor.
Those expose low-level browser controls and leave the heavy lifting to the model. AlohaJet is built for agents: it presents pages in a model-friendly form, which is why it completes more tasks, faster, with fewer tokens on the same benchmark.
Both ship in the same binary. Use headed mode when you want to watch or debug a run; use headless in pipelines, CI, and high-volume automation.
From the WebArena frozen 33-task set: pooled GPT-5-mini and Qwen3-30B-Instruct runs, 10 repetitions per task, on tasks where AlohaJet strictly beat both baselines. All winning cells were recounted from raw per-run logs with zero mismatches.
Yes — request a demo below and we'll run it on a task from your stack so you can compare latency and token usage directly.
LET'S TALK
Request a demo and we'll benchmark AlohaJet on your own web tasks.
Request a demoAndrew · team@alohabrowser.com · alohabrowser.com