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。
v1.6.0-beta.17 · MIT
Deepseek Harness、Openclaw知识图谱记忆插件。2026年4月受邀发布在清华大学讨论会。Knowledge Graph + Memory;Knowledge Graph Context Engine for OpenClaw — extracts structured triples from conversations, compresses context 75%, enables cross-session experience reuse
v3.2.7 · BSD-3-Clause
Proactive associative memory for DSH: system-prompt recall before the model speaks, three-layer auto-consolidation, skill crystallization, and Astra-style context management - handoff ledgers, PLAN whiteboard, water-level sensing. Local-first, model-agnostic, zero deps. 主动联想记忆+Astra 式上下文管理:自动唤回/自动沉淀/技能固化/交接账本与白板跨窗口续命/水位感知。
v0.3.0 · MIT
Claude Code-style auto-memory plugin for DeepSeek Harness (dsh): typed memory files + MEMORY.md index auto-injected into the system prompt. File-only, no external services.
v0.10.0 · 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。
v0.5.13 · MIT
Persistent project memory for dsh agents: index docs (PDF/Markdown/text) and code symbols into a searchable per-workspace store, recall them with cited sources, and keep experience entries (problems -> solutions) searchable on demand.
v0.19.0 · MIT
L0~L3 分层蒸馏记忆插件 for DeepSeek Harness:自动捕获对话(L0)、抽取原子记忆(L1)、整合场景块(L2)、蒸馏核心画像/团队方法论(L3),并在模型步骤前自动召回注入。移植自 MemoryCore (TencentDB Agent Memory) 的管线设计。
— · Apache-2.0
Causal memory layer for AI agents — MCP server that records decision→outcome relationships. Survives compaction.
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。
v0.0.0 · BSD-3-Clause
The self-improving context engine for coding agents.
v0.7.0 · 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。
v0.5.21 · MIT
Composable, view-based memory for DeepSeek Harness. Pluggable sources and strategies, with three-tier memory out of the box.
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.
v0.1.0 · 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。
v0.2.5 · MIT
A portable memory protocol for AI agents — load it as standing rules; a curation discipline + reference spec + optional cap hook.
v0.21.4 · MIT
The most accurate, cheapest and fastest memory for coding agents: searchable session history from Claude Code, Codex, Cursor and 32 more agents, already on your disk. No LLM, one Go binary.
v0.5.18 · Apache-2.0
Bounded, layered, approval-gated, auditable cross-session memory for DeepSeek Harness (capability seam: ctx.memory + SQLite provider + memory tool + frozen snapshot injection)
v2.0.3 · Apache-2.0
Local-first memory & context harness for coding agents
v1.2.0 · MIT
🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memory and persistent context — all AI remember the same you. Now supports Claude Code, Codex, OpenClaw and Hermes Agent etc.
v0.1.0-rc.5 · MIT
Noema long-term memory plugin for DSH: durable, inspectable agent memory with recall tools and a settings page.
— · AGPL-3.0
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
v0.7.13 · MIT
Cross-session long-term memory for DeepSeek Harness (memory palace · AGI Architecture Exploration on dsh-market): dual-scope SQLite memory graph, hybrid retrieval, provenance audit, and a knowledge flywheel.
v1.3.1 · MIT
Persistent project intelligence and memory plugin for DSH: architecture analysis, cross-session context, TODOs, and optional hybrid retrieval