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
- “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.
How Self-Learning Works
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.
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 是面向 AI agent 的自学习浏览器基础设施:它让 agent 控制真实浏览器去完成任务,然后把学到的网站知识存为本地记忆,让后续运行更快、更省钱,也不必重新摸索同一个网站。