LibSQL Memory▌
by spences10
LibSQL Memory offers a persistent memory database using LibSQL to store and retrieve knowledge graph entities and relati
Provides a LibSQL-based persistent memory database for storing and retrieving knowledge graph entities and relations across conversations.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / AI assistants that need long-term memory
- / Building persistent knowledge bases from conversations
- / LLM applications requiring context continuity
- / Knowledge graph management for AI systems
capabilities
- / Store entities and relationships in knowledge graphs
- / Search stored knowledge with fuzzy text matching
- / Persist memory across conversation sessions
- / Connect to local SQLite or remote LibSQL databases
- / Rank search results by relevance
- / Manage knowledge graph relationships
what it does
Provides a persistent LibSQL database for storing knowledge graph entities and relationships that persist across AI conversations. Enables AI assistants to remember and build upon previous interactions.
about
LibSQL Memory is a community-built MCP server published by spences10 that provides AI assistants with tools and capabilities via the Model Context Protocol. LibSQL Memory offers a persistent memory database using LibSQL to store and retrieve knowledge graph entities and relati It is categorized under ai ml, databases.
how to install
You can install LibSQL Memory in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
LibSQL Memory is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
mcp-memory-libsql
A high-performance, persistent memory system for the Model Context Protocol (MCP) powered by libSQL with optimized text search for LLM context efficiency.
<a href="https://glama.ai/mcp/servers/22lg4lq768"> <img width="380" height="200" src="https://glama.ai/mcp/servers/22lg4lq768/badge" alt="Glama badge" /> </a>Features
- 🚀 High-performance text search with relevance ranking
- 💾 Persistent storage of entities and relations
- 🔍 Flexible text search with fuzzy matching
- 🎯 Context-optimized for LLM efficiency
- 🔄 Knowledge graph management
- 🌐 Compatible with local and remote libSQL databases
- 🔒 Secure token-based authentication for remote databases
Configuration
This server is designed to be used as part of an MCP configuration. Here are examples for different environments:
Cline Configuration
Add this to your Cline MCP settings:
{
"mcpServers": {
"mcp-memory-libsql": {
"command": "npx",
"args": ["-y", "mcp-memory-libsql"],
"env": {
"LIBSQL_URL": "file:/path/to/your/database.db"
}
}
}
}
Claude Desktop with WSL Configuration
For a detailed guide on setting up this server with Claude Desktop in WSL, see Getting MCP Server Working with Claude Desktop in WSL.
Add this to your Claude Desktop configuration for WSL environments:
{
"mcpServers": {
"mcp-memory-libsql": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"source ~/.nvm/nvm.sh && LIBSQL_URL=file:/path/to/database.db /home/username/.nvm/versions/node/v20.12.1/bin/npx mcp-memory-libsql"
]
}
}
}
Database Configuration
The server supports both local SQLite and remote libSQL databases through the LIBSQL_URL environment variable:
For local SQLite databases:
{
"env": {
"LIBSQL_URL": "file:/path/to/database.db"
}
}
For remote libSQL databases (e.g., Turso):
{
"env": {
"LIBSQL_URL": "libsql://your-database.turso.io",
"LIBSQL_AUTH_TOKEN": "your-auth-token"
}
}
Note: When using WSL, ensure the database path uses the Linux
filesystem format (e.g., /home/username/...) rather than Windows
format.
By default, if no URL is provided, it will use file:/memory-tool.db
in the current directory.
API
The server implements the standard MCP memory interface with optimized text search:
- Entity Management
- Create/Update entities with observations
- Delete entities
- Search entities by text with relevance ranking
- Explore entity relationships
- Relation Management
- Create relations between entities
- Delete relations
- Query related entities
Architecture
The server uses a libSQL database with the following schema:
- Entities table: Stores entity information with timestamps
- Observations table: Stores entity observations
- Relations table: Stores relationships between entities
- Text search with relevance ranking (name > type > observation)
Development
Publishing
Due to npm 2FA requirements, publishing needs to be done manually:
- Create a changeset (documents your changes):
pnpm changeset
- Version the package (updates version and CHANGELOG):
pnpm changeset version
- Publish to npm (will prompt for 2FA code):
pnpm release
Contributing
Contributions are welcome! Please read our contributing guidelines before submitting pull requests.
License
MIT License - see the LICENSE file for details.
Acknowledgments
- Built on the Model Context Protocol
- Powered by libSQL
FAQ
- What is the LibSQL Memory MCP server?
- LibSQL Memory is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for LibSQL Memory?
- This profile displays 37 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Direct Database Queries from AI
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Data Analysis & Reporting
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Schema Exploration
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Data Validation & Quality Checks
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor with MCP support
- ›Database credentials (read-only recommended for safety)
- ›Network access from Claude client to database
- ›Understanding of database security and access control
Time Estimate
15-30 minutes including configuration and testing
Installation Steps
- 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 8.Document query patterns for team use
Troubleshooting
- ⚠Connection refused: Check database is running and network accessible
- ⚠Authentication failed: Verify credentials, check user permissions
- ⚠Claude can't see tables: Grant appropriate read permissions to database user
- ⚠Slow queries: Add indexes, limit result set size, use read replicas
- ⚠MCP server not loading: Check config syntax, restart Claude Desktop
Best Practices▌
✓ Do
- +Use read-only database credentials to prevent accidental writes
- +Connect to read replica, not production primary database
- +Set query timeout limits to prevent long-running queries
- +Document database schema and common queries for AI context
- +Monitor query performance and optimize slow queries
- +Use connection pooling for better performance
- +Test with non-production data first
✗ Don't
- −Don't use production write credentials—risk of data corruption
- −Don't query production database during peak traffic hours
- −Don't expose sensitive PII without proper access controls
- −Don't skip query result validation—AI can misinterpret schema
- −Don't allow unlimited result set sizes—set LIMIT clauses
- −Don't share database credentials in plain text config files
💡 Pro Tips
- ★Create database views for common queries to simplify AI access
- ★Add schema comments/descriptions so AI understands column meanings
- ★Use semantic table/column names ('customer_lifetime_value' not 'clv')
- ★Set up query logging to audit what Claude is querying
- ★Create saved query templates for recurring analysis
- ★Combine with data visualization tools for better insights
Technical Details▌
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
- Model Context Protocol (MCP)
- Database-specific protocols (PostgreSQL, MySQL, MongoDB)
Compatibility
- PostgreSQL
- MySQL
- SQLite
- MongoDB
- Redis
When to Use This▌
✓ Use When
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid When
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.
Integration▌
- →Read replica connection for analytics queries
- →Database view layer to abstract complex joins
- →Query result caching for repeated questions
- →Audit logging of all AI-generated queries
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★37 reviews- ★★★★★Hana Rao· Dec 24, 2024
We wired LibSQL Memory into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Carlos Abebe· Dec 24, 2024
According to our notes, LibSQL Memory benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Olivia Robinson· Dec 20, 2024
LibSQL Memory reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Yusuf White· Dec 20, 2024
I recommend LibSQL Memory for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Ganesh Mohane· Dec 12, 2024
Useful MCP listing: LibSQL Memory is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Ren Robinson· Nov 15, 2024
LibSQL Memory has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Yash Thakker· Nov 11, 2024
LibSQL Memory reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Aarav Shah· Nov 11, 2024
Strong directory entry: LibSQL Memory surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Sakshi Patil· Nov 3, 2024
We evaluated LibSQL Memory against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Oct 22, 2024
I recommend LibSQL Memory for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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