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Webcmd — stop paying agents to rediscover the web

NPM version Documentation License Join the community on Discord Follow AgentR on X

Webcmd

Self-learning browser infra for AI agents.

Webcmd learns the navigational context of websites as agents use them, then turns that knowledge into local memory for faster, cheaper, more reliable browser automation. The goal is simple: stop making agents rediscover the same sites on every run and cut browser-agent token spend by up to 90%.

Webcmd pairs live browser control with a self-learning memory layer:

Layer Scenario What Webcmd Helps With
0. Live browser control The site is unfamiliar. Use webcmd browser to inspect, click, type, extract, capture network calls, and complete the task in a real browser.
1. Sitemap memory The site is familiar, but the action space is not fully known. Capture an agent-facing sitemap of observed pages, states, actions, workflows, APIs, pitfalls, and fallback paths.

How Self-Learning Works

How Webcmd learns: load memory, use the live web, keep useful learnings, and help the next agent

Learning stays quiet and selective: the live browser is always truth, Webcmd never explores just to learn, and a memory failure never blocks the task. First access may use a Webcmd Cloud seed; subsequent learning stays local.

For local, multi-step browser exploration, agents can send one sandboxed Playwright-style program to an explicit browser session:

webcmd --profile work session create "Work Project" -f json
# id: work-project-k7
webcmd --profile work --session work-project-k7 browser tabs
webcmd --profile work --session work-project-k7 browser run --file explore.js
printf 'return await page.title();' \
  | webcmd --profile work --session work-project-k7 browser run --stdin
webcmd --profile work session close work-project-k7

Profiles are cookie jars; Sessions are independent browser windows within a profile, so Session IDs are immutable, Profile-scoped, and safe to reuse for that Session's lifetime. Parallel agents should create separate Sessions. Raw browser commands require an explicit readable Session ID.

Demo

webcmd.mp4

Quick Start

Agent prompt

Fetch and follow https://raw.githubusercontent.com/agentrhq/webcmd/main/start.md to set up Webcmd end to end.

Manual

Webcmd requires Node.js 20.6+.

npm install -g @agentrhq/webcmd
webcmd skills add

When prompted, choose Claude, Codex, another supported harness, or a custom skills path. That installs exactly one skill, webcmd-browser.

Load or tag webcmd-browser only for live browser work, then describe the outcome you want. Installation and setup commands do not require that skill.

Use webcmd to research the latest discussions about browser automation across Hacker News and Reddit, then return a concise comparison with source links.

What You Can Ask

  • “Use webcmd to research agentic browser automation on PubMed and return the title, authors, publication date, abstract, and URL for each result.”
  • “Use webcmd to find active AI infrastructure companies in the YC company directory and return the company, batch, description, location, profile URL, and source links. Keep it read-only.”
  • “Use webcmd to look up parts on Grainger by part number and return price, stock, minimum order quantity, lead time, and product URL.”
  • “Use webcmd with my logged-in work profile to summarize unread LinkedIn messages from the last seven days and return the sender, subject or opening text, received time, and conversation URL.”
  • “Use webcmd to check Grainger part prices and SAP Ariba purchase-order status, then return a combined summary.”

See It in Action

Use webcmd with my logged-in `social` profile to collect my recent X bookmarks and return the author, text, and URL.

The agent uses the logged-in profile to complete the task in a real browser. Along the way, Webcmd quietly retains useful navigation context so later agents can avoid repeating the same exploration.

Where Webcmd Works

Webcmd can work through authenticated browser sessions across research, social, AI, shopping, and booking products.

Group Supported surfaces Representative outcomes
research and communities Hacker News, Reddit, PubMed Compare current discussions, find primary research, and return concise summaries with source links.
social and professional X/Twitter, LinkedIn, TikTok Collect bookmarks, monitor public posts, or research people and creators with a named profile when needed.
AI tools ChatGPT, Claude, Gemini, NotebookLM Retrieve conversations, research outputs, notebooks, and generated materials from the tools you already use.
shopping and bookings Amazon, Blinkit, Zepto, BigBasket, District, Practo Compare products, availability, prices, appointments, events, and delivery options.

This list is illustrative. Webcmd can operate other websites through the same live browser workflow.

Benchmarks

On BU Bench V1, a 100-task browser automation benchmark, Webcmd recorded the highest accuracy and lowest estimated controller cost per completed task, and fewest agent turns per completed task in this comparison.

BU Bench V1 comparison: webcmd leads accuracy at 67%, cost per completed task at $0.255, and agent turns per completed task at 9.8

All tools used the same Pi controller, controller model, Codex gpt-5.4 judge, and CloakBrowser engine. This is a stronger judge than the original BU Bench setup, whose current runner uses Gemini 2.5 Flash. Accuracy is passed tasks out of 100. Cost and agent turns are averaged over completed tasks; cost excludes judge usage. See the benchmark report for category results, methodology, architectural analysis, and reproduction steps.

Learn More

Webcmd Cloud can run supported commands and browser sessions on hosted infrastructure. It is in active development and is not yet stable.

Contributing

See CONTRIBUTING.md.

License

Released under the terms in LICENSE.

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