v0.54.0 · MIT
OMK — Observe. Measure. Know. Evidence-backed knowledge changes for AI applications.
v0.3.3 · AGPL-3.0
Cherry Studio-style knowledge base system for DeepSeek Harness (DSH): bases, documents, chunking, embeddings (OpenAI-compatible / Ollama / local / lexical fallback), retrieval, model-facing tools, and a browser management panel
v1.0.2 · MIT
Local and remote knowledge bases for DeepSeek Harness, with scoped recall, controlled write-back, and a Web management console
— · no license
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
v0.7.1 · MIT
🧠 The memory that dreams — self-evolving memory for DeepSeek Harness: your AI remembers across sessions, consolidates in its sleep, forgets what matters less, and grows smarter over time. Fully offline & private.
— · Apache-2.0
📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | https://misakanet.org
v0.2.9 · MIT
AGI 的长期记忆基础设施。让 AI Agent 拥有不可遗忘的自我。跨会话记忆 · 持续学习 · 可审计信任(智能论 v3.2)
v1.0.0 · MIT
AI skills大全 mcp ai知识库 Agent 全维度 AI 资源百科,DSH插件 收录大模型、智能 Agent、RAG 检索增强、多模态、MLOps、AI 应用工具、AI面试集、Vibe coding 大全、零基础学习路线,持续更新前沿 AI 开源项目,开发者一站式 AI 导航库
v0.4.2 · MIT
记忆核心(Memory Eternal):自研的 DeepSeek Harness 记忆插件,不移植任何既有记忆框架——对话结束后自动沉淀知识卡到本地 Markdown Vault(自研去重、自研 CJK 检索、可 git 管理),设置页提供图形化知识库(统计 / 搜索 / 知识图谱 + 侧边栏一键弹窗),Agent 通过 memory_recall 工具按需召回历史上下文。零人工干预。
— · no license
Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · DeepSeek Harness · Hermes · VS Code · Windsurf.
— · AGPL-3.0
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
v0.1.14 · MIT
Autonomous (auto) mode permission classifier for DeepSeek Harness: a Claude-Code-auto-mode-like classifier over tools/pre-execute and approval/request, a selectable 'auto' permission preset, LLM semantic judge, git checkpointing, agent discipline guidance