Index / OpenClaw · updated Aug 17, 2026
OpenClaw
openclaw/openclaw · healthy · rank 1 of 43 by stars
The original lobster-themed personal AI assistant that runs on your own devices across any OS and channel. Recent commits show relentless focus on trimming dead code, speeding up gateway startup, and polishing multi-channel UX.
Facts
Repository
Runtime
Posture
Security breakdown
Composite 75 / 100 · how these are scored
higher is safer
higher is safer
higher is safer
higher is safer
higher is riskier
Model access
Read from the repository, not written by a model · 280 files examined
10 providers · runs locally · gateway support
Pinned models
gpt-5.6-sol released 2026-07-09 — 39 days old (from the public model catalogue)
Pin last edited 37 days ago
Evidence
Baseline project
OpenClaw is the reference point for every alternative on this site.
Use OpenClaw as the baseline when you want maximum ecosystem breadth, then compare it against narrower tools that optimize for security, local-first operation, team workflows, or ultra-lightweight deployment.
Star activity
386,470 stars today
Overview
OpenClaw is a personal AI assistant framework designed to run locally on user-owned devices across macOS, Linux, Windows, iOS, and Android. Its architecture separates a lightweight Gateway control plane from the assistant product itself, enabling seamless integration with over 20 messaging channels including WhatsApp, Slack, Discord, and Matrix, while supporting voice, live Canvas rendering, and skill extensions via the ClawHub registry.
Recent development activity shows a strong emphasis on performance and code hygiene: commits streamline cold startup and first agent turn latency, make gateway hot reload transactional, and aggressively trim dead exports across UI, Slack, browser, and plugin modules. This indicates a mature, actively maintained project focused on reliability and developer experience.
Unlike many minimalist clones, OpenClaw prioritizes breadth of channel support, native mobile presence, and a rich plugin ecosystem, making it the reference implementation for local-first personal AI assistants, though its Node/TypeScript baseline implies higher resource usage than pico-scale alternatives.