How does LLMSlim guarantee zero instruction loss in prompt compression?

LLMSlim uses an explicit Priority Tier engine heuristic. Sentence boundaries containing imperative directives ('must', 'never', 'ensure', system prompt role markers, numbered lists, and code blocks) are tagged as Priority 4 Critical, guaranteeing 100.0% retention across all compression ratios.

Will compressing my RAG documents reduce LLM answer quality?

Empirical benchmarks demonstrate 95%+ entity retention and meaning preservation. By eliminating redundant boilerplate text and unfocused prose, models focus more directly on key facts, often improving task performance while cutting costs by 40-70%.

Does LLMSlim require heavy model downloads or internet access?

No. Core LLMSlim works 100% offline out-of-the-box using lightweight TF-IDF centrality ranking with zero external model dependencies. For deep transformer semantic similarity, you can optionally install 'llmslim[semantic]'.

What processing latency overhead does LLMSlim add to API requests?

The standard Python CPU execution latency is ~28ms for standard system prompts and <120ms for 5,000-token RAG contexts. Upcoming v0.3 releases introduce ONNX runtime acceleration and sub-5ms Rust C-extensions.

Is LLMSlim tied to any specific LLM provider?

LLMSlim is completely model-agnostic. It compresses raw text before it ever leaves your server, making it 100% compatible with OpenAI GPT-5/GPT-4o, Anthropic Claude, Google Gemini, DeepSeek, Llama, and local vLLM instances.

Getting Started4 min read•Updated July 2026

Installing LLMSlim

Q: Is LLMSlim tied to any specific LLM provider?

LLMSlim is completely model-agnostic. It compresses raw text before it ever leaves your server, making it 100% compatible with OpenAI GPT-5/GPT-4o, Anthropic Claude, Google Gemini, DeepSeek, Llama, and local vLLM instances.

Complete installation guide for Python, Node.js, and CLI environments.

Standard PyPI Installation

Install the core LLMSlim package using `pip`:

Fast Package Manager

You can also install with uv: 'uv add llmslim' for sub-second installation speed.

terminal

pip install llmslim

Optional Deep Semantic Extra

For transformer-based semantic embeddings (cross-sentence similarity analysis), install the `[semantic]` extra:

terminal

pip install 'llmslim[semantic]'

JavaScript / Node.js Package

For Next.js, Node.js, and browser environments, install via `npm` or `pnpm`:

terminal

npm install @llmslim/core

Frequently Asked Questions

What Python versions are supported?

LLMSlim supports Python 3.9, 3.10, 3.11, 3.12, and 3.13 across Linux, macOS, and Windows.

Are there any heavy native binary requirements?

No. Core installation uses pure Python and standard C-extensions. Optional deep semantic similarity models can be loaded on-demand.

Next Recommended Guides

Quick Start Guide

CLI Reference

Configuration