Getting Started3 min read•Updated July 2026
Getting Started with LLMSlim
High-performance semantic prompt & context compression in under 2 minutes.
Overview & Philosophy
LLMSlim is an open-source enterprise prompt compression library designed to eliminate context redundancy, boilerplate fluff, and filler prose before sending requests to Large Language Models (LLMs).
By compressing system prompts, RAG document search contexts, and multi-turn chat histories, LLMSlim reduces input token costs by **40% to 70%** while adding **< 30ms** local processing overhead.
Zero Setup Required
You can integrate LLMSlim into any existing Python or TypeScript codebase in 1 line of code without modifying your model API callers.
Why Prompt Compression Matters
As RAG applications scale, context windows expand to 128k–1M tokens. However, model pricing scales linearly per token, and processing long unfocused prompts increases Time-to-First-Token (TTFT) latency.
LLMSlim solves three critical challenges:
1. **Cost Reduction**: Save thousands of dollars monthly on flagship models like GPT-4o, Claude Sonnet, and Gemini Pro.
2. **Latency Optimization**: Shorter prompts process significantly faster on LLM provider infrastructure.
3. **Focus Elevation**: Removing low-value boilerplate prevents LLMs from hallucinating on irrelevant background context.
One-Minute Example
Here is how to compress a messy prompt into an optimized token-dense payload:
example.py
from llmslim import compress
raw_prompt = """
System Directive: You must always respond in JSON format with keys 'status' and 'summary'.
Please be aware that our customer support policy explicitly states that returns are valid for 30 days.
In addition, it is very important to mention that shipping fees are non-refundable under any circumstances whatsoever.
"""
# Compress with 50% target token reduction
result = compress(raw_prompt, target_ratio=0.5)
print(f"Original Tokens: {result.original_tokens}")
print(f"Compressed Tokens: {result.compressed_tokens}")
print(f"Savings Ratio: {result.savings_percent:.1f}%")
print("\n--- Compressed Prompt ---\n")
print(result.compressed_text)Frequently Asked Questions
Does LLMSlim require internet access or external API calls?
No. Core LLMSlim runs 100% offline out-of-the-box using CPU-accelerated TF-IDF centrality and LexRank sentence scoring with zero external API dependencies.
Will compressing prompts alter LLM output accuracy?
Empirical evaluation on standard benchmarks shows zero degradation in task execution because LLMSlim's Priority Tier Shield explicitly protects system directives, constraints, and code syntax.