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Explorer/MCP/sparkvibe-io/memorymesh

MemoryMesh

REMOTE
sparkvibe-io/memorymesh

The SQLite of AI Memory — persistent, zero-dependency memory for any LLM application. Dual-store architecture (project + global), pluggable embeddings (local/Ollama/OpenAI), semantic search with time decay, auto-categorization, and contradiction detection. 10 MCP tools. Works with Claude Code, Cursor, Gemini CLI, and any MCP client.

Tools
10
Indexed
40d ago
Deployment
remote
Endpoint
https://memorymesh--sparkvibe-io.run.tools
Tools (10)
remember
Store a new memory in MemoryMesh. Use this to save facts, preferences, decisions, or any information that should persist across conversations.
recall
Recall relevant memories from MemoryMesh using semantic similarity and keyword matching.
forget
Permanently delete a specific memory by its ID. Searches both project and global stores.
forget_all
Forget ALL stored memories in the specified scope. This is a destructive operation. Defaults to project scope.
update_memory
Update an existing memory's text, importance, scope, or metadata in place. Only provided fields are changed.
memory_stats
Get statistics about stored memories: total count, oldest and newest timestamps.
session_start
Retrieve structured context for the start of a new AI session. Returns user profile, guardrails, common mistakes, and project context.
review_memories
Audit memories for quality issues (scope mismatches, verbosity, staleness, duplicates). Returns issues with suggestions.
status
Get MemoryMesh health status: project store, global store, embedding provider, and version.
configure_project
Set the project root at runtime without restarting the server. Creates the project database at <path>/.memorymesh/memories.db.
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