Memoria

Model-authored context pruning for DeepSeek Harness through the official compaction API.

Memoria de traspaso autogestionada por directorio de trabajo y rama de git: registra los turnos, los condensa en Markdown conciso, e inyecta el resultado en sesiones futuras desde ~/.agent/agent-handoff, compatible byte a byte con pi-handoff.

Wiki local en Markdown como memoria a largo plazo para DeepSeek Harness, adaptado de llmwiki

Memoria de personalidad persistente a largo plazo para DeepSeek Harness: almacenamiento en MEMORY.md/USER.md, herramientas memory y memory_search, inyección de contexto por solicitud y detección de secretos. Adaptado en espíritu de pi-hermes-memory.

Un plugin de DeepSeek Harness que convierte la sincronización git de anywhere-claude-mem en un par de DSH: descarga memoria entre máquinas al iniciar la sesión y envía nuevos prompts/observaciones a medida que llegan al worker de claude-mem

Backend de memoria vectorial y de grafo: herramientas observe/remember/search/recall contra un servidor memoria local, que fusiona la recuperación semántica HNSW, la búsqueda por palabras clave FTS5 y señales de grafo de conocimiento mediante ranking RRF, con persistencia automática al final del turno y aislamiento de espacio de nombres por agente.

Puente hacia una base de conocimiento global en Markdown compartida con la CLI kb.cmd de Codex: herramientas kb_add/kb_search/kb_show/kb_timeline con frontmatter compatible byte a byte.

Bóveda de memoria entre sesiones: herramientas remember / recall / forget, inyección de prompt por turno y un navegador de entradas en la página de ajustes.

A super-simplified LLM-wiki memory plugin: one index document (auto-loaded) + one markdown file per topic (read only when needed) — no dumping everything into the context and burning tokens. Simple and lightweight, painless to install/uninstall, and freely editable however you like.

DSH自记忆插件：结构化记录问题、方案与模型调用，支持加权检索、前提感知排序与外部导入。

DeepSeek Harness 分层长期记忆插件：瞬时/情景/语义三层记忆，自动捕获、AI 提炼、跨会话召回与可视化管理。

DeepSeek Harness (DSH) plugin for local-first automatic Wiki memory and native Skill generation.

Markdown folder long-term memory for DeepSeek Harness: one file per fact, an index line per memory, mounted into every request as a system prompt section

DeepSeek Harness (DSH) plugin — inject context when user input or model output hits configured keywords, with toggleable/configurable rules and model-editable memory.

Industrial-grade 4-tier cognitive memory, red-light guard, and vector semantic engine for DeepSeek Harness (dsh).

Import chat history and memory from other AI agents (Claude Code, Codex CLI, Gemini CLI, Aider, Cline, OpenCode, Hermes Agent, OpenClaw) into DeepSeek Harness

DeepSeek Harness community Bundle for local, evidence-backed Agent Memory

File-based self-evolving memory for DeepSeek Harness v2: compression & extraction over official compaction, daily episodic layer, dream consolidation to semantic files (user/agent/memory/dream + project), catalog disclosure injection, no confidence machin

Notion connector for DeepSeek Harness: search, read, query, create, update, and append Notion pages and databases via six agent tools, with a Web settings page — configured once with an Integration Token.

Write notes and memos to flomo (浮墨笔记) from any agent via the flomo_send tool, configured once with the flomo API URL or API Key.

Auto-supplement QMD semantic search when the model greps/globs a knowledge-base directory: zero-dependency DSH plugin, async injection via next-step, no manual invocation.

DSH ↔ agentmemory session-memory bridge: mirrors session lifecycle into a local agentmemory daemon (REST), exposes memory_recall / memory_remember tools, and injects a per-session memory context window via agent/pre-step.

Tracks the agent's state as Yogācāra mental factors over a durable seed store, and feeds it back into the prompt as first-person inclination.

Bi-temporal memory backed by a SodaMem daemon: recall is contributed to the prompt while each turn is assembled and every closed turn is ingested, with no tool call and no model call on the read path; each recalled fact cites the turn it came from and carries validity dates, so superseded facts stop being retrieved.