awesome-claws and awesome-openclaw

These are competitors—both curate lists of OpenClaw-related resources, with the first taking a broader approach to AI agents inspired by OpenClaw while the second focuses specifically on official OpenClaw projects and integrations.

awesome-claws
46
Emerging
awesome-openclaw
43
Emerging
Maintenance 10/25
Adoption 10/25
Maturity 9/25
Community 17/25
Maintenance 13/25
Adoption 9/25
Maturity 9/25
Community 12/25
Stars: 324
Forks: 36
Downloads:
Commits (30d): 0
Language:
License: MIT
Stars: 94
Forks: 10
Downloads:
Commits (30d): 0
Language:
License: CC0-1.0
No Package No Dependents
No Package No Dependents

About awesome-claws

machinae/awesome-claws

A curated list of awesome AI agents inspired by OpenClaw

Covers 34+ open-source AI agent implementations across TypeScript, Python, Rust, Go, and other languages, ranging from ultra-lightweight binaries (4MB, ESP32) to feature-rich multi-agent platforms. Projects span specialized domains—mobile automation, edge hardware, research workflows, privacy-first deployments—with common patterns including multi-channel messaging, sandboxed execution, MCP tool integration, and persistent memory. The collection emphasizes production-ready alternatives to the original OpenClaw architecture across different resource constraints and deployment environments.

About awesome-openclaw

alvinunreal/awesome-openclaw

A curated list of the best OpenClaw resources: official projects, skills, plugins, dashboards, deployment tooling, memory systems, and guides.

Organizes the OpenClaw ecosystem into specialized categories—skills registries, memory/context systems, deployment tooling (Ansible, Nix, Homebrew), and alternative runtimes—enabling developers to discover both official and community implementations. The curated structure emphasizes practical infrastructure patterns like Lobster for workflow composition and Clawdinators for always-on deployments, alongside extensibility through skill registries and plugin integrations. Actively crowdsourced with contribution guidelines, making it a living reference for both newcomers evaluating the platform and experienced users seeking production-grade tooling.

Scores updated daily from GitHub, PyPI, and npm data. How scores work