Prompt Compression
Top open-source Prompt Compression AI systems, agents, and MCP tools.
Discover and install verified Prompt Compression AI systems, autonomous agent skills, and Model Context Protocol servers on AIStoreHub. Reusable, security-scanned, and ready for 1-click execution in Cursor, Claude Code, and terminal workflows.
Available AI Systems & Skills (13)
Open in Advanced FilterNever hit rate limits again. One OpenAI-compatible endpoint for 60+ providers & 1,200+ models — smart auto-fallback, prompt compression, 60-language dashboard. Plug into Claude Code, Cursor, Codex & more. Free, open-source, self-hosted.
Menu bar app for macOS, Windows and Linux that cuts Claude Code and ChatGPT Codex token costs by ~50%
9RouterX (9R Next Go Generation): Single-binary Go AI router, LLM gateway & 20-40% token saver. Route Claude Code, Cursor & Codex across 155 providers, 28 OAuth & 248 free models (24 keyless) — OpenAI, Claude, Gemini, DeepSeek, Groq, Ollama & MCP.
dsh-sieve: context engineering & token optimization plugin for DeepSeek Harness (DSH) — tool output filtering, context pruning, progressive skill disclosure. 36% smaller payload in offline replay. DSH 上下文管理与 token 优化节省插件。
HEWN 2.0 2026: AI Output Router for Precision Summaries & Polished Code
Local proxy that compresses your LLM API requests so you pay less, with no change to the answers. Trims wasted tokens from prompts, history, tool output, and code before they're sent: -31% input / -74% output, measured live. Any provider, no extra model calls. Also an MCP server and embeddable library (Rust, Python, Ruby, Kotlin, Swift, JS/TS).
7.4 billion tokens per month. 34 free LLM providers. 635 free model endpoints. All behind one /v1 endpoint, plus any custom OpenAI-compatible endpoint. Smart routing, automatic failover, encrypted keys. Personal experimentation only.
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
Portable CC-inspired skills for memory, verification, multi-agent coordination, context compression, and proactive coding-agent workflows.
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
Your agent pays twice for output it has already seen. OMNI returns a handle instead: 97.2% off a file read twice. Nothing deleted, nothing invented.
Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.
Build & Monetize for Prompt Compression
Have you created an autonomous agent, MCP server, or Claude Code skill for Prompt Compression? Publish it to AIStoreHub with dual-currency checkout (USD $ + INR ₹) and earn 85–90% net revenue.