MCP Memory Suite

A comprehensive memory plugin for AI coding assistants and personal knowledge management, built on the Model Context Protocol (MCP). It gives AI assistants (Cursor, Claude, and any MCP-compatible client) persistent, intelligent memory that grows smarter with use -- combining neuroscience-inspired retrieval, Hebbian learning, and governance with a full suite of personal data management modules including habits, experiences, knowledge, accounts, and lifestyle tracking.

The system is organized into two major subsystems:

1. Core AI Memory System -- 9 MCP tools for storing, searching, and governing memories that help AI coding assistants recall project context across sessions. Uses a three-layer retrieval pipeline (L1 vector + BM25, L2 PageRank spreading activation, L3 context packing), three-factor reinforcement weights (retrieval frequency, adoption rate, feedback), Hebbian learning, Ebbinghaus decay, and automated codebase bootstrap scanning.

2. Personal Memory Modules -- 15 MCP tools for managing personal data: encrypted account vault, habit tracking with streaks, experience recording with emotion journeys, knowledge management with SM-2 spaced repetition, lifestyle information with PII masking, and a unified cross-module manager.

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Table of Contents

• Features Overview
  • Core AI Memory System
  • Personal Memory Modules
• Installation
• Configuration
• Usage -- MCP Server Setup
• MCP Tools Reference
  • Core Memory Tools (9)
  • Personal Memory Tools (15)
• Security
• Development
• Project Structure
• License

---

Features Overview

Core AI Memory System

The core system provides long-term, structured memory for AI coding assistants. Instead of storing raw dialogue, it extracts concise facts (30--50 tokens) with rich metadata that drive intelligent retrieval and lifecycle management.

Feature	Description
Three-Layer Retrieval Pipeline	L1: parallel vector (ChromaDB) + BM25 recall with fusion scoring. L2: Personalized PageRank spreading activation across the Hebbian connection graph. L3: three-factor weighted scoring with Ebbinghaus temporal decay and token-budget packing.
Three-Factor Reinforcement Weights	W = 1 + w_retrieval * f_retrieval + w_adoption * adoption_rate + w_feedback * f_feedback. Retrieval frequency (Hebbian), adoption rate (dopamine), and time-decayed feedback (neuromodulator) combine to amplify or attenuate each memory's influence.
Hebbian Learning	Memories that are co-activated during retrieval have their connection weights strengthened. Connections decay passively over time; weak edges below a threshold are pruned.
Memory Lifecycle	Episodic memories are compressed into semantic summaries after a configurable period. Low-weight memories enter a forget queue. Old memories are archived to cold storage.
Governance Engine	Automated audits detect redundant memories (high embedding similarity), contradictory memories (conflicting keyword pairs), and weak graph edges. A composite health score (0--100) summarizes store quality across five dimensions.
Codebase Bootstrap	On first project connection, a scanner extracts initial memories from config files (tech stack, build tools, directory structure, entry points) to provide immediate value before conversational memories accumulate.
Write-Time Deduplication	When a new memory's cosine similarity to an existing one exceeds the threshold, they are automatically merged (content concatenated, tags unioned, importance maximized, access count incremented).
LRU Hot Cache	An L1 hot cache with configurable TTL sits in front of the retrieval pipeline for frequently accessed memories.
Project Isolation	Memories are scoped by project_id, enabling multi-project workflows without cross-contamination.

Personal Memory Modules

Six interconnected modules for personal data management, each with its own SQLite store and privacy controls:

Module	Description
Account Vault	Encrypted credential storage with Argon2id key derivation and Fernet (AES-128-CBC + HMAC-SHA256) encryption. Supports 11 account categories, password history, security questions, API keys, recovery codes, 2FA flags, password expiry tracking, and strength evaluation. Auto-mode (passwordless) for local use; upgradeable to password mode. All display output is masked.
Habit Tracking	6 frequency types (daily, weekly, Mon--Fri, specific days, every-N-days, monthly), grace days, automatic streak computation (current + longest), completion rates, mood ratings with notes, categories, and favorites.
Experience Recording	16 experience categories, 7 sentiment levels, emotion journey tracking (chronological emotion data points), growth outcomes (reframing failures as learning), follow-up reflections with sentiment shift, milestone classification for narrative identity, and guided reflection prompts.
Knowledge Management	SM-2 spaced repetition algorithm with ease factors and intervals. 13 knowledge types, 6 mastery levels (Bloom's taxonomy), source provenance, application logging, knowledge graph (related/prerequisite/derived cards), confidence scores, and automatic decay risk calculation.
Lifestyle Information	Preferences with evolution tracking (strength changes over time), daily/weekly routines with energy and mood tracking, contacts with relationship dynamics, and addresses with emotional associations. PII masking on all sensitive fields.
PersonalMemoryManager	Unified facade with cross-module linking (5 link types: related, supports, inspired_by, contradicts, evolved_into), unified search across all modules, aggregate statistics, and due-item aggregation (overdue habits, due reviews, high decay risk).

---

Installation

Prerequisites

• Python 3.10 or later
• pip

Install from source

git clone https://github.com/sscctv/mcp-memory-suite.git
cd mcp-memory-suite
pip install .

For local embedding model support (offline vector embeddings via sentence-transformers):

pip install ".[local-embedding]"

For development dependencies:

pip install ".[dev]"

Dependencies

Package	Purpose
numpy	Numerical operations for embeddings and similarity computation
chromadb	Vector database for dense retrieval
rank-bm25	BM25 keyword search for sparse retrieval
scipy	Scientific computing support
mcp	Model Context Protocol server library
cryptography	Argon2id KDF and Fernet encryption (personal memory vault)
pyyaml	Configuration file parsing
sentence-transformers (optional)	Local embedding model (all-MiniLM-L6-v2)

After installation, the ai-memory-mcp command is available on your PATH.

---

Configuration

The server reads config.yaml from the current directory, the project root, or ~/.ai-memory/config.yaml. If no file is found, built-in defaults are used.

Full config.yaml Reference

# ── Storage ──────────────────────────────────────────────
storage:
  sqlite_path: "~/.ai-memory/memory.db"   # SQLite database for core memories
  chroma_path: "~/.ai-memory/chroma"      # ChromaDB directory for vector storage
  wal_mode: true                           # SQLite WAL mode for concurrent reads

# ── L1 Hot Cache ─────────────────────────────────────────
cache:
  max_size: 100            # LRU cache capacity (number of entries)
  ttl_seconds: 3600        # Cache entry TTL (1 hour)

# ── Embedding ────────────────────────────────────────────
embedding:
  api:                                      # Primary: OpenAI API (higher quality)
    enabled: false
    model: "text-embedding-3-small"
    base_url: "https://api.openai.com/v1"
    api_key_env: "OPENAI_API_KEY"           # Reads from environment variable
    dimension: 1536
  local:                                    # Fallback: local model (offline)
    model: "all-MiniLM-L6-v2"
    dimension: 384
    cache_dir: "~/.ai-memory/models"
  hash_fallback:                            # Last resort: hash-based embedding
    dimension: 256

# ── Three-Factor Reinforcement Weights ───────────────────
weights:
  w_retrieval: 0.3              # Call frequency weight (Hebbian)
  w_adoption: 0.4               # Adoption rate weight (Dopamine)
  w_feedback: 0.3               # Feedback score weight (Modulator)
  max_access_count: 100         # Log compression denominator
  feedback_half_life_days: 14   # Feedback decay half-life

# ── Decay ────────────────────────────────────────────────
decay:
  confidence_lambda: 0.03       # Ebbinghaus decay rate (~23-day half-life)
  hebbian_decay: 0.99           # Hebbian connection decay per update
  forget_threshold: 0.1         # Weight below this enters forget queue

# ── Retrieval ────────────────────────────────────────────
retrieval:
  l1_top_k: 5                   # L1 seed node count
  l2_max_expansion: 10          # L2 expansion candidate limit
  l3_max_results: 10            # L3 final result limit
  pagerank:
    damping: 0.5                # Personalized PageRank damping factor
    max_iterations: 3           # PageRank iterations
    min_activation: 0.01        # Stop spreading below this activation
  fusion:
    vector_weight: 0.5          # Dense retrieval weight
    bm25_weight: 0.3            # Sparse retrieval weight
    graph_weight: 0.2           # Graph expansion weight
  dedup_threshold: 0.85         # Similarity above this triggers merge

# ── Token Budget ─────────────────────────────────────────
token:
  default_budget: 2000          # Default token budget for context
  cold_start_budget: 800        # When memory count < 50
  min_budget: 500
  max_budget: 3000

# ── Lifecycle ────────────────────────────────────────────
lifecycle:
  compress_after_days: 7        # Compress episodic memories after N days
  archive_after_days: 30        # Move to archive after N days
  audit_interval_days: 7        # Weekly audit
  decay_interval_hours: 24      # Daily decay

# ── Governance ───────────────────────────────────────────
governance:
  auto_merge_threshold: 0.90    # Auto-merge if similarity above this
  auto_delete_criteria:
    min_access_count: 0
    max_importance: 0.5
    require_negative_feedback: true
  weak_edge_threshold: 0.05     # Prune Hebbian edges below this

# ── MCP Server ───────────────────────────────────────────
mcp:
  server_name: "ai-memory"
  server_version: "0.1.0"

# ── Personal Memory Modules ──────────────────────────────
personal:
  enabled: true
  data_dir: "~/.ai-memory/personal"

Data Directory

All data is stored under ~/.ai-memory/ by default:

~/.ai-memory/
  memory.db              # Core memory SQLite database (WAL mode)
  chroma/                # ChromaDB vector store
  models/                # Cached local embedding model
  config.yaml            # Optional config override
  vault/                 # Encryption vault metadata
    .vault_autokey       # Auto-mode Fernet key (0600 permissions)
  personal/              # Personal memory module data
    habits.db
    experiences.db
    knowledge.db
    lifestyle.db
    accounts.db
    cross_links.db       # Cross-module link graph

---

Usage -- MCP Server Setup

The server communicates over stdio transport using the Model Context Protocol. It works with any MCP-compatible client.

Cursor

Add the following to your Cursor MCP configuration (Settings > MCP or .cursor/mcp.json):

{
  "mcpServers": {
    "ai-memory": {
      "command": "ai-memory-mcp",
      "args": []
    }
  }
}

If you installed in a virtual environment, use the full path:

{
  "mcpServers": {
    "ai-memory": {
      "command": "/path/to/venv/bin/ai-memory-mcp",
      "args": []
    }
  }
}

Claude Desktop

Add to your Claude Desktop configuration file (claude_desktop_config.json):

{
  "mcpServers": {
    "ai-memory": {
      "command": "ai-memory-mcp",
      "args": []
    }
  }
}

Direct invocation

ai-memory-mcp

The server reads JSON-RPC 2.0 requests from stdin and writes responses to stdout. If the mcp Python package is not installed, a minimal stdio JSON-RPC fallback handler is used automatically.

Config file discovery

On startup, the server searches for config.yaml in this order:

1. Current working directory (./config.yaml)
2. Project root (relative to the package source)
3. User home (~/.ai-memory/config.yaml)

If none is found, built-in defaults are used.

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MCP Tools Reference

The server exposes 24 MCP tools in total: 9 core memory tools and 15 personal memory tools.

Core Memory Tools (9)

Tool	Description	Key Parameters
memory_search	Search memories through the full L1->L2->L3 retrieval pipeline. Returns a formatted context string within the token budget.	query (required), project_id, max_tokens
memory_add	Add a new memory with automatic embedding, tag extraction, and write-time deduplication. Merges if similarity exceeds the threshold.	content (required), memory_type, tags, importance, project_id
memory_update	Update an existing memory's content. Regenerates the embedding and refreshes the vector store.	memory_id (required), content
memory_delete	Delete memories by ID or by tags. Soft-deletes in SQLite and removes from the vector store.	memory_id, tags
memory_list	List memories with optional type and tag filters. Returns formatted entries with importance, access count, and tags.	memory_type, tags, project_id, limit
memory_stats	Return memory statistics: total count, type distribution, and cache hit rate.	project_id
memory_confirm_usage	Confirm that specific memories were used in the current session. Increments adoption count and strengthens Hebbian connections between co-activated memories.	memory_ids (required), session_id
memory_feedback	Submit feedback for a memory. Updates feedback history and recomputes the reinforcement weight.	memory_id (required), score (required: -1, 0, +1), comment
memory_audit_report	Generate a memory audit report with redundancy analysis, feedback summary, graph statistics, and a composite health score (0--1).	project_id

Memory types: semantic, episodic, procedural, working

Personal Memory Tools (15)

Unified / Cross-Module (3)

Tool	Description	Key Parameters
personal_search	Search across all personal memory modules (accounts, habits, experiences, knowledge, lifestyle). Returns matching results from each.	query (required), limit_per_module
personal_overview	Get an aggregate overview of all personal memory data: total counts per module and cross-module link count.	(none)
personal_due_items	Get all items needing attention: overdue habits, due knowledge reviews, and high decay risk cards.	(none)

Account Vault (4)

Tool	Description	Key Parameters
account_search	Search stored accounts by platform, username, or email. Returns masked results (no passwords).	query (required), limit
account_get	Get detailed information for a specific account by ID. Returns masked data.	account_id (required)
account_get_password	Decrypt and return the plaintext password for a specific account. Use with caution.	account_id (required)
account_add	Add a new account with an encrypted password. The password is encrypted before storage and never stored in plaintext.	platform (required), username (required), password (required), category, email, url, notes, tags, password_hint, two_factor_enabled

Account categories: email, social, cloud, developer, finance, shopping, entertainment, work, education, government, other

Habit Tracking (3)

Tool	Description	Key Parameters
habit_list	List all habits with optional category filter and favorites-only mode.	category, favorites_only
habit_checkin	Check in a habit for today or a specified date. Updates streak counters automatically.	habit_id (required), date, note, mood
habit_overdue	Get all habits that are overdue for check-in.	(none)

Habit frequencies: daily, weekly, mon_fri, specific_days, every_n_days, monthly

Knowledge Management (3)

Tool	Description	Key Parameters
knowledge_search	Search knowledge cards by title, content, or tags.	query (required), limit
knowledge_due	Get knowledge cards that are due for review (SM-2 algorithm).	limit
knowledge_review	Review a knowledge card, updating its SM-2 schedule.	card_id (required), mastery_after (required), notes

Mastery levels: aware, familiar, proficient, mastered

Experience Recording (2)

Tool	Description	Key Parameters
experience_search	Search experiences by title, description, or tags.	query (required), limit
experience_add	Record a new experience entry with optional lessons and tags.	title (required), description, category, sentiment, importance, lessons, tags

Experience categories: career, relationship, travel, education, health, finance, creativity, failure, success, life_lesson, conflict, discovery, growth, loss, transition, other

---

Security

Account Vault Encryption

The account vault uses a two-layer encryption scheme:

1. Key Derivation: The master encryption key is derived using Argon2id (RFC 9106) with 2 GiB memory cost, 4 lanes, and 1 iteration. If Argon2id is unavailable, PBKDF2-HMAC-SHA256 with 1,200,000 iterations is used as a fallback. The salt is 16 bytes of cryptographic random.

2. Symmetric Encryption: The derived key is used with Fernet (AES-128-CBC + HMAC-SHA256), which provides authenticated encryption -- tampering with ciphertext is detected on decryption.

3. Key Storage Modes:

   • Auto mode (default): A random Fernet key is generated on first use and stored in a permission-protected file (0600). No user password is required. Suitable for local-only plugins where OS file permissions provide the first line of defense.
   • Password mode (optional): The user sets a master password, validated via Argon2id key derivation against a stored verification token. More secure for shared devices. Users can upgrade from auto mode at any time via upgrade_to_password().

4. Sensitive fields are never stored in plaintext. Passwords, security answers, API keys, and recovery codes are encrypted as Fernet tokens. Non-sensitive fields (platform, category, tags) remain in plaintext for searchability.

Data Masking

All display output from personal memory tools applies configurable masking:

Data Type	Masking Example
Passwords	Always fully masked (--------)
Emails	u***@example.com (first char + domain visible)
Phone numbers	138****8888 (first 3 + last 4 digits)
API keys	sk-****...****ab2f (first 4 + last 4 characters)
Credit cards	**** **** **** 1234 (last 4 digits only)
Usernames	user*** (partial masking based on length)
ID cards	110***********1234 (first 3 + last 4 digits)

Three masking levels are available: full (complete masking), partial (default, shows some characters), and none (no masking, use with caution).

Privacy Levels

All personal data entries carry a privacy_level field for access control:

• public -- Shareable
• personal -- Default for habits, experiences, knowledge, preferences
• sensitive -- Default for contacts and addresses
• highly_sensitive -- Reserved for the most sensitive data

File Permissions

Vault directory and key files are created with restrictive permissions:

• Vault directory: 0700 (owner only)
• Key/metadata files: 0600 (owner read/write only)

---

Development

Setup

git clone https://github.com/sscctv/mcp-memory-suite.git
cd mcp-memory-suite
pip install -e ".[dev]"

Running Tests

The project includes 1204 tests with 91% code coverage.

# Run all tests
pytest

# Run with verbose output
pytest -v

# Run a specific test file
pytest tests/test_core.py

Test Organization

Test File	Coverage Area
tests/test_core.py	Core memory system: storage, retrieval, weights, Hebbian, governance, bootstrap
tests/test_personal.py	Personal memory stores: habits, experiences, knowledge, lifestyle
tests/test_personal_mcp.py	Personal MCP tool dispatcher and tool definitions
tests/test_auto_crypto.py	Vault auto-mode encryption/decryption
tests/test_habits.py	Habit tracking: check-ins, streaks, overdue detection
tests/test_experiences.py	Experience recording: categories, sentiments, reflections
tests/test_knowledge.py	Knowledge management: SM-2 scheduling, decay risk
tests/test_lifestyle.py	Lifestyle: preferences, routines, contacts, addresses
tests/test_manager.py	PersonalMemoryManager: cross-module linking, unified search
tests/test_cli.py / test_cli_extended.py	CLI commands: all subcommands and edge cases
tests/test_mcp_extended.py	Extended MCP server tool testing
tests/test_review.py	Scheduled review system: daily/weekly reports, scheduler
tests/test_notifications.py	Notification and reminder system
tests/test_backup.py	Backup and recovery system
tests/test_sync.py	Multi-device synchronization
tests/test_import_export.py / test_import_export_extended.py	Data import/export
tests/test_web_server.py	Web UI server
tests/test_coverage_gaps.py	Edge cases and coverage gap fills

Coverage Report

# Generate coverage report
pytest --cov=memory_plugin --cov-report=html
# Open htmlcov/index.html in a browser

Benchmarks

Benchmark scripts are available in the benchmarks/ directory:

python benchmarks/benchmark_retrieval.py    # Retrieval pipeline performance
python benchmarks/benchmark_performance.py  # Overall system performance

Results are saved to benchmarks/retrieval_report.json and benchmarks/performance_report.json.

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Project Structure

ai-memory-mcp/
|-- config.yaml                        # Main configuration file
|-- pyproject.toml                     # Package metadata, dependencies, entry points
|-- src/
|   `-- memory_plugin/
|       |-- __init__.py
|       |-- mcp_server.py              # MCP server: 9 core tool handlers, lifecycle
|       |-- personal_mcp_tools.py      # 15 personal tool definitions + dispatcher
|       |-- config.py                  # Configuration dataclasses, YAML loading
|       |-- models.py                  # MemoryEntry, Feedback, Provenance, SearchResult
|       |-- embedding.py              # Embedding provider (API / local / hash fallback)
|       |-- weights.py                # Three-factor reinforcement weight calculator
|       |-- hebbian.py                # Hebbian connection weight updater
|       |-- governance.py             # Audit, redundancy/contradiction detection, health score
|       |-- lifecycle.py             # Decay, compression, forgetting, archiving
|       |-- bootstrap.py             # Codebase scanner for cold-start memory extraction
|       |-- utils.py                  # Cosine similarity, token counting, tag extraction
|       |-- retrieval/
|       |   |-- __init__.py
|       |   |-- l1_direct.py          # L1: vector + BM25 parallel recall, fusion
|       |   |-- l2_expansion.py       # L2: Personalized PageRank spreading activation
|       |   |-- l3_context.py         # L3: weighted scoring, token-budget packing, formatting
|       |   `-- fusion.py             # Multi-channel score fusion + semantic deduplication
|       |-- storage/
|       |   |-- __init__.py
|       |   |-- sqlite_store.py       # SQLite storage with WAL mode
|       |   |-- vector_store.py       # ChromaDB vector storage
|       |   `-- cache.py              # LRU hot cache with TTL
|       `-- personal/
|           |-- __init__.py
|           |-- manager.py            # PersonalMemoryManager: unified facade, cross-module linking
|           |-- crypto.py             # VaultCrypto: Argon2id + Fernet encryption
|           |-- masking.py            # DataMasking: PII masking utilities
|           |-- password_utils.py     # Password generator + strength evaluator
|           |-- shared_types.py       # PrivacyLevel, ModuleType, LinkType enums
|           |-- account_models.py     # AccountEntry, SecurityQuestion, APIKeyEntry
|           |-- account_store.py      # AccountStore: encrypted CRUD operations
|           |-- habits_models.py      # Habit, HabitCheckIn, frequency/category enums
|           |-- habits_store.py       # HabitStore: check-ins, streaks, overdue detection
|           |-- experiences_models.py # Experience, EmotionPoint, GrowthOutcome, Reflection
|           |-- experiences_store.py  # ExperienceStore: CRUD, search, stats
|           |-- knowledge_models.py   # KnowledgeCard, ReviewRecord, SM-2 types
|           |-- knowledge_store.py    # KnowledgeStore: SM-2 scheduling, decay risk
|           |-- lifestyle_models.py   # Preference, Routine, Contact, Address
|           |-- lifestyle_store.py    # LifestyleStore: multi-entity storage
|           |-- backup.py             # Backup and recovery with retention
|           |-- notifications.py      # Reminder engine: habits, reviews, expiries
|           |-- review.py             # Scheduled review: daily/weekly reports
|           |-- sync.py              # Multi-device sync via JSON snapshots
|           `-- web_server.py        # Web UI server for browser access
|-- tests/                             # 1204 tests, 91% coverage
|   |-- conftest.py
|   |-- test_core.py
|   |-- test_personal.py
|   |-- test_personal_mcp.py
|   |-- test_auto_crypto.py
|   |-- test_habits.py
|   |-- test_experiences.py
|   |-- test_knowledge.py
|   |-- test_lifestyle.py
|   |-- test_manager.py
|   |-- test_cli.py
|   |-- test_cli_extended.py
|   |-- test_mcp_extended.py
|   |-- test_review.py
|   |-- test_notifications.py
|   |-- test_backup.py
|   |-- test_sync.py
|   |-- test_import_export.py
|   |-- test_import_export_extended.py
|   |-- test_web_server.py
|   `-- test_coverage_gaps.py
|-- benchmarks/                        # Performance benchmarking scripts
|-- data/                              # Runtime data (vault, databases)
`-- coverage.json                      # Coverage report data

---

License

This project is licensed under the MIT License -- see the LICENSE file for details.