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