"How to use the terminal" matching MCP servers:

Metis gives AI agents expert know-how that named reviewers have approved, and only where its recorded conditions match the situation. High-risk situations go to a person. When an agent sees practice depart from procedure, it can send the expert one short question. Every retrieval is recorded on a hash-linked evidence chain.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

coldstart gives coding agents durable memory of a specific codebase, with no embeddings, no vector store, and no API key. Agents write notes after real tasks — what a file is for, how a flow spans files, which invariants hold — and those notes are surfaced automatically to a later session whose task matches, so nothing gets rediscovered from scratch. Every note is anchored to real files by content

Records your terminal sessions per command (PTY + OSC 133) into local SQLite, so AI agents can search, retrieve, and diff what commands actually printed. Secret redaction is applied by default to everything served over MCP.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

A self-hostable, open-source, semantically-searchable Agent Skills registry delivered over MCP, with a three-tier progressive disclosure architecture.

Enables coding agents to use an Obsidian vault as long-term memory, with search, reading, and writing of notes, plus automatic capture of learnings that are propagated to MOCs, daily notes, and the knowledge index with git commits.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

A local memory engine any AI tool can use.

SeaMeet MCP connects Claude, Cursor, Codex, and other AI agents to SeaMeet meeting recordings, transcripts, AI summaries, screenshots, action items, webhooks, and desktop recording controls. Use it to search meeting memory, read synced cloud recordings, and automate meeting notes through the Model Context Protocol.

Cross-model AI memory persistence server that gives every AI tool you use access to the same persistent memory.

Self-hosted MCP memory server for personal use and teams

MCP memory server for AI agents that gets better with use

MCP server for intelligent, use-adaptive Obsidian vault search. It uses Hebbian learning and graph centrality to rank files by usage and relevance.

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.

Enables Claude Code with persistent semantic memory, cross-session context, and memory hygiene via a local AD4M executor, offering 13 tools for managing semantic graphs and cross-terminal relay.

Gives Claude direct access to an Obsidian vault, enabling read, write, search, and automatic git sync for cross-device use.

Enables AI agents to use Obsidian vaults as persistent, bidirectional knowledge workspaces with wikilink/backlink resolution, structured frontmatter/tag indexing, task aggregation, and Obsidian Headless Sync.

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