Galadriel harness trims Claude costs, latency

This is a strong systems-level reminder that long-running agent quality is often constrained more by context plumbing than by model quality. The pitch is credible as an architecture pattern, even if the exact savings are deployment-specific and self-reported.

• –The main trick is separating stable prefixes from churn: tool schemas, persona/system prompts, and trailing history each get their own cache behavior instead of forcing every turn through a giant re-send.
• –MemPalace gives the agent persistent memory without stuffing everything back into the prompt, which is the right shape for continuity in long-lived assistants.
• –The repo’s own numbers are useful as a case study, but not a universal benchmark; treat the 86.5% cache hit and 71.2% token-savings figures as evidence of good instrumentation, not a guaranteed result.
• –For teams building Claude-based agents, the practical takeaway is that compaction, cache thresholds, and prompt hygiene can matter as much as model choice.
• –The local/private-subnet positioning makes sense for internal tools where API key control, auditability, and low-latency operation matter more than consumer polish.