Lightpanda vs NemoClaw
Two of the top sandboxes, side by side: score, setup, license, activity and what each review found.
Lightpanda
Headless browser written in Zig for AI agents and scraping
NemoClaw
NVIDIA reference stack running OpenClaw and Hermes inside OpenShell sandboxes
| What we compare | Lightpanda | NemoClaw |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 85, widely used | 62, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 76, fair |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 36.3k | 22.7k |
| License | AGPL-3.0 (copyleft) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Zig | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Lightpanda
Lightpanda is a headless browser written in Zig, built on V8 for JavaScript, libcurl for HTTP and html5ever for parsing, with no graphical rendering. It exposes a CDP server on port 9222 for Puppeteer and Playwright, plus WebDriver BiDi, an MCP server, and a built-in LLM agent mode. It can also dump pages as HTML, Markdown, PNG or PDF from the command line.
Who it is for: Engineers running browser automation or web-browsing agents at scale
Strengths
- README benchmark: 123MB peak vs 2GB for headless Chrome over 100 pages
- CDP server works with Puppeteer; WebDriver BiDi also supported
- MCP server over stdio or HTTP, with isolated or shared sessions
- Agent output saved as replayable JavaScript scripts that need no LLM at runtime
Weaknesses
- No native Windows build; WSL2 required
- Linux binaries need glibc; they fail on Alpine/musl
- Telemetry is on by default; opt out via environment variable
- Not a full browser: no graphical rendering, partial Web Platform Tests coverage
- no GPU
- Docker
- Models: Anthropic, OpenAI, Gemini, Vertex AI, Mistral
- port 9222
NemoClaw
CLI and installer that provision OpenShell sandboxes for OpenClaw (default), Hermes or LangChain Deep Agents Code, with guided onboarding, inference provider selection, baseline network policies with operator approval, managed integrations and persistent sandbox state. Express install targets DGX hosts and Windows WSL; a starter prompt lets Cursor, Claude Code or Codex drive setup. For personal agents with kernel-enforced isolation.
Who it is for: People running a personal agent with kernel-enforced isolation
Strengths
- Three supported agents: OpenClaw, Hermes, LangChain Deep Agents Code
- Network policy with operator approval flow and egress control from OpenShell
- Express preset install on DGX and WSL hosts
- Documented sandbox hardening: capability drops and process limits
Weaknesses
- Alpha project; maintainers review issues without guaranteed response times
- Depends on OpenShell as the runtime; details live in NVIDIA docs, not the README
- README is mostly links; no architecture or resource figures in the repo itself
- Supported platforms are limited to those on the prerequisites page
- no GPU
- Docker
- Needs NVIDIA OpenShell, Inference provider (local or routed)
- Models: providers configured through OpenShell routed inference