by aeromomo · Agent Tool · ★ 1.4k
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{ "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Claw Compactor", "description": "Open-source LLM token compression tool that reduces AI agent costs by up to 97% using 5-layer deterministic context compression", "applicationCategory": "DeveloperApplication", "operatingSystem": "Cross-platform", "softwareVersion": "1.0.0", "license": "https://opensource.org/licenses/MIT", "url": "https://github.com/aeromomo/claw-compactor", "downloadUrl": "https://github.com/aeromomo/claw-compactor", "author": { "@type": "Organization", "name": "OpenClaw", "url": "https://openclaw.ai" }, "offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" }, "keywords": "token compression, LLM, AI agent, prompt compression, context window optimization, cost reduction" } -- Claw Compactor — LLM Token Compression & Context Reduction Tool [![Release](https://img
| Stars | 1,412 |
| Forks | 124 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.7492113132256/100 |
| Open Issues | 2 |
| Last Updated | 2026-03-10 |
| Created | 2026-02-10 |
| Platforms | python |
| Est. Tokens | ~96k |
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claw-compactor is 🦞 LLM Token Compression & Reduction Tool — Cut AI agent token costs by up to 97%. 6-layer deterministic context compression for AI agent workspaces. No LLM required. Prompt compression, context windo. It is categorized as a Agent Tool with 1.4k GitHub stars.
claw-compactor is primarily written in Python. It covers topics such as ai-agent-tools, ai-cost-saving, ai-infrastructure.
You can find installation instructions and usage details in the claw-compactor GitHub repository at github.com/aeromomo/claw-compactor. The project has 1.4k stars and 124 forks, indicating an active community.
claw-compactor is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to claw-compactor on Agent Skills Hub include claw-compactor, headroom-desktop, snip. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.