webcmd README

The project's README from github.com/agentrhq/webcmd. As one markdown file: README.md.

Webcmd — stop paying agents to rediscover the web

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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.

Demo

https://github.com/user-attachments/assets/bdb65307-9e2a-4d58-9175-45d59528ae37

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

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.

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.

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.

webcmd (agentrhq/webcmd)

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2.7K ·GitHub
Webcmd
What is this?Webcmd is self-learning browser infrastructure for AI agents: it lets an agent control a real browser to complete tasks, then saves what it learns about a site as local memory so future runs are faster, cheaper, and don't re-explore the same site.
Is this a browser library I import, or a CLI skill for my coding agent?It is a skill you load into a coding-agent harness, not a library you import into code. The installer sets up exactly one skill called webcmd-browser that you load only when you need live browser control.
How do I install and start using Webcmd?Run the quick-start install command, then pick your harness when prompted (Claude, Codex, another supported harness, or a custom skills path) to install the webcmd-browser skill.
What do I need installed before setting it up?You need Node.js 20.6 or newer, plus a supported coding-agent harness (Claude, Codex, Gemini, or your own) to actually load the installed skill.
Any catches before relying on this?The hosted Webcmd Cloud option, used for running sessions on remote infrastructure and for seeding first-time learning, is explicitly still in active development and not yet stable; local-only usage is the steadier path today.
Does my browsing/navigation data stay on my machine?Mostly yes: a site's first visit may pull a seed from Webcmd Cloud, but every learning step after that is stored and reused locally.
How does Webcmd actually perform versus other browser-automation tools?On BU Bench V1, a 100-task browser automation benchmark, Webcmd recorded the highest accuracy, lowest estimated cost per completed task, and fewest agent turns among tools compared under the same controller and judge setup.
What kinds of websites can it already handle?It already works across research sites (Hacker News, Reddit, PubMed), social platforms (X/Twitter, LinkedIn, TikTok), AI tools (ChatGPT, Claude, Gemini, NotebookLM), and shopping/booking sites (Amazon, Blinkit, Practo), though the README notes this list is illustrative rather than exhaustive.
What's the difference between a Session and a Profile in Webcmd?A Profile is a cookie jar, while a Session is an independent browser window inside that Profile; Session IDs are immutable and Profile-scoped, so parallel agents should each use their own Session.
Why not just give an agent raw Playwright access?Raw browser control makes agents rediscover the same site layout on every run. Webcmd adds a sitemap-memory layer on top of live browser control so repeated exploration is skipped, cutting browser-agent token spend by up to 90%.
What are similar projects?Raven, scout, brigade, codify, nanobrowser, Agentlas-OS, testsprite-cli, holaOS
Which topics is it in?browser-use, ai-agent-tools, llm-agents, playwright, node, agent-memory, web-scraping, memory, sandbox, browser-automation, chatgpt, cli-tool
Creator
agentrhq
Size
8.6 MB
Stars
2.7K
Updated
CommitSep 25Releasewebcmd-v0.8.4Sep 09
Age
3mo
Commits · 90 Days