context-compressor: AI-powered text compression library for RAG systems and API calls

by Huzaifa785 · Agent Tool · ★ 90

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install context-compressor

pip install context-compressor
pip install "context-compressor[ml]"          # ML models only

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aianthropicapi-optimizationcost-optimizationextractivefastapilangchainllmmachine-learningneural-networks

Quick Facts

Stars90
Forks14
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score70/100
Open Issues2
Last Updated2025-08-16
Created2025-08-14
Platformspython
Est. Tokens~12k

About context-compressor

Context Compressor The most powerful AI-powered text compression library for RAG systems and API calls. Reduce token usage by up to 80% while preserving semantic meaning with state-of-the-art compression strategies.

Compatible Skills

These tools work well together with context-compressor for enhanced workflows:

  • llm-use — semantic(0.15)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)

context-compressor alternative? Top 6 similar tools

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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