Context Gateway – Compress agent context before it hits the LLM
SLM classifiers compress context based on tool call intent before LLM sees it.

Beats full-context GPT-4o at 80% token budget with zero AI overhead.
Developers building LLM applications and RAG systems
SLM classifiers compress context based on tool call intent before LLM sees it.
Entropy-based context compression beats naive token stuffing, but the category is crowded.
Cuts token bills 68% by swapping full history for vector-retrieved signals.
Cuts cargo test output from 61 lines to 1 — saves 60-90% of wasted LLM tokens.
Cuts token costs 70% with receipts proving no accuracy drop on hard evals.
Prompt compression cuts token costs 40-60%, but it's lossless text optimization, not a novel insight.