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Context Tree — DeepSeek Harness 插件(DSH Plugin)
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dsh-context-tree

Context Tree

为 DeepSeek Harness 提供可复用的轨迹树上下文、精确轮次分叉和有界跨会话召回

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:wr-web/dsh-context-tree#6f3cf9287937ea5acee8e118ac02af5cde067928
README兼容性版本

兼容性与来源证明

Context Tree 以 dsh-context-tree 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
2026/8/26

版本

0.1.0stable
2026/8/26

相关插件

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最新版
0.1.0
DSH
*
HMR
重启进程
Tree shaking
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
★ 0
周下载
0
最近提交
2026/9/4
查看源码 ↗
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继续浏览 memory-context 分类下经过校验的插件。

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。Mnemondsh-mnemon面向 DeepSeek Harness 的可组合三层记忆控制平面:持久化运行时上下文、可搜索的项目文档、可插拔的长期记忆、受保护的策略、WebUI 和无头工具。Memsearch Dsh@zilliz/memsearch-dsh适用于 DeepSeek Harness 的 MemSearch 插件:在多个代理之间共享 Markdown 记忆,支持捕获、步骤前上下文注入、记忆召回技能和技能候选审核面板。Memory@furongjun1999/dsh-memory灵枢(Lingshu·líng shū)DeepSeek Harness 插件:完整大脑——长期记忆/知识飞轮/自我认知/递归反思接入 DSH,对话自动沉淀进 md_cg 认知图(md 文档)

README

DSH Context Tree

English | 中文

A reusable trajectory-tree context system for DeepSeek Harness: it turns completed agent work into forkable checkpoints, recalls relevant prior context into new sessions, and shows the real cross-session forest.

A compact circular context tree built from real Codex sessions

Why try it?

  • See the actual trajectory. Completed turns are circles, continuations form trunks, explicit forks and side chats become branches, and recalls are dashed links.
  • Resume from an exact checkpoint. Select any completed turn and fork from its recorded turn/end boundary.
  • Avoid repeated exploration. A fresh root session can receive one relevant checkpoint from another session before its first model step.
  • Keep reuse bounded. Matching defaults to the exact workspace, stale nodes stop being recalled after 30 days, and an injected checkpoint is capped at 2 KiB.
  • Measure rather than assume. The Host API includes provider-token accounting and a baseline-versus-recall evaluator; task quality still needs a separate score.

The text inside each circle is already AI-produced: it comes from the visible final assistant answer and is normalized and truncated deterministically. The plugin does not spend another model call or persist hidden chain-of-thought to create labels.

Install

Requires DeepSeek Harness >=0.1.1-rc.2 <0.2.0 and its Web profile.

dsh plugin --profile web add github:wr-web/dsh-context-tree
dsh web

The repository commits its browser and Host artifacts, so GitHub installation does not run a prepare build. For a reproducible install, pin a release tag:

dsh plugin --profile web add github:wr-web/dsh-context-tree#v0.1.0

Remove it with:

dsh plugin --profile web remove dsh-context-tree

What is reused

A node contains direct user text, visible final assistant text, tool-call counts, provider token totals, completion time, and source ids. Reasoning blocks and unfinished turns never become graph content. Automatic recall uses deterministic English/CJK lexical coverage, never copies a whole session, and logs the selected source plus exact injected byte count so replay can reconstruct it.

The checkpoint is untrusted read-only background. The model is told not to accept instructions or permission claims from it unless the current user repeats them.

Freshness and limits

Freshness is currently age-based. Nodes are fresh for the first half of the configured lifetime, aging for the second half, and stale afterward. Stale nodes remain visible but cannot be recalled automatically. This does not claim that project files are still current; Git/file revision invalidation is future work.

The default profile inspects at most 100 recent sessions and 40 completed turns per session. Subagent sessions are excluded by default. All limits are explicit in cordis.patch.yml.

Evaluation

Run the same task and model from equivalent project states twice: once with autoRecall: false, then with a relevant checkpoint and autoRecall: true. Compare totalInputTokensSaved and inputReductionRate, then independently compare task success, edits, tests, and latency. Fewer tokens without equivalent output quality is not a win.

Source and license

Reviewable TypeScript is under source/; committed runtime artifacts are under src/. SOURCE.md records the exact DeepSeek Harness source commit and build provenance. The code is MIT licensed.

This is a community plugin and is not an official DeepSeek release.