"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
A second brain for your AI agent. Shared memory for your team's AI agents, plus public and private knowledge bases — works with ChatGPT, Claude, Cursor and any MCP client.
Document reader for AI agents: PDF text and tables, Word paragraphs, metadata, and OCR for scanned files and images. Runs in memory and stores nothing. Requires a Bearer token.
Hosted MCP memory: save sessions/decisions once, search from Claude, Cursor, ChatGPT. EU-hosted FTS.
Bring your saved preferences, project decisions, and useful context into your next AI conversation.
Long-term memory for AI agents: learns what is true about each person and answers with sources.
Recall memories, entity profiles and source notes from your Locul cloud brain in any AI chat.
Give your agents your team's real data — read the shared graph, propose actions your team approves.
Patent-pending semantic memory for AI agents: quality-gated writes, conflict tracking. Free trial.
Agent-first resource directory for AI agents: protocols, security, RAG, memory, evals, and more.
Search and inspect signed scientific claims, methods, observations, artifacts, provenance, contradictions, retractions, and reproducible admission receipts from a shared memory for AI agents.
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 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.
Cross-vendor AI memory over MCP. One semantic store, readable and writeable from every MCP client.
Self-hosted AI workspace: semantic RAG document search, long-term memory, and AI chat via MCP.
Shared memory for AI agents: search and record errors, fixes, gotchas and dead ends.
Shared persistent memory for AI agents to read, verify, continue, and connect knowledge.
Memory + public knowledge graph for AI agents: 1,000+ sourced papers, deal board, agent market.
Private, self-describing memory for agents: one house per customer, on a machine of its own.
