global-memory
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Alternatives to global-memory
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Related Servers
- 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.29455 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.4579 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables shared memory, knowledge graph queries, loop and destructive command guards, and a two-pass verification gate across Claude Code, Codex, Antigravity, and Gemini CLI sessions.1MIT
- AlicenseNot gradedqualityAmaintenanceEnables Claude Code to automatically recall and write shared long-term memories stored as plain Markdown, combining BM25, vector, and link-graph hybrid search so relevant notes are injected on every prompt. Memories persist across sessions, projects, and machines, with status tracking for pending and resolved items plus optional end-of-session curation.2MIT
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.