"Linux Mint" matching MCP servers:

Enables semantic search over Omarchy, Arch Linux, and Hyprland documentation, allowing users to query configuration and usage information via natural language in Cursor, OpenCode, or Claude Code.

Enables MCP clients such as Codex and Hermes Desktop to search files indexed by a local DigUp installation on macOS and Linux, returning matching passages and file paths.

Enables MCP clients to give AI agents a shared, persistent Graph-RAG memory backed by an Obsidian-native vault, with hybrid search, token-budgeted recall, and memory updates.

Turn any folder or Git repo into safe, token-efficient context for LLMs, AI chats, and coding agents like Claude Code, Codex, and Cursor — local-first GUI, TUI, CLI, and a read-only MCP server with Live Context, agent journal, Smart Ignore, secret/PII redaction, Git scopes, and code compression. Windows, macOS, Linux.

MCP server that serves PostHog JavaScript SDK documentation to coding agents via efficient retrieval, offering tools to query and assemble cited context for tasks like capturing custom events.

Local-first MCP server providing semantic search over library docs, fully offline. Single Go binary speaks MCP over stdio against a vector index pinned to the binary version. Like Context7 with the internet turned off. Apache 2.0. Linux + macOS, also available as a container image.

Enables AI agents to query Linux kernel source code, git history, LKML discussions, Patchwork patches, and documentation with evidence-ranked results, citations, and explicit assumptions or contradictions.

Hebbian learning MCP server with neural memory graphs, eligibility traces, and three-factor reward signals. Associative memory that strengthens through use.

Local long-term memory for AI assistants, written in Rust: hybrid search with reranking over a local Qdrant, a fact store that resolves contradictions, and a hash-chained audit log. Runs fully offline, no API keys.

A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.

Resonance Detection MCP server that proactively finds cross-domain pattern matches between the current problem and past experiences.

Long-term memory for AI agents over MCP — episodic + semantic memory, a temporal knowledge graph, and a dialectic user model, exposed as 32 tools (recall, remember, context, graph, dreaming, peers). Zero dependencies, runs fully offline; leads the LoCoMo benchmark at ~35x fewer LLM calls.

Ingest, query, and generate study materials from documents (PDF, DOCX, Markdown, images, web pages) using vector search, knowledge graph, and study tools through OpenCode chat.

Persistent, auditable memory for AI agents. Hybrid BM25 + vector recall with 18 MCP tools, adaptive block metadata (A-MEM), intent-aware routing, contradiction detection, and governance workflows. Zero external dependencies. Drop-in memory for Claude Code and any MCP-compatible agent.

A persistent memory server that stores and retrieves atomic coding insights like architectural decisions and debugging patterns for AI agents. It enables agents to maintain institutional knowledge across sessions using semantic search and local SQLite storage.

Enables agents to view active beliefs, read owner-selected source text, submit inferred extraction for selected sources, and consult bounded choices from a local personal profile.

Drop-in memory layer for AI coding agents, upgrading them from chat history to a governed Memory OS with hybrid search, contradiction detection, and safe governance.

Persistent memory infrastructure for AI systems, running on Cloudflare's edge network with 28 MCP tools for semantic memory, emotional processing, identity continuity, and a subconscious daemon.

Enables LLM clients to capture, search, and recall personal and family knowledge as structured facts and narrative memories, with every memory linked to its source.

Self-hosted AI memory system for coding assistants, providing project-scoped memory storage and retrieval via MCP tools (add, search, list, delete) to recall prior learnings without re-running discovery.