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LLMSlim
Semantic prompt compression that never drops instructions.
Open-source Python package cutting LLM token costs by 40–70% in 1 line of code. Surgically compresses prompts, RAG document contexts, and multi-turn chat logs with guaranteed 100% instruction fidelity.
Hey Product Hunt! I'm Yash, a CS/AI undergrad currently doing an AI research internship, and LLMSlim is something I built because I kept hitting the same problem in my own RAG pipelines: most of what I was sending to the API was filler.
LLMSlim runs a deterministic, offline pipeline, no LLM calls, no telemetry, that scores every sentence on centrality, entity density, and instruction presence, then hard-locks anything matching an imperative ("must," "never"), a role marker, or a code fence before it's ever eligible to be cut. Everything else gets ranked and trimmed to hit your target ratio.
It's early v0.2.0, MIT-licensed, 93%+ test coverage, and the benchmarks on the site are the actual numbers from the CI-gated benchmark suite in the repo, not rounded up. Latency ranges from ~24ms on short prompts to ~340ms on 12K-token documents, I'd rather you see the real range than a single best-case number.
What's next: streaming compression (compress_stream), native ONNX embeddings for sub-5ms scoring, and async batch helpers, all tracked on the roadmap in the repo.
I'd genuinely like feedback on where the compression gets it wrong. If you paste a prompt into the playground and it drops something it shouldn't have, that's exactly the kind of report that makes v0.3.0 better. Thanks for taking a look.
About LLMSlim on Product Hunt
“Semantic prompt compression that never drops instructions.”
LLMSlim was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #7 on the daily leaderboard. Open-source Python package cutting LLM token costs by 40–70% in 1 line of code. Surgically compresses prompts, RAG document contexts, and multi-turn chat logs with guaranteed 100% instruction fidelity.
On the analytics side, LLMSlim competes within Open Source, Developer Tools, Artificial Intelligence, GitHub and Vercel Day — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how LLMSlim performed against the three products that launched closest to it on the same day.
Who hunted LLMSlim?
LLMSlim 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.
For a complete overview of LLMSlim including community comment highlights and product details, visit the product overview.
Yashvardhan Thanvi