OpenClaw vs career-ops
Two of the top projects in Assistants, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
OpenClaw
Personal AI assistant reachable through Discord, Slack, Telegram, WhatsApp and more
career-ops
Job-search agent that scores postings against your CV and drafts applications
Verdict
OpenClaw ranks higher, 85 to 77. Of the 5 score parts we measure for both, OpenClaw leads on 2, career-ops on 2 and 1 is even.
- AdoptionOpenClaw leads by 9 points, 100 to 91.
- FreshnessEven at 100.
- Maintenancecareer-ops leads by 3 points, 94 to 91: close to even.
- Easy to runOpenClaw leads by 34 points, 67 to 33: a wide gap.
- Agent-readycareer-ops leads by 40 points, 85 to 45: a wide gap.
Pick OpenClaw if you are
Self-hosters who want a personal or team AI assistant inside their chat apps
- Connects to 20+ messaging channels plus native apps on five desktop and mobile platforms
- Model providers and agent harnesses are plugins, with hosted and local models supported
Pick career-ops if you are
Job seekers comfortable running an AI CLI in a terminal
- Drafts only: no auto-submit and no mail transport in the code, per the README
- No telemetry or backend; CV goes only to your chosen AI provider
| What we compare | OpenClaw | career-ops |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 100, widely used | 91, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | 94, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 45, minimal | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 392k | 74.6k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | TypeScript | JavaScript |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
OpenClaw
OpenClaw runs a local Gateway that connects an AI assistant to 20+ messaging channels, including iMessage, Teams and Signal, with native apps for macOS, iOS, Android, Windows and Linux. State, memory and credentials stay on your hardware, and models and agent harnesses such as Claude, Codex or local models are swappable plugins. The same Gateway can serve one user or a shared team deployment.
Who it is for: Self-hosters who want a personal or team AI assistant inside their chat apps
Strengths
- Connects to 20+ messaging channels plus native apps on five desktop and mobile platforms
- Model providers and agent harnesses are plugins, with hosted and local models supported
- No paid tier or hosted service; only a daily version check phones home, and it can be disabled
- Skills and plugins extend it through a plugin SDK and the ClawHub registry
Weaknesses
- Tools run on the host for the main session unless sandboxing is configured
- Prompts still go to whichever model provider and chat platforms you configure
- Requires Node 24.16+ or 26.1+; plain npm install from the source repo is unsupported
- Exposing the Gateway or adding users needs careful reading of the security guides
- GPU optional
- Docker + Compose
- Needs Node.js 24.16+ or 26.1+, model provider account or local model
- Models: Claude, Codex, local models
career-ops
Runs inside an AI coding CLI (Claude Code, Codex, OpenCode, Qwen and others). You paste a job URL or text and it checks the posting is still live, scores fit against your CV, and drafts a tailored ATS PDF, cover letter and form answers. It never submits or sends anything; data stays local apart from calls to the AI provider you choose. It also has a portal scanner, a terminal dashboard and batch evaluation.
Who it is for: Job seekers comfortable running an AI CLI in a terminal
Strengths
- Drafts only: no auto-submit and no mail transport in the code, per the README
- No telemetry or backend; CV goes only to your chosen AI provider
- Portal scanner preconfigured for 100+ companies across Ashby, Greenhouse, Lever and Wellfound
- Works with several AI CLIs and can use local or free-tier models
Weaknesses
- Requires an AI CLI and a model provider; output quality depends on the model
- README says early runs are rough until it learns your profile
- CV rewording check cannot catch every fabrication; manual review required
- Playwright Chromium needed for PDF generation
- GPU optional
- Docker + Compose
- Needs Node.js, Playwright Chromium, AI CLI (Claude Code, Codex, OpenCode, Qwen)
- Models: Claude, Codex, OpenCode, Qwen, local models
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