LLMSlim v0.3.0
Cut LLM token costs 40-70% with offline prompt compression
LLMSlim v0.3.0 launched on July 21, 2026 with 4 votes, #6,023 of 24,623 launches that month and more than 75% of that year's launches.
What it does
v0.3.0 ships hybrid prompt compression: offline TF-IDF extraction pre-prunes context, then an optional LLM rewrite pass semantically optimizes what remains. Result: 40-70% fewer tokens billed, sub-30ms CPU latency, and 100% retention of system directives, code blocks, and JSON schemas. Works with OpenAI, Anthropic, Gemini, LangChain, LlamaIndex, Ollama and more. Zero dependencies. Pure Python. pip install llmslim
Open-source Python library for extractive, rewrite, and hybrid LLM context compression with provenance-aware priority handling.
LLMSlim makes long prompts, documents, retrieval, and chat history easier to reason about—with local extractive compression, provider-aware strategies, and provenance-aware priorities. 24 curated samples stay visible in the methodology; the release story also includes categories, languages, schema contracts, security regression, and machine-readable artifacts. LLMSlim gives context a smaller, more intentional shape—without hiding the role, strategy, or boundary that produced it. Illustrative motion only. The released library exposes results—not hidden model reasoning. Local sentence selection. Deterministic, inspectable, and available without a provider. RAG text cannot gain protected…from llmslim.app
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