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Hacker News 热门(buzzing.cc 中文翻译)· s0ck_r4w·· 2026-07-24精选AI 评分79

Claude-thermos:保持 Claude 会话缓存热度,避免重新编码费用

Show HN: Claude-thermos 能为您保持 Claude 会话的热度

AI 导读

Claude-thermos 通过本地反向代理监控 Claude Code 会话,在主智能体因等待子智能体而空闲超过 5 分钟时,自动发送预热请求刷新提示缓存。实测约 185 次本地会话中,缓存过期导致的重新编码占账单约 22%。工具以 uvx 运行,支持自定义空闲阈值和预热间隔。

推荐理由

一个小工具解决了一个被忽视的成本黑洞,Claude Code 的缓存过期,作者自己测了 185 个会话,省下 22% 费用,对于把 Claude 当开发助手的团队来说是个必装工具。

正文 · 原文

Stop paying to rebuild your Claude Code cache. When your main agent waits on a subagent for more than 5 minutes, its prompt cache silently expires, and the next turn re-encodes your entire conversation at the write rate instead of reading it back cheap. On long sessions with many subagents that's roughly 20% of your bill. claude-thermos keeps the cache warm so you never pay that tax.

Use

Run Claude Code exactly as you normally would, but through claude-thermos with uvx:

uvx claude-thermos                     # instead of: claude
uvx claude-thermos -p "fix the bug"    # any claude args pass straight through

Requires Python 3.11+ and the claude CLI on your PATH.

That's it. Warming runs automatically in the background. To disable it for a run without changing the command, set CLAUDE_WARMER_DISABLE=1.

Tuning (all optional):

Flag Default Meaning
--idle 270 Seconds the main agent must be idle before warming kicks in
--interval 270 Seconds between warming cycles
--max-cycles 4 Max warms per idle episode (auto for unlimited)
--subagent-window 540 Seconds a subagent counts as "still active"

Why your cache keeps expiring

Claude Code's prompt cache uses a 5-minute TTL. Every turn, your whole conversation history is served from cache at 0.1x the input price instead of being re-sent at full price, as long as the cache stays alive.

The cache expires if more than 5 minutes pass between requests on the same prefix. The dominant trigger for that gap is not you thinking. It's the main agent blocked on a subagent that runs longer than 5 minutes. A subagent has a different system prompt and tool set, so its requests have a different cache prefix and never refresh the main agent's. While the subagent works, the main agent's cached history ages untouched; past 5 minutes it's gone. When the subagent returns, the main agent resumes with a byte-identical, append-only history, and finds its cache missing, forcing a full re-encode at the 1.25x write rate.

By then the history is large, so the re-encode is expensive: individual collapses re-write 200K to 500K tokens. Measured across roughly 185 local sessions, these rebuilds accounted for about 22% of the total bill, money spent re-encoding content that was already cached moments earlier.

How it works

claude-thermos launches Claude Code behind a small local reverse proxy (it points ANTHROPIC_BASE_URL at a loopback port; all traffic still goes to the real Anthropic API).

  1. Observe. The proxy watches /v1/messages traffic and groups it into sessions and lineages, a lineage being one cache prefix, keyed by model + tool set + system text. The first tool-bearing lineage is the main agent; the rest are subagents.
  2. Detect the danger window. When the main lineage goes idle and a subagent is actively running, the main prefix is at risk of expiring.
  3. Warm. On an interval under the 5-minute TTL, it replays the main agent's last real request as a warm request: identical cacheable prefix, but max_tokens: 1 and no streaming. The single token is thrown away; the point is the prefill, which reads and refreshes the full cached prefix. Warm requests go directly to the API, never through the proxy, so they can't disturb real traffic.
  4. Result. When the subagent finishes, the main agent's cache is still warm. It pays a cheap read instead of a full rewrite.

Each warm costs a cache read (0.1x); each rewrite it prevents would have cost a write (1.25x) on a much larger prefix, so the trade is heavily in your favor.

Event logs & savings

Every session writes to:

~/.claude-thermos/logs/<session_id>/
├── events.jsonl    # append-only structured event stream
└── summary.json    # rollup totals, written when the session ends

events.jsonl records each request/response's token usage plus every warming decision (warm_fired, warm_result, cap_reached, resume_detected, and so on). summary.json is the rollup you'll usually read:

Field Meaning
warms_fired Warm requests sent
cache_read_total Tokens read back by those warms
episodes Idle-with-subagent episodes that ended in a successful resume (a rewrite actually avoided)
rewrite_avoided_tokens Tokens that would have been re-written, summed across episodes
warm_cost What warming cost you: 0.1 × cache_read_total
rewrite_avoided_cost What it saved: 1.25 × rewrite_avoided_tokens
net_savings rewrite_avoided_cost − warm_cost

All three cost figures are in base-input-token units (token counts already weighted by their cache multiplier). To turn net_savings into dollars, multiply it by your model's price per input token:

dollars saved ≈ net_savings × (input token price)

For example, at an input price of $3 / 1M tokens, a net_savings of 1_200_000 is about 1_200_000 × $3 / 1_000_000 = $3.60 saved that session.

来源:Hacker News 热门(buzzing.cc 中文翻译) · github.com