AI Token processing

CLI proxy tool, automatically compresses shell command output, reduces LLM Token consumption by 60-90%, single-file Rust binary with zero dependencies

CONNECT tunnel proxy, 50-90% Token savings, supports system prompt caching + file diffs + history compression + deduplication

Zero-config local proxy + command engine, 30-95% Token savings, includes Slash commands (/compact etc.) and 20+ model routing

Token optimization meta-installer, one-click integration of Headroom context compression + RTK CLI compression + Memory system

Semantic compression agent, 350+ rules + local LLM (Qwen2.5) semantic condensation, 6-level pipeline, 1,408 req/s concurrency

Token compression agent, 9-level compression dial (15-72%), Caveman output mode, zero code changes

Context optimization layer, 47-92% compression ratio, supports cross-Agent shared memory and reversible compression (CCR), Python/TS dual language

Mathematical prompt compression, semantic chunking + mutual information pruning + Lost-in-the-Middle reordering, pure CPU no GPU

Python context compression middleware, 60-80% savings, automatic quality threshold fallback, supports 5 integration methods

MCP-specific stdio proxy, offloads large outputs to storage and returns a streamlined preview, ~98% savings

Self-hosted token cost analysis platform, Postgres-native, supports 74+ models, cost attribution by user/endpoint/day

Project token cost estimator, natural language input for project description, automatically estimates token consumption per task and recommends the optimal model