Store important facts, decisions, user preferences, and project context for long-term retrieval. Memories persist across sessions with automatic chunking and duplicate detection.
MIT
482,608 tools. Updated 2026-08-27 20:05
"Methods to store and persist memories for AI systems" matching MCP tools:
- List all AI systems in your workspace with registration status and evidence coverage to identify unregistered systems and gaps before the EU AI Act deadline.MIT
- Retrieve relevant saved memories from past AI conversations using hybrid semantic and keyword matching. Results are ranked by relevance and may include temporary image URLs.MIT
- Store up to 20 memories in a single API call for bulk imports or multi-fact storage. Ideal for populating an AI assistant's memory efficiently.MIT
- List all stored memories for the current user from both Arc and Atlas in the shared store.MIT
- Extract and store important information from conversations to maintain persistent memory across interactions, enabling AI assistants to recall facts and user context.MIT
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- Fix AI mistakes by storing corrections that always appear first. Override previous memories with accurate behavior.MIT
- Store and persist conversation memories for AI agents. Supports episodic, semantic, and procedural memory types with adjustable importance weights.MIT
- Persist key information as memories in a local database for cross-session use. Record preferences, lessons, project context, and relationship notes to be recalled later.MIT
- Retrieve paginated memories from an agent's archival store with search, sorting, and filtering capabilities.MIT
- Persist multiple memory units atomically in one SQLite transaction, reducing overhead when storing several memories at session end.MIT
- Audit memory store health: view totals, categories, stale memories, near-duplicates, and expired references. Read-only.AGPL 3.0
- Store up to 100 independent memories in a single request, ideal for persisting structured data extracted from documents. Use when you have multiple facts to save at once.MIT
- Gather the most reflection-worthy memories by importance and recency, then synthesize and store higher-level insights linked to their source memories.PolyForm Noncommercial 1.0.0
- Store text memories in an agent's archival memory for later retrieval and management within the Letta system.MIT
- Store and search persistent AI memories to recall user preferences, decisions, and facts. Use actions like add, update, and delete to maintain information across sessions.Apache 2.0
- Save, search, and delete project context or decisions across sessions using semantic memory. Store notes or decisions, find them by similarity, and clean up by ID, tags, or type.MIT
- Merge duplicate memories and synthesize concept memories to keep the memory store compact and enhance recall efficiency.Apache 2.0
- Atomically store multiple memories in a single transaction to guarantee all-or-nothing consistency, then rebuild the index once for optimal search performance.Apache 2.0
- Create a new memory to persist important information across sessions. Add tags and trigger phrases so the memory surfaces when relevant topics appear.MIT