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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Memory protocol enforcement plugin: bridges the opencode-memory MCP server, forces memory_weave before tool calls, auto-ingests turns, and injects memory context into agent steps.
dsh plugin --profile default add github:baaai123/dsh-memory-protocol -
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PLUR memory rendered into the system prompt on each assembly rather than exposed as a tool call, so the block is replaced instead of appended and context stays flat across a session. Fully local hybrid search (BM25 + BGE fused with RRF), plain YAML storage you can edit, per-workspace scoping, and a /plur-memory browser.
dsh plugin --profile default add github:plur-ai/dsh-plugin -
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Compaction backend replacing LLM summarization with a deterministic semantic extractor (keeps code/paths/commands, drops chatter) plus 28.4x KV-compression accounting.
dsh plugin --profile default add github:ljsysfurryACE/dsh-compaction -
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LLM-driven remember/forget memory: after each turn a model decides what to keep (MemoryDirector), injects relevant memories before each step, dedups, and persists cross-session.
dsh plugin --profile default add github:ljsysfurryACE/dsh-memory-director -
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Cross-session memory for the model: full-text search all past sessions (SQLite FTS5 via ctx.sessionQuery) and bring the strongest matching excerpts back into the current context.
dsh plugin --profile default add github:truelove-dreamer/dsh-plugin-recall -
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Advanced memoria tools: session context injection (profile + recall), recent decisions, health report, namespace allowlist, memory relation graph, and entity search; companion to dsh-memoria.
dsh plugin --profile default add github:jiayan-xu/dsh-memoria-extra
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.