
Memory Fts
- Updated April 11, 2026
- kurovu146/claude-memory-mcp
memory-fts is a MCP server that gives Claude Code SQLite FTS5 long-term memory with BM25-ranked search.
About
io.github.kurovu146/memory-fts is an MCP server that adds long-term memory to Claude Code using a local SQLite database with FTS5 indexing and BM25 relevance ranking. developers shipping with agents install it when chat context is too short for multi-day repos, repeated decisions, or onboarding notes they do not want to re-paste every session. The server exposes memory tools over stdio via the npm package claude-memory-fts; you can point MEMORY_DB_PATH at a custom database file while keeping the default under ~/.claude/memory.db. It fits workflows that prioritize privacy and offline-capable recall over a cloud vector store. Pair it with project-specific skills or docs skills so retrieved snippets stay trustworthy; this server stores and finds what you saved, it does not replace code review or planning artifacts.
- SQLite FTS5 full-text search with BM25 ranking over stored memories
- stdio MCP package claude-memory-fts (npm) v1.0.1 for Claude Code
- Optional MEMORY_DB_PATH (default ~/.claude/memory.db)
- Local-first storage without a hosted memory SaaS requirement
Memory Fts by the numbers
- Data as of Jul 7, 2026 (Skillselion catalog sync)
claude mcp add --env MEMORY_DB_PATH=YOUR_MEMORY_DB_PATH claude-memory-fts -- npx -y claude-memory-ftsAdd your badge
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| Package | claude-memory-fts |
|---|---|
| Transport | STDIO |
| Auth | None |
| Last updated | April 11, 2026 |
| Repository | kurovu146/claude-memory-mcp ↗ |
What it does
Give Claude Code durable, searchable memory across sessions so context survives restarts and long projects.
Who is it for?
Best when you're on Claude Code and want private, searchable session memory without running a separate vector stack.
Skip if: Skip if you need shared cloud memory, multimodal embeddings-only RAG, or memory with no local SQLite footprint.
What you get
After install, the agent can save and retrieve ranked memory hits from a local DB across sessions.
- Searchable long-term memory store backed by SQLite FTS5
- BM25-ranked memory retrieval inside agent workflows
- Configurable local database path for backups and multi-machine sync (manual)
By the numbers
- Registry version 1.0.1
- Default database path ~/.claude/memory.db
- Transport: stdio via npm identifier claude-memory-fts
README.md
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
Recommended MCP Servers
How it compares
Local FTS memory MCP, not a planning skill or hosted vector database product.
FAQ
Who is memory-fts for?
Claude Code and MCP-compatible agent users who want durable, local text memory with keyword-style search.
When should I use memory-fts?
When a project spans many sessions and you need BM25-ranked recall of past notes, prefs, and decisions.
How do I add memory-fts to my agent?
Register the npm stdio server claude-memory-fts in your MCP config and optionally set MEMORY_DB_PATH.