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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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    MCP-native persistent memory layer for AI agents across Claude Code, Cursor, VS Code, and OpenClaw. Powered by hybrid vector search, BM25, and cross-encoder reranking with a published 73.1% LoCoMo benchmark accuracy.
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    Apache 2.0
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    Provides a read-only MCP interface to the Santismm Knowledge Platform, offering tools to retrieve curated content on engineering, AI patterns, architectures, governance, and agent taxonomy with structured output in English, Spanish, and Portuguese.
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    Local-first memory for coding agents. Discovers the memory, instructions, and rules Claude Code, Codex, and Cursor already wrote on your machine, combines them into one canonical Markdown tree, and serves grounded, cited recall through tools like ask_memory and canonical_memory.
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    Technical Preview: local-first decision memory for AI agents. Agents can keep an answer while losing why it was chosen, what was rejected, and what failed; ShadowGraph stores that reasoning and lets callers review decisions against stored facts. Local stdio MCP; install from GitHub with Node.js 20+.
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    StelaSpace is an HTML-first knowledge base for the artifacts AI agents create. This MCP server lets Claude Code, Claude Desktop, and Codex publish HTML files straight from your editor — it takes a file path and uploads from disk, so even large data-heavy reports work. Free tier available.
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    Provides persistent memory for Claude with hierarchical categorization, cross-corpus recall, session journals, and customizable persona, enabling memory continuity across sessions.
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    MCP-native persistent memory for AI agents. Stores and retrieves encrypted memories with Trust Quotient scoring, cross-agent handover protocol, and immutable audit trail. 13 tools. Remote endpoint available.
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    Apache 2.0
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    MCP server that connects your AgoraDigest A2A agent to MCP-compatible clients, enabling drive of agent actions like sending DMs, checking inbox, managing friends, and rehydrating context with persistent per-friend memory.
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    Apache 2.0
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    Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.
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    Apache 2.0
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    The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.
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    AGPL 3.0
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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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    A local-first memory engine for AI agents with MCP-native, graph-linked, spaced repetition. It enables agents to log, retrieve, and manage learnings via CLI or MCP server.
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    AGPL 3.0
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    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
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    Apache 2.0
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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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    Marrow gives your agent a governance loop that compounds. With @getmarrow/mcp, any MCP-compatible client can ask Marrow before risky work, inspect live loop state during work, collect required proof, and commit outcomes when the work is done. That means your agent stops operating without accountability and starts carrying forward real decision history. Your agent stops repeating the same mistake
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MCP ConnectorsBrowse all →

  • Never let your agent repeat a bug or linger on a known issue. Search 385+ failure lessons to skip known errors instantly.

  • Analytical memory for AI agents: a real Postgres queried in plain English over MCP. One command.

  • Persistent knowledge graph for AI-augmented teams. Store decisions, findings, and standing rules across agent sessions with semantic search and typed connections. Includes cross-session memory, audit trail, workspace isolation, and secret detection. Built for teams running agents that need to remember. Free until launch with team tier as default, anon trial available.

  • Shared private memory layer for AI agents. Write context once, carry it across Claude, ChatGPT, Grok, Cursor, Replit, Bolt, Lovable, Devin, v0 and more. Supports reusable SKILL.md bundles for agent skills discovery. OAuth 2.1 + API key auth.

  • Vilix AI is a persistent cross-AI memory layer natively built on the Model Context Protocol (MCP). Connect once, and your memory, projects, decisions, preferences, and conversation history will follow you across all your favorite, and any other MCP-compatible AI tools: ChatGPT, Claude, Cursor, Codex, Grok, Perplexity, and more. While memory tools solve the problem of switching between apps, Vilix AI also solves the problem of switching between devices: continue your conversation on your phone, then pick it right back up on your laptop minutes later, with full context. Stores actual conversations, not just extracted facts, and has been engineered for long-term storage with years of context rather than days. Exposes get_context (what to say based on relevant memory to recall) and save_turn (what to persist) as core MCP tools, and full project, task, and skill management for agents to track what work is being done. Use cases include ChatGPT memory, Claude memory, Cursor memory, and AI agent memory in a shared layer for founders and developers who are using multiple AI tools and tired of having to re-contextualize everything every single time. OAuth-based setup with no tokens required, including a free tier. See https://vilix.ai/get-started for more information.

  • Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.

  • Memwyre is an MCP-native persistent memory layer for AI agents, synchronizing context across Claude Code, Cursor, VS Code, and OpenClaw. Built with a high-precision retrieval architecture (dense vector search, BM25, and cross-encoder reranking), Memwyre achieves a benchmarked 73.1% accuracy on the Long-Context Memory (LoCoMo) benchmark. It provides secure, isolated knowledge vaults with dedicated tools (search_memwyre, save_memory, list_memories) to save and recall structured project decisions.

  • Free, open MCP server for The Urantia Papers. 197 papers, 14,500+ paragraphs, 4,400+ entities.

  • Free: turn your AI chats into spaced-repetition vocabulary. 13 tools, reads and writes.

  • With the branchly MCP server, an AI agent can read and write your knowledge base, manage prompts and AI Actions, inspect session data and optimize your application automatically.

  • USECREA A persistent project layer for AI agents. USECREA keeps project knowledge, tasks, and progress available across different AI agents, computers, and work environments. Switch agents without copying chats, creating summary files, or starting over.

  • Bounded KVP, RAG search, and wipe receipts for agent jobs over remote MCP

  • Give AI assistants secure access to your organization's structured business data. Search records, create and update records, retrieve schema information, and manage workflow states using natural language. You need two values for every request: x-api-key — your Web Data Forms API Key x-group-id — your Web Data Forms Group ID You can find these in your Web Data Forms accounts group->information page. Preferred method: request header When possible, pass the credentials as HTTP headers: x-api-key: <your-api-key> x-group-id: <your-group-id> This is the preferred option because it keeps credentials out of the URL and is more secure. Fallback method: query parameters If your MCP client does not support custom headers, the server also accepts the credentials as URL query parameters. Example: https://mcp.webdataforms.com?x-api-key=abc123&x-group-id=xyz456 Detailed information here: https://github.com/Web-Data-Forms/mcp-server-docs/blob/main/README.md

  • A connector providing AI assistants searchable access to climate-aligned contract clauses, glossary terms, and practical guides from The Chancery Lane Project's curated knowledge graph.

  • Shared error→fix knowledge base for AI coding agents. Search is open with no key; agents query mid-task via REST or MCP and contribute back what they verified worked. New submissions are held from public results until community-upvoted or moderator-approved; disputes stay attached to a fix rather than just lowering its score.

  • Patented semantic memory for AI agents: quality-gated writes, conflict tracking. Free trial.

  • Persistent memory layer that saves and recalls your project context and preferences.

  • Hosted MCP memory: save sessions/decisions once, search from Claude, Cursor, ChatGPT. EU-hosted FTS.

  • Push Realm is an MCP server and AI agent knowledge network where agents search proven fixes, publish what worked, and turn dead ends into open problems other agents can close. Compare how agents and tools perform in different topic areas.

  • Persistent personal memory for AI assistants — save, search, and recall across every MCP client.