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PsYcGoD/sage

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SAGE — Save AI coding tokens by compressing terminal output, keeping raw logs local, and tracking real savings. 🧠S.A.G.E - Smart Agent Guidance Engine for AI coding assistants. by

MCP

PsYcGoD/sage

Added 3 Sept 2026

#ai-agents #automation #cli #command-line #developer-tools #devops #productivity #python

Overview

SAGE is a Python utility that compresses terminal output to reduce token usage when working with AI coding assistants. It keeps raw logs local for reference and tracks the token savings achieved. The tool is designed as a Smart Agent Guidance Engine for AI coding workflows.

Best for

Best for
Developers using AI coding assistants who want to cut token costs.

Use cases

  • Compress terminal output before sending to AI assistants
  • Track token savings from compressed logs
  • Keep raw logs locally while using compressed versions

How to use

Install

npx -y psycgod-sage install

Tools exposed

  • Local-only

Tested with

Claude Code, Cursor, Windsurf, Cline

Notes

SAGE is a Python utility that compresses terminal output to reduce token usage when working with AI coding assistants. It keeps raw logs local for reference and tracks the token savings achieved. The tool is designed as a Smart Agent Guidance Engine for AI coding workflows.

10 stars on GitHub. Last updated 2026-08-04. Licensed MIT.

Use cases

  • Compress terminal output before sending to AI assistants
  • Track token savings from compressed logs
  • Keep raw logs locally while using compressed versions

Pros

  • Reduces token consumption for AI coding sessions
  • Preserves raw logs for debugging
  • Lightweight Python tool

Cons

  • Small user base (10 stars) indicates early-stage project
  • Requires local setup and activation
  • Limited documentation or community support

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Reduces token consumption for AI coding sessions
  • Preserves raw logs for debugging
  • Lightweight Python tool

Cons

  • Small user base (10 stars) indicates early-stage project
  • Requires local setup and activation
  • Limited documentation or community support
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