Browser Use
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
Descripción del creador (original en inglés)
Self-hosted MCP server: sandboxed browser, desktop, vision, code-edit packs for any agent.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
HELMDECK_URL, HELMDECK_TOKENLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"helmdeck": {
"command": "npx",
"args": [
"-y",
"@helmdeck/mcp-bridge"
],
"env": {
"HELMDECK_URL": "your-value",
"HELMDECK_TOKEN": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add helmdeck --env HELMDECK_URL=your-value --env HELMDECK_TOKEN=your-value -- npx -y @helmdeck/mcp-bridgeCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"helmdeck": {
"command": "npx",
"args": [
"-y",
"@helmdeck/mcp-bridge"
],
"env": {
"HELMDECK_URL": "your-value",
"HELMDECK_TOKEN": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"helmdeck": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@helmdeck/mcp-bridge"
],
"env": {
"HELMDECK_URL": "your-value",
"HELMDECK_TOKEN": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub tosin2013, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: HELMDECK_URL, HELMDECK_TOKEN
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.29.10) llegó al registro hace 5 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js, Docker en tu computadora.
En la misma categoría: Browser Use, Chrome DevTools MCP, MCP Server Browser
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
MCP server for Chrome DevTools
MCP server for browser use access
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
Today's helmdeck install ran a full 6-step code-edit loop (clone, read, patch,
test, commit, push) on
gpt-oss-120bfor $0.07. The same loop on Cursor
or Claude Code direct via Sonnet would have cost $0.30+. Same outcome,
~5× cheaper — and the "expensive" stack isn't even the most expensive option.
| Workflow | Frontier-model approach | Helmdeck (gpt-oss-120b) |
|---|---|---|
| Browser scrape + GitHub comment | $0.25 (Anthropic Computer Use) | $0.005 |
| Code edit loop (6 steps) | $0.35 (Cursor / Aider) | $0.07 |
| Multi-step browser test | $0.20 (Browser-use NL) | $0.03 |
| PDF → structured Markdown | $1.00 (naive Sonnet vision) | $0.003 |
Most browser agents require GPT-4o or Claude Sonnet to work reliably.
Helmdeck is built for the other 99% of deployments — **local 7B models,
air-gapped environments, and teams that can't send credentials to a
cloud API.** It wraps every browser, desktop, git, and code action
into a single typed JSON call that even a small model can fill in correctly.
The numbers above are the consequence: when packs absorb the work the
LLM would otherwise burn tokens rediscovering, cheap or local models do
agentic work that frontier-model APIs charge 10× more for.
A self-hosted, containerized platform for AI agents, exposed as Capability Packs — schema-validated, one-shot JSON tools — and native MCP. The defining metric is ≥90% pack success on 7B–30B-class open-weight models, something no frontier-targeting competitor is optimizing for.
📊 Full per-task comparison with reproduction recipe at https://helmdeck.dev/explanation/why-helmdeck. These are one maintainer's findings; we welcome community reproductions.
Smart models thrive on bash and a README. Weak models stall on open-ended interfaces. Helmdeck closes that gap by hiding browser sessions, desktop actions, credentials, and multi-step workflows behind single typed REST / MCP calls.
Three audiences specifically:
v0.22.0 shipped — *agents that work on free models, with memory.* The release closes ADRs 047–050:
helmdeck.route meta-pack recommends the best pack/pipeline for an intent (with structured gap warnings when nothing fits), backed by per-caller learned defaults surfaced through the helmdeck://routing-guide and helmdeck://my-defaults MCP resources and a Routing Memory management UI.helmdeck.memory_store persists durable user facts (read back via helmdeck://my-memory), an optional embedding sidecar powers OpenClaw's memory_search, and a QMD corpus bridge exposes helmdeck memory to OpenClaw.helmdeck.plan turns a multi-action prompt into an ordered, pipeline-aware step plan plus a rewritten_prompt.internal/llmcontext compacts catalog-heavy prompts to fit small-model context budgets (tiered per-model budgets, cascading select + lexical rank, optional two-pass filter), surfaced through helmdeck://context-budgets and helmdeck://my-plans.57 capability packs ship in the control-plane binary (47 without an AI gateway configured), alongside 21 built-in pipelines, a community pack marketplace (helmdeck pack install ), and operator-supplied cmd.* subprocess packs. Earlier headline features remain: end-to-end content chaining (image.generate auto-feeds podcast/slides/blog covers), the helmdeck://image-models MCP resource, image-mode install (./scripts/install.sh --image-mode), and the Pack Test Runner UI. Helmdeck is published to the official MCP Registry as io.github.tosin2013/helmdeck for one-line install in registry-aware clients. Phases 1–6.5 are complete; the current milestone is v1.0 — Kubernetes & GA (Phase 7), with backlog materialised as GitHub issues tagged good first issue and help wanted.
docs/adrs/ — every architectural decision with PRD back-referencesdocs/TASKS.md — ~85 tasks across 8 phases with critical pathdocs/MILESTONES.md — drop-in issue checklists with current ship statedocs/PACKS.md — every shipped pack's input/output contractgit clone https://github.com/tosin2013/helmdeck
cd helmdeck ./scripts/install.sh
That's it. The script runs preflight checks (docker, node ≥20, go ≥1.26, make, openssl, curl) with platform-aware install hints, generates fresh secrets into deploy/compose/.env.local (chmod 600), builds the Management UI bundle, the Go binaries, and the browser sidecar image, brings the Compose stack up, and prints the URL plus a freshly generated admin password.
✓ helmdeck is up
URL: http://localhost:3000 Username: admin Password:
Useful flags:
./scripts/install.sh --reset — tear down, regenerate secrets, reinstall (new admin password)./scripts/install.sh --no-build — skip build steps, just bring the stack up./scripts/install.sh --help — full flag referenceOr via make: make install.
A running stack is just the platform — the value is packs called by an agent. Wire one of the supported MCP clients to your fresh install:
| Client | Status | Setup guide |
|---|---|---|
| OpenClaw | ✅ validated end-to-end | docs/integrations/openclaw.md |
| Claude Code | 🟡 documented | docs/integrations/claude-code.md |
| Claude Desktop | 🟡 documented | docs/integrations/claude-desktop.md |
| Gemini CLI | 🟡 documented | docs/integrations/gemini-cli.md |
| Hermes Agent | 🟡 documented | docs/integrations/hermes-agent.md |
Once a client is connected, work through the pack-demo-playbook.md — 20+ copy-pasteable prompts that exercise every pack. The per-pack reference covers each pack's contract, error codes, and chained workflows.
If you'd rather drive each step yourself instead of running the install script:
# 1. Build the Management UI bundle (needs Node 20+)
make web-deps && make web-build
make build
HELMDECK_JWT_SECRET=$(openssl rand -hex 32) \ HELMDECK_VAULT_KEY=$(openssl rand -hex 32) \ HELMDECK_ADMIN_PASSWORD=changeme \ ./bin/control-plane
Or use the Compose stack directly (control plane + Garage object store + bundled init):
cp deploy/compose/.env.example deploy/compose/.env.local
docker compose -f deploy/compose/compose.yaml --env-file deploy/compose/.env.local up -d
The login endpoint accepts a static admin password set via the HELMDECK_ADMIN_PASSWORD env var on the control plane process. Suitable for the dev / single-node Compose tier; OIDC SSO for production deployments lands in a later phase.
| Setting | Default | Override |
|---|---|---|
| Username | admin | HELMDECK_ADMIN_USERNAME env var |
| Password | *(none — UI login disabled)* | HELMDECK_ADMIN_PASSWORD env var (required) |
| Session length | 12 hours | Hardcoded in internal/api/auth_login.go |
To change the password: stop the control plane, set HELMDECK_ADMIN_PASSWORD to the new value, and restart. There is no in-UI "change password" flow today — the password is managed out-of-band by whichever orchestrator runs the control plane (Compose, systemd, Kubernetes Secret, etc.).
If `HELMDECK_ADMIN_PASSWORD` is unset, the login endpoint returns 503 login_disabled. The control plane still runs and the API still works — operators can mint a JWT directly via the CLI:
./bin/control-plane -mint-token=alice -mint-token-scopes=admin -mint-token-ttl=12h
The minted token can be pasted into any tool that speaks Authorization: Bearer .
Production note: the static-password path uses constant-time comparison so it's safe against timing attacks, but it's still a shared secret that has to be rotated by hand. For production deployments with multiple operators, OIDC SSO via your existing identity provider is the right answer — see the Phase 6 follow-up roadmap.
helmdeck-mcp bridge binary (ADRs 025, 030)57 packs ship in the box (47 without an AI gateway configured). Each one hides a multi-step workflow behind a single typed JSON-Schema call so weak open-weight models can drive it as reliably as frontier models. The full input/output contract for every pack lives in docs/PACKS.md. The highlights:
| Pack | What it hides |
|---|---|
| Browser & web | |
browser.screenshot_url | Session lifecycle, navigation, render wait, cleanup |
browser.interact | Deterministic multi-step CDP (navigate, click, type, scroll, screenshot, assert_text) — no LLM needed |
web.scrape / web.scrape_spa | Firecrawl-backed markdown scrape OR schema-driven SPA extraction |
web.test | Natural-language browser tests via Playwright MCP + LLM loop |
research.deep | Multi-source Firecrawl search + per-source scrape + LLM synthesis with inline citations |
content.ground | Parses a markdown file for claims, finds authoritative sources, inserts real [link](url) citations in place |
| Document & vision | |
slides.render | Marp + Chromium + format flags |
slides.narrate | Narrated MP4 video (ElevenLabs TTS per slide) + YouTube engagement metadata + sidecar SRT captions + structured validation field (default-on post-step) |
| av.validate | Structured AV-artifact validation (faststart, codec pin, packet contiguity, RMS swee
Solo se muestra el principio; lee el resto en el repo