global-memory
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Alternatives to global-memory
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- AlicenseNot gradedqualityAmaintenanceSelf-learning memory for AI tools. Remembers user preferences and context across Claude, Cursor, and Codex with multi-parameter forgetting and cross-tool identity.14MIT
- -licenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.-

pūrmemoofficial
AlicenseAqualityAmaintenanceProvides persistent memory for AI assistants like Claude, enabling them to remember user identity, projects, and conversations across sessions and platforms via natural language commands.29408 npm2MIT- AlicenseAqualityBmaintenanceEnables AI assistants to retain persistent, searchable memory and versioned vaults, spawn and recall named agents with state and audit history, run automations, monitors, and deep research, and execute on-chain and web tasks across sessions. It can be installed alongside Claude Code, Cursor, Windsurf, Zed, Hermes, and other MCP clients, with vault and memory optionally run entirely on the user's machine.4521 npmMIT

Memcellofficial
AlicenseNot gradedqualityCmaintenanceLiving memory for AI coding agents (Claude Code, Cursor, Copilot, Codex). Cross-vendor persistent memory, decision recall, and outcome calibration via MCP and hooks.780 npmApache 2.0- FlicenseBqualityDmaintenanceProvides reusable AI assistant behavior rules as prompts and automatic git repository context (branch, commits, diffstat) to eliminate repetitive explanations across sessions and clients.1-
TDQS
Scored across 9 tools
Most tools have clearly distinct roles: recall/record_usage, list/overview, and start_consolidation/apply_consolidation are well separated. The only potential confusion is between update_memory and apply_consolidation (both can modify presets) and between delete_memory and apply_consolidation's delete, but the usage context (direct user action vs. consolidation workflow) disambiguates them.
Eight of nine tools follow a consistent verb_noun pattern (record_usage, list_memories, update_memory, start_consolidation, apply_consolidation, recall_presets, save_requirement, delete_memory). memory_overview breaks the pattern by being noun_noun, but it is still readable and clearly the odd one out.
Nine tools is a well-scoped size for a memory/preset management server. Each tool covers a distinct operation or workflow stage—CRUD, recall, usage reporting, and consolidation—without redundancy or bloat.
The tool surface covers the full lifecycle: save_requirement creates presets, list_memories/memory_overview/recall_presets read them, update_memory plus apply_consolidation handle updates/disable/archive, and delete_memory covers deletion. The two-phase consolidation workflow and record_usage for outcome tracking round out the domain with no obvious gaps.