#Token-reduction
Showing 13 of 13 repositories tagged #token-reduction, ranked by stars
[EMNLP 2024 & AAAI 2026] A powerful toolkit for compressing large models including LLMs, VLMs, and video generative models.
97% token reduction for AI coding sessions — zero deps, 31 languages, MCP server
CLI proxy that reduces LLM token usage by 60-90%. Declarative YAML filters for Claude Code, Cursor, Copilot, Gemini. rtk alternative in Go.
The Context OS for Autonomous AI Agents. Distill terminal noise into pure semantic signal, stop agent hallucinations, and cut token costs by up to 90%.
A discovery and compression tool for your Python codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project | Code structure visualization | LLM Context Window Efficiency | Static analysis for AI | Large Language Model tooling #LLM #AI #Python #CodeAnalysis #ContextWindow #DeveloperTools
AI-powered text compression library for RAG systems and API calls. Reduce token usage by up to 50-60% while preserving semantic meaning with advanced compression strategies.
A lightweight tool to optimize your Javascript / Typescript project for LLM context windows by using a knowledge graph | AI code understanding | LLM context enhancement | Code structure visualization | Static analysis for AI | Large Language Model tooling #LLM #AI #JavaScript #TypeScript #CodeAnalysis #ContextWindow #DeveloperTools
ZON → 35-70% cheaper LLM prompts than JSON/TOON. Zero overhead.
⚡ Cut Claude token usage by 90%+ — free, open-source, local-first context compression for Claude Code. Hybrid RAG (BM25 + ONNX vectors), AST chunking, reranking. No API needed.
Customizable CLI proxy for coding agents that cuts noisy terminal output while preserving command behavior
Token-compression skill. An adaptation of caveman — short common words, trust context, say just enough, be laconic.
Verdict-first output for AI coding agents. Tiny prompt + installer for Claude Code, Codex, Gemini, Cursor, opencode, and 30+ agents.
Context-Optimized Memory Bank — Reduce AI token usage with structured documentation and cache-aware reading strategies