"SQLite - A Lightweight Database Engine" matching MCP servers:

Enables coding agents to query and modify a SQLite database through twelve SQL-based tools, with safety enforced server-side via statement policies, protected tables, result caps, and snapshots. It also provides an append-only, hash-chained memory log with checkpoints so agents can record events and resume context across sessions.

A zero-config MCP server that enables AI to access, analyze, and manage local SQLite databases with secure read-only querying and automatic schema discovery.

Provides comprehensive SQLite database interaction for AI agents, including data manipulation, schema inspection, and automated query logging. It features a unique context preservation pattern that uses a dedicated meta-table to help autonomous agents maintain self-documenting database architectures.

Governed cross-agent memory for coding agents with hybrid retrieval, provenance tracking, and cross-machine sync.

Offline RAG MCP server that indexes documents into SQLite with hybrid vector and lexical search, and enables natural language queries via Claude.

Fireproof ledger database with multi-user sync

Enables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.

Provides persistent memory, skill tracking, failure indexing, and context sharing for Claude Code using SQLite with FTS5 full-text search.

A graph-friendly relational server that stores project memory, tasks, and metadata in a centralized SQLite database as the authoritative source of truth. It enables AI agents to manage complex project states through entity-relationship modeling and can generate human-readable markdown views on demand.

Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.

Graph RAG based context management engine for coding agents

A personal knowledge graph and memory system for AI assistants using SQLite with FTS5 full-text search, enabling persistent memory across conversations with Claude or any MCP-compatible AI.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Persistent memory for AI agents with semantic memory, belief tracking, and dream consolidation, enabling cross-session knowledge retention.

Local-first stdio MCP server for agent relationship-continuity state: PAD mood, affective pulse, trust, decay, and boundary signals kept beside factual memory.

A graph database MCP server that lets AI assistants build, analyze, and visualize relationship graphs with algorithms like PageRank and cycle detection.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Provides a dynamic SQLite-backed code knowledge graph for AI assistants, with MCP tools to inspect symbols, find dependency paths, and analyze code structure across multiple languages.

Enables AI applications to submit claims, decisions, and evidence so it maintains a typed state model of beliefs, conflicts, changes, and next actions, preserving claim lineage and semantic evidence across versioned persistence.

MCP server for querying a personal knowledge base using semantic RAG, powered by DeepSeek and local embeddings from Ollama.