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Long-term memory for Claude Code with SQLite FTS5 search and BM25 ranking
About
Long-term memory for Claude Code with SQLite FTS5 search and BM25 ranking
Security Report
10.0
Low Risk10.0Low RiskValid MCP server (2 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
7 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Custom path to the SQLite database file (default: ~/.claude/memory.db)
Environment variable: MEMORY_DB_PATH
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-kurovu146-memory-fts": {
"env": {
"MEMORY_DB_PATH": "your-memory-db-path-here"
},
"args": [
"-y",
"claude-memory-fts"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
claude-memory-fts
Long-term memory MCP server for Claude Code. Stores facts in a local SQLite database with hybrid search (FTS5 + semantic vector similarity) and automatic context injection.
Features
- Hybrid search — FTS5 keyword search + semantic vector similarity, merged via Reciprocal Rank Fusion (RRF)
- Semantic understanding — find memories by meaning, not just keywords (powered by all-MiniLM-L6-v2 embeddings)
- Auto context injection — top 30 most important memories injected into every prompt via hook
- Importance ranking — facts ranked by access frequency, recency decay, and category weight
- Access tracking — tracks how often each memory is accessed
- Upsert — automatically updates existing facts instead of duplicating
- Categorized — organize by type: preference, decision, technical, project, workflow, personal, general
- MCP Resources — exposes
memory://contextresource for session context - Zero config — works out of the box, stores data in
~/.claude/memory.db
Install
# Add to Claude Code
claude mcp add memory -- npx claude-memory-fts
# Auto-configure context injection hook (recommended)
npx claude-memory-fts --setup-hook
The --setup-hook command automatically:
- Creates
~/.claude/scripts/memory-context.sh - Adds a
UserPromptSubmithook to~/.claude/settings.json - Top 30 memories are injected into every prompt automatically
CLI Commands
| Command | Description |
|---|---|
npx claude-memory-fts | Start MCP server (used by Claude Code) |
npx claude-memory-fts --context | Output top 30 facts (used by hook script) |
npx claude-memory-fts --setup-hook | Auto-configure context injection hook |
Configuration
| Environment Variable | Default | Description |
|---|---|---|
MEMORY_DB_PATH | ~/.claude/memory.db | Path to the SQLite database file |
Example with custom path:
claude mcp add memory -e MEMORY_DB_PATH=/path/to/my/memory.db -- npx claude-memory-fts
Tools
memory_save
Save a fact to long-term memory.
| Parameter | Type | Required | Description |
|---|---|---|---|
fact | string | yes | The information to remember |
category | string | no | One of: preference, decision, personal, technical, project, workflow, general |
memory_search
Hybrid search: runs FTS5 and semantic search in parallel, merges results with RRF. Falls back to LIKE for partial matches.
| Parameter | Type | Required | Description |
|---|---|---|---|
keyword | string | yes | Search keyword or phrase |
limit | number | no | Max results (default: 10) |
memory_update
Update a memory's content or category by ID.
| Parameter | Type | Required | Description |
|---|---|---|---|
id | number | yes | Memory ID |
fact | string | no | New content (omit to keep current) |
category | string | no | New category (omit to keep current) |
memory_list
List all saved memories grouped by category.
| Parameter | Type | Required | Description |
|---|---|---|---|
category | string | no | Filter by category |
limit | number | no | Max results (default: 50) |
memory_delete
Delete a memory by ID.
| Parameter | Type | Required | Description |
|---|---|---|---|
id | number | yes | Memory ID |
Resources
memory://context
MCP resource exposing top 30 facts ranked by importance score:
- Access frequency — frequently accessed facts score higher (capped at 20 points)
- Recency — recently updated facts score higher (10 points, decays over 90 days)
- Category weight — preference/decision (3), workflow/technical (2), project/personal (1), general (0)
How It Works
Search Pipeline
- FTS5 + BM25 and semantic vector similarity run in parallel
- Results are merged and deduplicated using Reciprocal Rank Fusion (k=60)
- Facts appearing in both lists get naturally boosted
- If both return empty, falls back to LIKE substring matching
- Access count is tracked on every search hit
Embeddings
- Model: all-MiniLM-L6-v2 (384 dimensions, ~23MB)
- Generated locally via
@xenova/transformers— no API calls, no data leaves your machine - Embeddings are created on save and backfilled on server startup
- Cosine similarity with 0.3 threshold to filter noise
Storage
- SQLite with WAL mode for fast concurrent reads/writes
- FTS5 virtual table synced via triggers for real-time full-text indexing
- Embeddings stored as BLOB columns alongside facts
Development
git clone https://github.com/kurovu146/claude-memory-mcp.git
cd claude-memory-mcp
npm install
npm run build
npm test
License
MIT
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