54 results
为 DeepSeek Harness 带来分层记忆（全局 / 用户 / 项目 / GIT 分支 / 每日）与自我进化（经验沉淀 + 技能自动创建）和技能管理、待办管理、CLI 调度（kimi/codex/grok/hermes 等外部 AI 统一调度）、临时信息便签，带 WebUI 管理界面。Hermes-style long-term memory, self-evolution, skills, todos and external CLI agent dispatch for DeepSeek Harness.
Local-first persistent memory infrastructure for DeepSeek Harness: bounded hot-memory bootstrap, explainable cold recall (exact + Chinese BM25), lease-lock transactional writes, read-only governance, and trajectory review. DSH plugin host; Python stdlib + Markdown core. Zero database / vector service / external service dependencies.
Cross-session memory plugin for DSH — auto-extraction, semantic recall, scheduled maintenance, LLM-powered consolidation, context explosion prevention, global Soul identity
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。
Long-term memory system for DeepSeek Harness: two-phase extraction/consolidation pipeline over session logs, with summary injection and memory tools
Cross-session plaintext memory plugin for the DeepSeek Harness — deterministic BM25 recall, human-owned, no embeddings
Memory protocol enforcement plugin for DeepSeek Harness — forces memory_weave before tool calls, auto-ingests session turns, injects memory context into agent steps. Bridges the opencode-memory MCP server with a hard protocol gate.
Generic stateless dialogue compression that mimics human memory — forgetting-curve bounded context for LLM conversations, with a ready-to-use adapter module for DeepSeek Harness (DSH).
把 WorkBuddy 的文件式记忆系统移植进 DeepSeek Harness：用户级 + 工作区的人类可读 Markdown 记忆（~/.deepseek-harness/MEMORY.md 与 <cwd>/.deepseek-harness/memory/）。
Cross-session persistent memory for the DeepSeek Harness: a model-facing memory tool, an always-on index section, and a bundled maintenance skill.
Retrieved ≠ injected: CBDC-gated memory for DeepSeek Harness — decides how memory is USED (use/verify/ignore decisions, feedback learning, full audit); local SQLite + FTS5, bounded, no extra model call
DSH 跨会话记忆插件 | Cross-session memory plugin: YAML 存储、embedding 语义检索、标题优先注入、两级自动注入
LMA (Layered Memory Architecture) 是一个为 DeepSeek Harness 设计的地基插件（Foundation Plugin）。它定义了 “记忆体（Memory Body）” 这一抽象概念及其挂载/卸载协议。 它与其他记忆插件的本质区别在于：LMA 是一个容器和载体，让 dsh-memory-evolve、EchoCore 等“能力性插件”可以挂载其上，它本身也内置了一套可开关的基础记忆流程，作为可选能力。LMA结束记忆功能的“一锅乱炖”，实现跨会话的结构化记忆机制。(目前功能还在持续完善以及测试，对于其他插件的接口在构想完整后逐步推动)
Local, storage-bounded cross-session search for DeepSeek Harness conversations with SQLite FTS5, Chinese segmentation, and message-level jump targeting
给 DeepSeek Harness 补上时间轴记忆：定宽只追加的事实日志 + agent 自己维护的摘要金字塔，固定阅读预算下 recall 成本不随记忆量增长。
记忆搬家：把 Claude / ChatGPT / Claude Code 的历史记忆搬进 DeepSeek Harness —— 带逐字证据，逐条批准 | Move your Claude & ChatGPT memory into DeepSeek Harness (DSH), with verbatim evidence and one-by-one approval
dsh-memory — cited memory over DSH's lossless session log: post-session distillation into human-auditable markdown files, a compact recall index, memory_read/memory_expand tools, and a dsh-memory CLI
MemoryHub (mh) integration for DeepSeek Harness: loads .memoryhub checkpoint memory on session start, saves dsh sessions back into checkpoints, and registers the mh workflow skill and tools
Solve the AI goldfish brain: a personal memory layer for DeepSeek Harness — preferences, project conventions, workflows, and error lessons, stored locally as auditable, evidence-backed Markdown/JSON. 解决 AI 金鱼脑：偏好、项目约定、工作方式与纠错教训的本地可审计记忆层。
dsh自动进化记忆系统
Non-invasive SQLite hybrid memory and knowledge-graph plugin for DeepSeek Harness
DSH 记忆管理插件（宿主级，标准化重写版）：文件夹记忆库、印象、组合与双向链接、会话级注入计划与消息注入、规约记忆与最近会话总结的新会话自动注入、会话总结、跨工作区会话列表、6 个 Agent 记忆工具、左侧图谱双视图浮层与消息跳转、设置页开关。只依赖 DSH 核心扩展点（settings/tools/agent 事件/webServer），功能实现自包含，官方升级影响面最小。
Long-term memory for DeepSeek Harness: memory_save/recall/list/delete/clear tools plus a bilingual sidebar panel visualizing what the agent remembers about you
Explicitly governed shared memory spaces for DeepSeek Harness sessions