Green-PT/honey-for-devs

What it solves

Honey reduces the high cost and token usage associated with AI coding agents. It prevents agents from generating excessive, redundant code and verbose prose, which typically inflates LLM API bills without adding value to the correctness of the software.

How it works

Honey employs three primary "levers" to minimize token waste:

1. Less code: It follows a "YAGNI" (You Ain't Gonna Need It) approach, prioritizing the simplest possible implementation (e.g., standard libraries over new dependencies) to minimize lines of code.
2. Less prose: It removes conversational filler, hedging, and narration, forcing the agent to provide answers first.
3. Denser handoffs: For agent-to-agent communication, it uses token-efficient formats like ESON (Efficient Structured Object Notation) or compact JSON to reduce handoff size by approximately 50% without losing data.

Additionally, it includes specialized tools for extreme cases: CCR (Compress-Cache-Retrieve) for sampling huge redundant logs and PX for rendering dense read-only text as images to exploit lower image-token pricing.

Who it’s for

Developers using AI coding agents such as Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, and others who want to lower their API costs and speed up agent responses without sacrificing code quality or correctness.

Highlights

• Reproducible Benchmarks: Includes a benchmark suite proving significant reductions in lines of code (up to -71% on some models) and output tokens.
• ESON Format: A schema-first, zero-dependency format designed specifically for efficient agent-to-agent data transfer.
• Safety Carve-outs: Ensures that critical elements like input validation, error handling, and security are never compressed or omitted.
• Multi-intensity levels: Offers lite, full, and ultra modes that the agent selects reflexively based on the request.
• Image-based reading (PX): Can reduce tokens by up to 85% on large read-only files by treating them as images for Fable-class models.