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 ```text 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+. ```bash 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. ```text 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 ```text 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: ```bash 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](https://github.com/browser-use/benchmark#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](./benchmarks/charts/bu-bench-readme.svg) 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](https://github.com/browser-use/benchmark/blob/main/run_eval.py#L37-L38). Accuracy is passed tasks out of 100. Cost and agent turns are averaged over completed tasks; cost excludes judge usage. See the [benchmark report](./benchmarks/README.md) 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. - [Prompt Cookbook](https://webcmd.dev/docs/agent-prompts) - [How Webcmd Works](https://webcmd.dev/docs/concepts) - [Local or Cloud](https://webcmd.dev/docs/local-or-cloud) - [Command Surface](https://webcmd.dev/docs/cli-reference) ## Contributing See [CONTRIBUTING.md](./CONTRIBUTING.md). ## License Released under the terms in [`LICENSE`](./LICENSE).