Knowledge Graph Memory

Context7

Official Remote

Discover Context7 MCP, a powerful tool that injects fresh, version-specific code docs and examples from official sources directly into your AI prompts. Say goodbye to outdated or incorrect API info—Context7 ensures your language model answers come with the latest coding references. By simply adding "use context7" to your prompt, you get precise, reliable library documentation and working code snippets without leaving your editor. Designed for smooth integration with many MCP-compatible clients and IDEs, it enhances AI coding assistants with accurate, real-time context that boosts developer productivity and confidence.

Provides a knowledge graph-enhanced retrieval system that combines vector search with graph-based relationships for persistent memory and contextual information retrieval

Provides a structured thought process management system for maintaining explicit reasoning steps, policy verification, and tool output analysis through persistent memory storage

Maintains persistent project context through a structured Memory Bank system of five markdown files that track goals, status, progress, decisions, and patterns with automatic timestamp tracking and workflow guidance for consistent documentation across development sessions.

TypeScript-based server for tracking project context across sessions, enabling persistent knowledge sharing through modular, markdown-based memory management with support for multiple development modes.

Structures reasoning processes through defined thought stages, managing a history of thoughts with metadata for transparent, step-by-step problem solving and decision making.

Basic Memory

Official

Knowledge management system that builds a persistent semantic graph in markdown, locally.

Provides persistent intelligence infrastructure for codebase analysis through hybrid Rust-TypeScript architecture that combines Tree-sitter AST parsing with semantic concept extraction, developer pattern recognition, and SQLite-based persistence to build contextual understanding of codebases over time, learning from developer behavior and architectural decisions.

Provides structured problem-solving tools through Sequential Story and Sequential Thinking approaches, enabling narrative-based or Python-implemented thought sequences with branching and visual formatting capabilities for enhanced memory retention.

Database-backed project memory management system that stores decisions, progress tracking, and system patterns in a queryable knowledge graph with vector embeddings for semantic search and comprehensive import/export functionality.

Basic Memory

Official

Knowledge management system that builds a persistent semantic graph in markdown, locally.

Provides a LibSQL-based persistent memory database for storing and retrieving knowledge graph entities and relations across conversations.

Provides persistent memory capabilities through a SQLite-based knowledge graph that stores entities, observations, and relationships with full-text and semantic search using BGE-M3 embeddings for intelligent context retrieval across conversations.

Provides a knowledge graph-enhanced retrieval system that combines vector search with graph-based relationships for persistent memory and contextual information retrieval

Production-ready semantic memory management server that stores, retrieves, and manages contextual knowledge across sessions using PostgreSQL with pgvector for vector similarity search, featuring intelligent caching, multi-user support, memory relationships, automatic clustering, and background job processing for persistent AI memory and knowledge management systems.