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Knowledge & Memory

Persistent memory storage using knowledge graph structures. Enables AI models to maintain and query structured information across sessions.

MCP ServersBrowse all →

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    An agent-neutral MCP server providing SQLite key/value storage, Postgres knowledge base, and Kart task queue functionality. Features SAP/1.0 authorization on every tool call for secure multi-application access.
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    An MCP server that gives AI agents persistent, narrative memory: concepts as nodes, relationships as typed "because" edges, so anything filed can be pulled back by association rather than by address.
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    Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.
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    Shared research cache for AI agents. Caches web research across sessions and users - hit means instant answer from verified sources, miss means your research saves the next dev's tokens. Semantic search with freshness tracking, gap detection, and real-time token measurement via JSONL. Free, open source.
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    AGPL 3.0
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    ContextLattice is an HTTP-first, MCP-compatible memory/context/task orchestrator that persists writes and returns fused recall from specialized stores with local-first defaults. Primary URL: https://contextlattice.io/ Install: https://contextlattice.io/installation.html Troubleshooting: https://contextlattice.io/troubleshooting.html
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    Mason indexes your codebase into a persistent concept map linking features and flows to their implementing files, so AI agents can answer "where is X implemented" without running grep/glob. It also provides pre-edit impact analysis and generates CLAUDE.md files from structured analysis of git history, architectural file sampling, and test mappings.
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    Strata is a native MCP server that gives agents verified intelligence across 22 AI ecosystems. Tools: * get_best_practices * get_latest_news * get_top_integrations * search_ecosystem * find_mcp_servers * Claude-validated content, injection-safe by design * Refreshed twice daily via automated pipeline * Works with Claude Code, Cursor, Windsurf, Cline * Free tier available Site: https://usestrat
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    Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp
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    Memento is a local-first MCP server that gives AI coding agents durable project memory — facts, decisions, patterns, and architecture notes — so they stop re-learning the same context every session. Runs locally on Node.js 18+ with SQLite storage and optional cloud embeddings; works with Claude Code, Cursor, Windsurf, and any MCP client.
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    Headless semantic MCP server for Obsidian, Logseq, Dendron, Foam, and any markdown folder. Features built-in hybrid semantic search, surgical AST editing, template scaffolding, zero-config local embeddings, and workflow tracking.
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    Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.
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    MIT
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    Persistent AI memory server with 3-layer hybrid search (vector + FTS5 + keyword), confidence scoring via Reciprocal Rank Fusion, episodic/profile memory, and 16 tools. Zero LLM dependency. Works standalone with Claude Desktop and Claude Code. MIT licensed.
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    A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.
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    With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding, keeping context across sessions without any SDK integration. With MCP, it can: Store stable user facts and preferences after answering using the advanced_augmentation tool Recall relevant memories before answering using the recall tool Maintain context across sessions us
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