Memory

Injects conclusions already established in the previous reasoning block after each tool result, so the model continues from them instead of re-deriving the same conclusions.

Search the documents and pictures in your Quberi knowledge base, browse its spaces and documents, save notes to it, and upload files into it with your approval for each one, from DeepSeek Harness over OAuth.

Cross-compaction memory for a long session: on compaction it stores what is being dropped, and at question time it injects a few relevant passages (or nothing) so the model can recall what you decided before.

Bridges DSH to a local MemVault store: core memory blocks injected into the system prompt, finished turns extracted through MemVault's own pipeline, and a panel to browse, trace, review and replay what was stored.

即作记忆：创作记忆、设定与知识图谱查询

DSH plugin: a four-entry learning flow in the session input bar — scaffold a preset, collect candidates, merge reviewed changes, and run a read-only workspace audit.

Keeps a project's documentation in step with its decisions on DeepSeek Harness: the same append-only decision log and derived design snapshot, kept fresh in-process instead of by a hook process.

独立本地记忆插件：JSONL 记忆条目、每轮 runtime-context 注入、local_memory_search/local_memory_remember 工具、设置面板管理页。不依赖 Mnemon。

Cross-session long-term memory: captures durable facts from each turn, auto-associates them by shared entity and tag, and recalls with BM25 plus spreading activation over the link graph.

Long-term memory for DeepSeek Harness in Claude Code's format: private, overlay, or shared with Claude Code, with provenance on every file

Reflect2Evolve memory plugin: layered L1/L2/L3 memory, reflection-weighted value backprop, cross-task policy induction, skill crystallization, and three-tier retrieval for OpenClaw, Hermes Agent, and DeepSeek Harness.

Local SQLite long-term memory with CJK-aware search, BM25 and character n-gram cosine reranking, deduplication, and lifecycle management.

Cross-session experience memory for DSH. Lessons are evidence-graded, injected when the turn is about the same thing, and shown just before an action only when the record declares which call it applies to. A rule that holds whatever the turn is about — "always answer in Chinese" — can be marked **standing** and is then carried every turn: measured on 24 isolated real-model turns, a standing rule is followed 6/6 against 0/18 for the controls (empty store, same record unmarked, unrelated standing record). A guess still cannot become always-on: a standing record needs a verified passage like any other. What nobody looks up is retired, reversibly. Five tools, seven slash commands, zero runtime dependencies.

Long-term memory and self-evolving skills for the DeepSeek Harness · DeepSeek Harness 的长期记忆与自我演进能力 —— Plain Markdown on disk, no vector store

Lightweight zero-dependency memory plugin for DeepSeek Harness: Markdown-file memory store, L0 catalog injection, and 5 model-facing tools.

Recalls lessons recorded in earlier projects: when the running work matches one, the local model decides whether to inject a short note before the next step. Requires a local Ollama model (default qwen3.5:9b) and the dsh-mnemon store with its mnemon CLI; without them it only observes and logs, never injects.

Evidence-governed experience memory for DeepSeek Harness

DeepSeek Harness 插件：空闲时自动把本机全部 DSH 会话记录解压、蒸馏、脱敏为个人档案与日程（默认输出 C:\myself）

Memory Fortress for DeepSeek Harness: 5-track layered memory, self-evolution, knowledge graph (SQLite+FTS5), status pill. 本地记忆堡垒：五轨记忆·自我进化·知识图谱·状态灯。

Cross-session long-term memory for the DeepSeek Harness: a two-phase extraction/consolidation pipeline over a file-based memory workspace, recall injection, a citation feedback loop, lease-based job semantics, plus a settings page, a sidebar status panel

Canonical project memory for DeepSeek Harness: one governing article per asset, an append-only journal beneath it.

Standing instructions edited in the DSH settings UI and injected into the system prompt of every subsequent turn.

Persistent project intelligence and memory plugin for DSH: architecture analysis, cross-session context, TODOs, and optional hybrid retrieval

Tiered persistent memory (user/env/project) with per-tier char budgets and semantic query routing; full layers refuse writes instead of silently evicting.