Save, search, and manage long-term memories across conversations. Set preferences, facts, and skills; recall them later by topic.
MIT
197,914 tools. Last updated 2026-06-13 00:58
"Techniques and Strategies to Improve Long-Term Memory" matching MCP tools:
- Retrieve historical TAIEX index data for analyzing long-term market trends and patterns in Taiwan's stock market.PythonMIT
- Assess cryptocurrency market conditions using a dual-score system that measures long-term trend strength and short-term momentum to determine market regime and sentiment for trading decisions.MIT
- Trace the origin and creation history of any long-term memory chunk to verify its reliability and understand why it exists.Apache 2.0
- Deprecate a long-term memory chunk to flag it as outdated or superseded, removing it from recall results. Optionally link to a replacement chunk for traceability.Apache 2.0
- Distill conversation sessions into structured knowledge and persist to long-term memory. Use when a session ends or context grows large.MIT
Matching MCP Servers
- Alicense-qualityDmaintenanceA persistent, human-like memory system for AI companions in LM Studio, using SQLite and ChromaDB for hybrid storage, enabling seamless recall across sessions and models.Last updated29MIT
- Alicense-qualityBmaintenanceProvides persistent long-term memory for AI agents with semantic search and activation-based decay. Enables AI systems to remember across sessions through layered memory architecture and automatic context-aware retrieval.Last updated65MIT
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- Analyze Bitcoin's market cycle position using eight key indicators including MVRV Z-Score, NUPL, and Puell Multiple to inform long-term positioning and trading strategies.MIT
- Initialize a new session to restore identity, workflow context, and recent status from long-term memory. Runs a health snapshot and heartbeat sign-in for continuity.Apache 2.0
- Provides onboarding guidance for Memento's long-term memory system, including retrieval workflows, storage protocols, and best practices for cross-session knowledge management.MIT
- Run diagnostics on the memory system to identify expiring short-term memories, timeline gaps, and conflicting long-term chunks for memory hygiene maintenance.Apache 2.0
- Analyze decisions or strategies to detect where short-term gains create long-term fragility. Uses a 6-phase protocol to assess alignment gaps across 9 dimensions at interpersonal, organizational, or systemic scales.
- Store important information such as decisions, preferences, and facts in long-term vector memory. Automatically deduplicates and filters noise for reliable recall across sessions.MIT
- Store user-specific memories like preferences and identity for persistence across sessions. Use for long-term facts.MIT
- Convert ephemeral insights into permanent long-term memory; automatically chunks text, generates embeddings, and stores for semantic recall.Apache 2.0
- Creates or updates a node in a persistent graph memory for long-term RAG retrieval. Requires initialized NFT matrix; if missing, purchase license key first.Business Source 1.1
- Record interaction events like messages or tool calls to store them in the agent's long-term memory for future synthesis.Apache 2.0
- Store new information in short-term memory with temporal decay, where frequently accessed content gets promoted to long-term storage automatically.AGPL 3.0
- Retrieve established patterns, past decisions, and documented workflows from long-term memory using semantic search. Describe the concept you need in a natural language query, and the tool finds relevant knowledge chunks.Apache 2.0
- Move high-value or frequently used memories to permanent long-term storage like Obsidian vaults, with options for automatic detection and preview mode.AGPL 3.0
- Save facts, decisions, preferences, or lessons to long-term memory. Automatically create embeddings for future semantic search.MIT