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dsh-auto-compact

Auto Compact

DeepSeek Harness 的自动上下文压缩:基于令牌压力的摘要检查点,支持可配置的触发阈值(默认为 LATE)和设置卡片。

插件会安装到这里;不确定时保持 web。

npx -y @deepseek-ai/dsh plugin --profile web add github:lileikeji/dsh-auto-compact#2c15ea577b133060ac676e56acad3c5d40ae6f5a
README兼容性版本

兼容性与来源证明

Auto Compact 以 dsh-auto-compact 发布,当前版本为 0.1.0。Plugin Hub 会校验它的 manifest,并保存精确安装来源,便于复现安装结果。

DSH 兼容范围
*
运行环境
web
发布来源
github
Registry 更新时间
2026/8/20

版本

0.1.0stable
2026/8/20

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最新版
0.1.0
DSH
*
HMR
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未声明可安全裁剪
解包体积
未提供
文件数
未提供
Surface
web
许可证
MIT
发布源
github
GitHub
★ 2
周下载
0
最近提交
2026/8/25
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继续浏览 memory-context 分类下经过校验的插件。

Experimental Session Search@deepseek-ai/dsh-experimental-session-search为 Agents 提供搜索和读取当前工作区早期会话的工具。Contextdsh-context用于上下文洞察和管理的 DeepSeek Harness 插件,提供上下文仪表板和上下文命令,帮助了解上下文的构成及其演变过程。Memory Plugin@openviking/dsh-memory-plugin适用于 DeepSeek Harness 的 OpenViking 记忆与上下文套件Ab Memorydsh-ab-memory--- description: "dsh-ab-memory 包:一种跨会话、基于文件的智能体记忆,提供五个工具(remember / recall / forget / load_skill / distill)以及始终启用的轻量注入。" kind: "package-reference" ---

README

dsh-auto-compact

Automatic context compaction for DeepSeek Harness: at every agent step it prices the routed request against the model's context window, and once pressure crosses the configured threshold it condenses the older span of the conversation into a durable <compacted-summary> checkpoint (via the conversation's own model, reusing the warm prefix cache) — so long sessions keep working without losing essential context.

It wraps the harness's own token-meter-driven engine (@deepseek-ai/dsh-compaction-basic); this plugin adds a LATE-by-default trigger, a settings card, and install docs.

Why "compact later"?

The harness's pi-ai provider falls back to DEFAULT_CONTEXT_WINDOW = 262144 (256K) for models that do not declare a window. With the default thresholdRatio of 0.8 that means compression can fire at ~205K tokens — only ~20% of a real 1M-window model. This plugin defaults to thresholdRatio: 0.92 and documents the window fix, so big windows are actually used.

Install

# profile dir (C:\Users\<you>\.dsh\profiles\<name>)
pnpm add -w dsh-auto-compact
# or from GitHub:
pnpm add -w dsh-auto-compact@github:lileikeji/dsh-auto-compact

Add dsh-auto-compact to the profile package.json dsh.profile.bundles list, then disable the harness's own engine so only one engine owns compaction:

# profile cordis.patch.yml
- id: compaction-basic
  name: '@deepseek-ai/dsh-compaction-basic'
  disabled: true

Window configuration (important)

The engine reads the model's context window from the LLM adapter. If your model's window is not declared (pi-ai defaults to 256K), set it per provider in ~/.dsh/settings.yaml:

llm-pi-ai:
  providers:
    my-provider:
      apiKeyEnv: MY_API_KEY
      defaultContextWindow: 1000000   # the model's real window

Configuration (settings card)

Settings → 插件 → 自动压缩上下文(auto-compact):

  • thresholdRatio — trigger threshold as a fraction of the context window (default 0.92).
  • retainRatio — fraction of the recent conversation kept verbatim (default 0.12).
  • maxTokens — summary output cap (default 8192).
  • auto — enable/disable automatic compaction.

Changes apply after a profile restart.

How it works

  1. agent/pre-step: price the latest durable routed request with the token meter.
  2. If totalTokens ≥ thresholdRatio × contextWindow (output-budget aware when known), pick a head-anchored span that never splits a tool call/result pair, retaining the recent tail.
  3. Summarize that span with the conversation's own model (system prompt + tools + leading messages replayed verbatim so the provider's KV cache is reused), appending the compaction instruction.
  4. Land the checkpoint as a compaction/start → summary → compaction/end transaction on the session surface; subsequent steps continue from the checkpoint.

License

MIT