v0.1.6 · MIT
RDB durable session backend for DeepSeek Harness: implements both session-persistence and session-branch (rewind / retry / fork) providers.
v0.0.0 · MIT
Shared engine for dsh-plugin-ops: profile readers, scan rules, fault memory, fix planner, panel API
v0.4.0 · MIT
DeepSeek Harness plugin for previewable cross-preset session migration. Fixed-schema handoffs preserve state, source-model intent, and unresolved images; the original session stays untouched.
v2.0.2 · MIT
Bridge Deepseek-harness into Claude Code for review, critique, delegation, and session import.
v1.27.2 · MIT
dsh Web UI admin plugin: eleven management panels (extensions, skills, MCP servers, subagents, commands, hooks, automation - cron / webhook / workflow, sessions & web search, usage dashboard, todo dock) inside the official dsh settings UI
v0.0.0 · MIT
Manage MCP servers (stdio / streamable-http) for dsh web; tools are reached only through four middleware tools. DSH MCP 管理器:Web 侧边栏「MCP」入口,面板按状态分级展示 MCP 服务器(运行中/连接中/已配置/失败),支持快速接入(stdio / streamable-http 表单)与粘贴 mcpServers JSON 导入。已连接服务器的工具一律经中间层工具(ws_mcp_
v0.5.0 · MIT
把对话里说过的重要事情,变成持续可跟进的计划。 为 deepseek-harness 打造的个人助手:从对话中整理事项、跟踪变化,并在需要时提醒你。
v0.62.2 · Apache-2.0
The best DeepSeek Harness plugin for context insight and management, with context dashboard / browser / sidebar and context command, for context statistics, composition, breakdown, evolution details, understanding how the context is made of, and how it evolves. 一站式 DeepSeek Harness 上下文可视化插件,Context 面板及浏览器和侧边栏与 Context 命令,透视上下文组成、演进、压缩、剪枝等事件与动作。
— · MIT
A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown and Milvus.
v0.3.8 · Apache-2.0
Local-first, self-hosted AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, audit/replay, and a local Console.
v0.5.21 · MIT
Composable, view-based memory for DeepSeek Harness. Pluggable sources and strategies, with three-tier memory out of the box.
v1.8.5 · MIT
Cost tracking for DeepSeek Harness: session and model costs, token usage, budgets, provider balances, and coding-plan quotas. Bilingual English/Chinese UI.
v0.1.0 · Apache-2.0
LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with DeepSeek Harness, Claude Code, OpenClaw, and any agent runtime.
v0.2.5 · MIT
DeepSeek Harness Desktop App: a local AI desktop workspace for DSH Sessions, projects, files, web research, plugins, and Office artifacts.
v0.7.0 · MIT
白箱AGI架构探索:元认知(自我认知循环)、持续学习(知识飞轮)、世界模型(条件空间+语义时空图)、自我改进(自举纪律)、零LLM白箱管线与可审计信任护栏。
v0.1.0 · MIT
为 DeepSeek Harness 带来「跨会话长期记忆 + 后台自我进化」能力的纯插件实现:五轨记忆 · git 分支感知 · 回合内自我审查 · 技能自我进化与技能管理器 · 四轨待办 · COI 调度 · 会话广播 · 会话搜索 · 提示词管理器 · 临时信息便签——零核心修改、零运行时依赖,随装随用、卸载即净。
v1.0.0-rc.2 · Apache-2.0
官方 DeepSeek Harness 的交互式终端 UI 插件:自研 ANSI 极简交互渲染、流式 Markdown/工具卡、16+ 主题、slash 命令与选择器、输入历史与本地偏好持久化、LSP 诊断、memory记忆,很丝滑的开发体验。
v1.0.11 · Apache-2.0
DSH Archive Manager — 在 DeepSeek Harness 中安全查看、恢复和管理已归档会话 · Safely view, restore, and manage archived sessions in DSH
v0.12.10 · Apache-2.0
Second-model AI auto-review for DeepSeek Harness approval requests: a read-only reviewer subagent returns structured allow/deny verdicts with reasons, fail-closed by default, fully auditable from the session log (approval/asked -> autoReview/verdict -> approval/decided).
v0.2.5 · MIT
A portable memory protocol for AI agents — load it as standing rules; a curation discipline + reference spec + optional cap hook.
v0.3.8 · Apache-2.0
Local-first, self-hosted AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, audit/replay, and a local Console.
v0.9.3 · MIT
A local Git-backed long-term memory bundle for DeepSeek Harness
v0.29.0 · MIT
Cross-session memory plugin for DeepSeek Harness: seven-layer SQLite store (soul/user/project/fact/lesson/topic/rules), BM25 retrieval, per-window dream consolidation. 跨会话七层长期记忆插件。
v0.1.179 · no license
基本稳定可用 A context-compression plugin for small context windows (a 100K context is enough), token savings (5x fewer tokens), and month-long single sessions (billions of tokens).上下文压缩插件,兼顾小窗口(100k上下文足矣)省token(省5倍token)和超长会话(数月级别几十亿token单会话)。billion-context is all you need