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LLMSlim v0.3.0
Cut LLM token costs 40-70% with offline prompt compression
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
Hey PH! I'm Yashvardhan, the builder behind LLMSlim.
The idea started from a real pain point: I was running a RAG pipeline that passed massive retrieved contexts to Claude, and watching my API bill climb even when 60% of those tokens were filler sentences and redundant background prose that the model just ignored anyway.
Truncation killed instruction fidelity. Summarization added latency and cost. I wanted something that could surgically remove noise while guaranteeing system directives and code blocks survive untouched. So I built LLMSlim.
v0.3.0 is the biggest release yet: a full hybrid strategy engine. The extractive pipeline (offline TF-IDF + LexRank centrality) now feeds into an optional generative rewrite pass that uses any LLM you already have to semantically compress what's left. The result is cleaner context, not just shorter context.
Happy to answer questions about the architecture, the Priority Tier system, or anything about building open-source Python libraries. Would genuinely love feedback from folks running production LLM pipelines.
honestly this looks super useful, especially the one line integration. one thing i'd love to see is a built in benchmarking mode that runs the compression against a small eval set you provide and shows you the actual token savings vs quality scores before you ship it to production. would make it way easier to trust the fidelity claim without manually testing every prompt change
About LLMSlim v0.3.0 on Product Hunt
“Cut LLM token costs 40-70% with offline prompt compression”
LLMSlim v0.3.0 was submitted on Product Hunt and earned 7 upvotes and 2 comments, placing #158 on the daily leaderboard. 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
LLMSlim v0.3.0 was featured in Open Source (68.6k followers), Developer Tools (516.2k followers), Artificial Intelligence (474.3k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 224.8k products, making this a competitive space to launch in.
Who hunted LLMSlim v0.3.0?
LLMSlim v0.3.0 was hunted by Yashvardhan Thanvi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Yashvardhan Thanvi