"Kotlin RAG (Retrieval-Augmented Generation) implementation resources" 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
Bounded KVP, RAG search, and wipe receipts for agent jobs over remote MCP
XMemo is a user-owned Memory OS for AI agents, providing a shared, persistent memory layer across AI assistants, IDEs, CLIs, tools, projects, and sessions. It enables ChatGPT, Claude, Codex, Cursor, Gemini, and other supported AI clients to access authorized long-term context without requiring users to repeatedly explain their preferences, project decisions, or previous work. Beyond basic memory storage and retrieval, XMemo supports semantic search, contextual recall, memory updates and corrections, source attribution, version history, project-scoped context, task tracking, and governed memory lifecycle management. Identity-aware access controls, scoped authorization, and memory isolation help users manage which agents and workflows can access their information. XMemo also provides advanced capabilities for structured knowledge, reusable procedures, and memory consolidation through its broader Memory OS platform. Connect through hosted MCP with OAuth or bearer-token authentication, or integrate directly through REST APIs and supported client tools. Memory remains available across authorized clients and sessions, with user-controlled access, export, and deletion. Website: https://xmemo.dev Documentation: https://xmemo.dev/docs
Canonical vocabulary server for autonomous business design. Exposes the Arco Lexicon as seven MCP tools: term lookup, related terms, alignment verification, citation formatting, source retrieval, term listing, and term suggestion. No authentication required. Streamable HTTP transport.
Deterministic contextual decision arbitration and action routing for autonomous software. Takes current state, context, or intent plus caller-supplied candidate actions, state transitions, routes, refusals, escalations, tools, or models and returns a deterministic ordered candidate field. Also provides persistent machine representations for memory, retrieval, indexing, and downstream coherence measurement.
Sovereign intelligence dossiers, daily briefings, RAG search, knowledge graph, codon optimizer.
Agent-first resource directory for AI agents: protocols, security, RAG, memory, evals, and more.
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.
Agent-first resource directory for AI agents: protocols, security, RAG, memory, evals, and more.
A personal RAG database you build from chat, so AI creates work that sounds like you.
Generate 18 AI readiness files (llms.txt, ai.txt, RAG indexes, schema) for any website.
Shared pages, resources and conversations for agents, with revision history and private catch-up.
Knowledge Base von designare.at – Michael Kanda, Web & KI aus Wien. Semantische Suche über RAG.
MCP server exposing Kettle Logic insight articles & industry guidance as tools + resources.
Append-only decisions with provenance, supersession, retrieval, and audited MCP actions.
Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.
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.
Universal memory runtime for AI agents: episodic, semantic, and procedural memory with hybrid retrieval and spaced-repetition decay.
Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG.
NVIDIA NemoClaw knowledge graph — 55 nodes, F1 0.576 (+269% vs RAG), 11x fewer tokens. MCP-native.
