14-stage Fusion Pipeline for LLM token compression — reversible compression, AST-aware code analysis, intelligent content routing. Zero LLM inference cost. MIT licensed.

Drop-in prompt compression for production LLM apps. Cut your token bill 40-60% without changing your code. Python SDK, LLMLingua-2, MIT.

JavaScript/TypeScript implementation of LLMLingua-2 (Experimental)

A self-improving knowledge base about LLM agent infrastructure

Python command-line tool for interacting with AI models through the OpenRouter API/Cloudflare AI Gateway, or local self-hosted Ollama. Optionally support Microsoft LLMLingua prompt token compression

Lossless-first prompt compression for JSON, YAML, CSV, and Markdown. Library, CLI, MCP server, desktop app, and browser extension.

Reverse T9 for LLMs. Free, open-source prompt compressor for your AI prompts and agents.

Rolling context compression for Claude Code — never hit the context wall. Auto-compresses old messages while keeping recent context verbatim. Zero config, zero latency. Works as a Claude Code plugin.

A curated list of strategies, tools, papers, and resources for reducing LLM token costs and improving efficiency in production.

CUTIA: compress prompts while preserving quality

A Claude Code skill that shrinks massive prompts and files using LLMLingua to save tokens.

This repository is the official implementation of Generative Context Distillation.

AI-assisted context management and prompt compression toolkit for developer productivity, ADR workflows, and LLM token optimization.

TOON for TYPO3 — a compact, human-readable, and token-efficient data format for AI prompts & LLM contexts. Perfect for ChatGPT, Gemini, Claude, Mistral, and OpenAI integrations (JSON ⇄ TOON).

LLMLingua-2 prompt compression hook for Claude Code — cut token usage by ~55%

KO-first bilingual (KO/EN) LLM output-compression skill for AI coding agents. Tokens are money — spend them like a miser.

LLM judgment control layer for drift, memory loss, hallucination, and cost optimization.

Advanced token reduction and prompt optimization framework for LLMs, featuring linguistic, algorithmic, and architectural patterns.

Enhance the performance and cost-efficiency of large-scale Retrieval Augmented Generation (RAG) applications. Learn to integrate vector search with traditional database operations and apply techniques like prefiltering, postfiltering, projection, and prompt compression.

Compress LLM Prompts and save 80%+ on GPT-4 in Python