Memory & Context Plugins Memory & Context
75 plugins
Plugins that give your agent long-term memory, knowledge persistence, and context management. From simple key-value stores to full knowledge graphs with vector search.
75 plugins
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Bounded, layered, approval-gated, auditable cross-session memory: a typed `ctx.memory` seam with a zero-dependency SQLite provider, a `memory` tool, and frozen snapshot injection, plus the dsh-memory-protocol v1 rehearsal with an adapter registry and a distributable conformance suite.
dsh plugin --profile default add github:PerryLink/dsh-memento -
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Structured memory engine for DeepSeek Harness. Offline semantic search, entity-attribute-timeline, autoDream self-consolidation, and human-editable Markdown storage.
dsh plugin --profile default add github:modusensus/dsh-mneme#path:/dsh-mneme -
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Model-driven context compression (Active Context Pruning) for DeepSeek Harness: the model decides when and what to compress.
dsh plugin --profile default add github:Tyan66666/billion-context-dsh -
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Automatic conversation distillation: background subagent reflection + skill create/update.
dsh plugin --profile default add github:LoserFox/distill -
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AGI long-term memory base: multi-agent spatiotemporal memory graph, self-evolving knowledge flywheel, self-cognition, and auditable trust guardrails.
dsh plugin --profile default add github:FuRongJun-1999/dsh-memory -
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Cache-friendly three-layer memory for DSH: lean auto injection, per-turn AI consolidation, proactive calendar reminders and warm greetings, plus memory inheritance from other AI tools.
dsh plugin --profile default add github:Aik358/dsh-auto-memory
Frequently Asked QuestionsFAQ
Memory plugins store knowledge that persists across multiple sessions — facts, entities, and conversation summaries. Session plugins manage the current conversation's state: message history, branching, and import/export.
Most memory plugins add 50-200ms per turn for retrieval. Vector-search plugins are slightly slower but much better at finding relevant context. Key-value stores are fastest.
Yes. A common pattern is context-compression for the current session plus a vector-recall plugin for long-term knowledge.