AI Agents
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View guide →// agents · agentrhq/webcmd
webcmd
webcmd is an open-source ai agents project by agentrhq (agentrhq/webcmd on GitHub). It has 2,665 GitHub stars and 1,542 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 25 September 2026.
Self-learning browser infrastructure for AI agents — sites remembered, so agents stop rediscovering the web
★ 2.7k stars⑂ 1.5k forksApache-2.0updated 2026-09
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
so here's a placeholder. The guide below is complete regardless.
// project facts
Project facts
- Repository
- agentrhq/webcmd
- GitHub stars
- 2,665
- Forks
- 1,542
- License
- Apache-2.0
- Last updated on GitHub
- 25 September 2026
- Category
- AI Agents
- Listing
- Full install guide
- Official site
- Official site
// how it works
How it works
- Clarify your goal
Name the outcome you want from the web — research with source links, data from a logged-in site, a price/availability check — in one prompt.
- Get Webcmd
Install the npm package globally (Node.js 20.6+ required).
- Install the skill
Run webcmd skills add and choose your harness (Claude, Codex, another supported harness, or a custom path).
- Run your first task
Tag the webcmd-browser skill for live browser work and describe the outcome; Webcmd works in a real browser and quietly retains navigation context for next time.
// features
Why people love it
Live browser control
Agents inspect, click, type, extract and capture network calls in a real browser to complete tasks on unfamiliar sites.
Sitemap memory
For familiar sites, Webcmd keeps an agent-facing sitemap of observed pages, states, actions, workflows, APIs, pitfalls and fallback paths.
Logged-in profiles
Profiles are cookie jars for authenticated sessions (e.g. a 'work' or 'social' profile); sessions are independent browser windows within a profile, so parallel agents can work safely.
Sandboxed browser programs
For multi-step local exploration, agents can send one Playwright-style program to an explicit browser session via webcmd browser run.
Learning stays local and quiet
After an optional first-access cloud seed, subsequent learning stays local; learning never blocks the task and the live browser is always the truth.
Broad site coverage
Used across research/community sites, social and professional networks, AI tools, shopping and booking products — the README stresses the same workflow works on other sites too.
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// run it
Run it yourself
- Clone the repo
Clone webcmd if you want the source; the normal install is the npm package in step 2.
git clone https://github.com/agentrhq/webcmd.git cd webcmd - Install Webcmd
Install globally and add the browser skill to your harness, as the quick start shows.
npm install -g @agentrhq/webcmd webcmd skills add - Run a browser session
Create a named session in a profile, inspect its tabs, and close it when done (raw browser commands need an explicit session ID).
webcmd --profile work session create "Work Project" -f json webcmd --profile work session close work-project-k7
// use cases
What you can build
- Research across Hacker News, Reddit or PubMed with source links returned
- Collecting your own bookmarks or messages from logged-in sites
- Checking part prices and stock across supplier sites
- Combining data from two authenticated tools into one summary
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// faq
Questions, answered
Who maintains Webcmd?
agentrhq (AgentR) maintains it; docs are at webcmd.dev.
Is Webcmd free?
The package is Apache-2.0 licensed open source. Webcmd Cloud (hosted sessions) is described as in active development and not yet stable.
Does it need a paid API key?
Webcmd is browser infrastructure your agent drives; model costs belong to the harness (Claude, Codex, etc.) you connect it to.
How does it compare to a plain Playwright script or browser MCP?
Those start from zero on every run. Webcmd adds a persistent, self-learning memory of how sites work, so repeat tasks skip rediscovery.
How do I get started?
npm install -g @agentrhq/webcmd, run webcmd skills add, then ask your agent for a web outcome using the webcmd-browser skill.
// keep exploring
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Get webcmd
Free and open — grab the source, star the repo, and start creating.