wilpel/caveman-compression

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1092 stars · 67 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python tool that compresses text for LLM contexts by stripping predictable grammar while preserving factual content, reducing token usage by 15-58%. It offers three modes: LLM-based (best compression via OpenAI API), NLP-based (free, offline, multilingual via spaCy), and MLM-based (free, offline, predictability-aware).

Use cases

• reduce token costs when sending long context to an LLM
• compress prompt text to fit more content in a context window
• shrink RAG retrieved documents before passing to a model
• compress verbose documentation for LLM consumption
• offline text compression without an API
• compress multilingual text for LLM contexts

When to choose

• you pay per token and want 15-58% context reduction
• you need a free, offline compression option via spaCy or masked language models
• you want to fit more retrieved documents into a fixed context window

When to avoid

• you need byte-perfect lossless reconstruction of original text
• your content is mostly facts with little redundant grammar
• you cannot tolerate LLM-based decompression ambiguity in critical data

Facets

library · maturity active

nlp llm-inference compression prompt-engineering large-language-models python cli semantic-compression token-reduction context-optimization text-compression natural-language-processing

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