mcp-memory-libsql and memora
Both tools are MCP servers for semantic storage and knowledge graphs, making them competitors as alternative implementations for providing persistent memory to AI agents.
About mcp-memory-libsql
spences10/mcp-memory-libsql
🧠 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.
Implements relevance-ranked text search with fuzzy matching across entities, observations, and relations using libSQL's full-text capabilities, optimized to minimize token consumption in LLM prompts. Supports both local SQLite and remote Turso databases via environment configuration, with token-based authentication for remote access. Exposes standard MCP memory operations (create/update/delete entities and relations, relationship exploration) through a text-search interface designed for AI agent knowledge persistence.
About memora
agentic-box/memora
Give your AI agents persistent memory — MCP server for semantic storage, knowledge graphs, and cross-session context
Implements a Model Context Protocol (MCP) server with pluggable embedding backends (OpenAI, sentence-transformers, TF-IDF) and multi-tiered storage—local SQLite, Cloudflare D1, or S3/R2 with optional encryption and compression. Features include interactive knowledge graph visualization, RAG-powered chat with streaming LLM tool calling, event notifications for inter-agent communication, and automated memory deduplication via LLM comparison. Integrates with Claude Code and Codex CLI through stdio or HTTP transports.
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