"How to obtain cryptocurrency" matching MCP servers:

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 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.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Gives Claude persistent, local context about how you work, your weekly agenda, and shipped work, with automatic work logging and git integration—all stored privately on your machine.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Persistent, local memory for AI coding agents that learns how you work, not just what you said. Supports Claude Code, Codex CLI, Cursor, and any MCP client.

Provides on-chain identity and memory tools for AI agents, enabling them to register identities and store/retrieve memories on the Emercoin blockchain without holding cryptocurrency.

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.

Serves portable behavioral identity models via MCP. Extracts how a person thinks, communicates, and makes decisions from text, then provides always-on identity context, semantic fact retrieval, keyword search, and provenance tracing through 4 tools and 1 resource.

Local-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

Combines bm25, vector and knowledge graphs to create a comprehensive memory solution. https://github.com/lumetra-io/engram-memory-mcp

An MCP server whose versioned resources can be revised mid-sentence, letting an assistant stop, retract what it just said, and report what changed, who changed it, and how long ago. Every revision is signed, attributable, and hash-chained so the current instruction can be verified and replayed without a key.

MCP server for verified reuse infrastructure that lets agents query before work to obtain safe, permissioned results (exact component, source-free method, or abstention) with receiver-owned verification and non-disclosure guarantees.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Provides read-only access to the Family Office Knowledge Graph, enabling resolution of family offices, retrieval of public records with provenance, and listing of events, aliases, and nodes.

Shared memory, skills and agents for your team — Claude, Cursor and other MCP clients start every session with how your firm actually works. Versioned artifacts with a wiki-link graph, remote Streamable HTTP with OAuth 2.1 sign-in.

Enables Claude to act as a personal assistant in Telegram by providing tools to read new messages, send replies, and manage tasks/reminders/notes in a Supabase-backed memory.