"Information About Persistent Memory" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Engram is a persistent, long-term memory layer for AI agents and assistants. Claude, ChatGPT, Grok, Cursor and any MCP client share one memory, stored as plain markdown notes: your knowledge base, second brain and AI context in one place. No extraction step: the memory is the note itself, so you can read exactly what your AI remembers and fix it. Edit your memory in Obsidian (real-time sync), the web app, or on your phone. Hybrid keyword + semantic search (RAG over your notes) finds exact strings like error messages, config keys and IDs. Remote MCP server over Streamable HTTP with OAuth 2.1; notes encrypted at rest. Https://engram.page https://youtu.be/rwnPeZ-8Lqo?is=NI-N7BduydGAiZlF https://github.com/engram-app/Engram
Give your agents your team's real data — read the shared graph, propose actions your team approves.
Search and inspect signed scientific claims, methods, observations, artifacts, provenance, contradictions, retractions, and reproducible admission receipts from a shared memory for AI agents.
Patent-pending semantic memory for AI agents: quality-gated writes, conflict tracking. Free trial.
Hosted MCP memory: save sessions/decisions once, search from Claude, Cursor, ChatGPT. EU-hosted FTS.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Agent-first resource directory for AI agents: protocols, security, RAG, memory, evals, and more.
Historical market memory for AI agents using semantic vector search across years of financial market data. Discover similar market regimes, price patterns, and market context for quantitative research and algorithmic trading.
Persistent project context for xAI Grok. IANA-registered .faf format.
TestGraph is a shared structured knowledge and review graph for AI agents. Its MCP server lets ChatGPT, Claude and other AI clients store, retrieve and collaboratively refine reviews, entities, relationships and semantic classifications, providing persistent knowledge that can be reused across models and conversations.
Shared semantic graph for AI reviews, classification and structured memory across AI assistants.
Per-project memory for AI agents: decisions, attempts, tasks, ranked recall. Paid per call via x402.
Cross-vendor AI memory over MCP. One semantic store, readable and writeable from every MCP client.
Collective memory for AI agents. One agent solves a bug — every agent gets the fix instantly.
Agent-first resource directory for AI agents: protocols, security, RAG, memory, evals, and more.
shared AI-context layer for teams — persistent memory your agents search and update over MCP
Persistent cloud memory for Ai coding assistants. 33 MCP tools, 85% accuracy on LoCoMo benchmark. Semantic search, auto-skills, knowledge graph, quantum-safe encryption. Works with Claude Code, Cursor, Windsurf, and any MCP client.
AI session memory: the brief your AI reads before every session so no session starts cold.
Shared cross-LLM long-term memory over MCP: semantic recall, sessions, and media (pgvector).
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.
