Galadriel: Open-Source Warm-Cache Harness for Persistent Claude Agents

A Reddit user has open-sourced Galadriel, a harness for persistent Claude agents that achieves 87% cost savings and sub-3s latency on 100K token contexts by optimizing prompt caching. The project, released under MIT license, targets the memory and cost issues often called the "Goldfish Problem" in AI coding agents.

Key Features

• 3-Tier Stacked Caching: Separate cache breakpoints for tool definitions, system prompts (CLAUDE.md), and trailing conversation history. This avoids cache invalidation across different context segments.
• Integrated MemPalace: A vector-based persistent memory system that does not break the prompt cache, enabling permanent recall.
• Privacy-first: Designed for private subnets — no middleman, no message caps, just your API key and rules.
• CLAUDE.md Guidelines (Karpathy-style): Built-in rules to prevent agent bloat (unnecessary context expansion).

Benchmarks

According to the author, tested against OpenClaw/Cursor workflows:

• Cost: $10 for every $100 normally spent (87% reduction).
• Latency: 100K token context drops from 11s to <3s (85% improvement).

Who It's For

Developers running persistent Claude agents for tasks like infrastructure management or codebase maintenance who are paying high API costs due to uncached context.

Setup

The harness is currently customized for Discord (the author's personal setup), but the caching logic is generic. Clone the repo and adapt the transport layer for your needs.

GitHub

github.com/avasol/galadriel-public (MIT License)