CarryMem — Your AI Finally Remembers Who You Are

Stop teaching AI who you are every single conversation.

Your portable AI memory — preferences, decisions, and corrections that follow you across models, tools, and devices.

Every time you open a new chat, you introduce yourself again. Your preferences, your decisions, your corrections — all forgotten. Switch from Cursor to Claude Code, from GPT to Claude, start from scratch every time.

You're not using AI. You're training it. Over and over.

CarryMem fixes this. It's a lightweight, zero-dependency memory system that stores who you are and makes that identity available to any AI tool. Your AI remembers your preferences, your past decisions, and the corrections you've made — so you can focus on building, not repeating yourself.

English | 中文 | 日本語 | 한국어 | 繁體中文

🌟 The 30-Second Version

你每天见客户、开会、聊天，AI 问你一句你答一句，下次对话它又忘了你是谁。

CarryMem 让 AI 自动记住你的偏好和决策——不用每次重复说。装一次，所有 AI 工具通用。

Technical users: see PrefEval benchmarks (83.0% ICLR 2025 Oral) and architecture docs below.

Topics: ai-memory mcp claude-code agent-memory cursor obsidian preference-injection sqlite llm-tools portable-memory

What CarryMem Does

5 scenarios you'll recognize:

"I don't want to tell AI my preferences every time"
"I prefer PostgreSQL" "Use React not Vue" "No comments in code" — say it once, remembered forever.

"I switched AI tools and started from scratch"
Taught AI in Cursor, now teaching it again in Claude Code. CarryMem makes your AI memory follow you.

"I want to take my data with me"
Your AI memory is yours. One file to pack, restore on any machine, any tool.

"USB Carry — my memories in my pocket" 🔑
Pack your memories to an encrypted .carry file, copy to USB, unpack on a new machine. Your AI identity travels with you — preferences, decisions, corrections, and rules all intact. Every agent on the new machine instantly knows who you are.

"My team shares conventions across agents"
Team lead packs company rules as a Skill bundle, every team member installs it. All agents enforce the same conventions — no more "I didn't know we use SSL."

🎯 Real User Scenarios

Scenario 1: The Multi-Tool Developer

Monday:  Tell Cursor "I prefer dark mode, PostgreSQL, React"
Tuesday: Open Claude Code — it already knows your stack
Friday:  Switch to TRAE — same preferences, zero repetition

How: carrymem setup-mcp --all --global — one command, all tools share one memory.

Scenario 2: The USB Carry — New Machine, Same Identity

1. On your laptop:  carrymem pack -o my_identity.carry --encrypt
2. Copy my_identity.carry to USB drive
3. At new workplace: Install CarryMem on new machine
4. carrymem unpack my_identity.carry
5. Every agent on the new machine knows your preferences, decisions, and rules

Encrypted + SHA-256 checksum — your identity is safe even if the USB is lost.

Scenario 3: The Team Lead

1. Create team conventions as rules: "Always use SSL", "Never deploy on Friday"
2. Pack as Skill: carrymem skill-pack rules.json --name team-conventions
3. Share the .json file with team
4. Each member: carrymem skill-install team-conventions.json --scope company
5. All agents now enforce company conventions automatically

Scenario 4: The Long-Term User

Month 1: "I prefer dark mode" → stored as user_preference
Month 3: "Switch to light mode" → auto-supersedes old preference
Month 6: carrymem whoami → shows "I prefer light mode" (dark mode archived)

Preferences evolve. CarryMem tracks the history.

Get Started (pick your path)

Using Cursor / Claude Code / TRAE?

pip install carrymem && carrymem setup-mcp --all --global

Restart your AI tool. Done.

Verify it works (30 seconds)

Tell your AI:

Remember, I prefer PostgreSQL

Start a new conversation and ask:

What database do I prefer?

AI answers "PostgreSQL" — it works!

Need to move your memory?

carrymem pack                    # Creates carrymem_identity_20260526.carry
# Copy to USB / cloud / new machine
carrymem unpack my_identity.carry  # All memories restored

# With encryption for sensitive data
carrymem pack -o my_memories.carry --encrypt   # Password-encrypted .carry file
carrymem unpack my_memories.carry              # Auto-detects encryption, prompts for password

Auto-backup & Recovery

carrymem backup                  # Manual backup (also auto-backup every 20 writes)
carrymem backup --list           # List all backups
carrymem backup --restore memories_backup_20260527_120000.db  # Restore from backup

📊 Academically Verified: CarryMem's preference injection accuracy (83.0%) was measured using the PrefEval protocol (ICLR 2025 Oral, Amazon Science), outperforming simple reminder (80.0%) and zero-shot (71.5%) baselines across 200 test items. See Citation below.

3 Reasons to Choose CarryMem

These are what make CarryMem different from every other memory solution:

1. Preference Injection Precision — 83.0% (Academically Verified)

Measured by PrefEval (ICLR 2025 Oral, Amazon Science), 200-sample 3-condition comparison
CarryMem 83.0% > simple reminder 80.0% > zero-shot 71.5%
Proactive injection > full reminder — first system to prove this
24% fewer unhelpful responses than reminder (28 vs 38) — more precise, less noisy

2. Zero-LLM Classification — 88% Without Calling Any LLM

Rule engine classifies 88% of memories with zero token cost
Only system with built-in rule engine (competitors: 0%)
P99 latency: 1.3ms — 93x faster than Mem0

3. Lightweight & Portable — SQLite Only

Zero external dependencies for core functionality
Single .db file — carry your identity anywhere
Works with Cursor, Claude Code, ChatGPT, any MCP client

How It Works

User Input → Auto-Classification (7 types, 88% rule-based) → Smart Storage (SQLite + FTS5)
    → Semantic Recall (cross-language) → Context Injection (token budget) → AI Tool

Quick Start

Install

pip install carrymem

Requires Python 3.12+. Check your version: python --version

From PyPI: https://pypi.org/project/carrymem/

For development: git clone https://github.com/lulin70/carrymem.git && cd carrymem && pip install -e ".[dev]"

System Requirements

Python: ≥3.12 (64-bit)
OS: macOS 10.15+, Ubuntu 20.04+, Windows 10+
Disk: ~5MB for core, ~200MB with semantic search
Memory: ~50MB base

Dependencies

Feature	Package	Install
Core	PyYAML≥5.0	`pip install carrymem` (included)
Multi-language	pycld2, langdetect	`pip install carrymem[language]`
Semantic Search	sqlite-vec, sentence-transformers	`pip install carrymem[semantic]`
Encryption	cryptography≥41.0	`pip install carrymem[encryption]`
Full (all features)	all above	`pip install carrymem[full]`
Development	pytest, black, flake8...	`pip install -e ".[dev]"`

Zero LLM dependency for core features — classification uses rule engine only.

Verify Installation

carrymem version

If command not found, add Python bin to PATH:

# macOS (add to ~/.zshrc)
export PATH="$HOME/Library/Python/3.12/bin:$PATH"

# Linux (add to ~/.bashrc)
export PATH="$HOME/.local/bin:$PATH"

# Or use Python module directly
python3 -m carrymem.cli version

Then run carrymem doctor to check your setup.

5 Lines of Code

⚠️ Package vs Import Name: Install with pip install carrymem (lowercase), but import as from carrymem import CarryMem (CamelCase class). The package name (carrymem) and class name (CarryMem) differ in casing.

from carrymem import CarryMem

cm = CarryMem()
cm.classify_and_remember("I prefer dark mode")        # Auto-classified as preference
cm.classify_and_remember("Use PostgreSQL not MySQL")   # Auto-classified as correction
cm.classify_and_remember("I prefer light mode now", session_id="sess_002")  # Session-aware
memories = cm.recall_memories("database")              # Semantic recall
memories = cm.recall_memories("mode", filters={"session_id": "sess_002"})  # Filter by session
agg = cm.recall_aggregated()                           # Aggregate by type
timeline = cm.recall_timeline("database")              # Knowledge evolution
print(cm.build_system_prompt())                        # Inject into any AI
cm.close()

CLI (50+ commands)

carrymem init                           # Initialize
carrymem add "I prefer dark mode"       # Store a memory
carrymem add "test note" --force        # Force store (bypass classification)
carrymem list                           # List memories
carrymem search "theme"                 # Search memories
carrymem show <key>                     # View memory details
carrymem edit <key> "new content"       # Edit a memory
carrymem forget <key>                   # Delete a memory
carrymem whoami                         # Who your AI thinks you are
carrymem profile export --output identity.json   # Export your AI identity
carrymem stats                          # Memory statistics
carrymem check                          # Quality & conflict check
carrymem clean --expired --dry-run      # Preview cleanup
carrymem doctor                         # Diagnose installation
carrymem setup-mcp --tool cursor        # One-line MCP config
carrymem tui                            # Terminal UI
carrymem export backup.json             # Export all memories
carrymem import backup.json             # Import memories
carrymem pack -o my_memories.carry      # Pack into portable .carry file
carrymem pack -o my_memories.carry --encrypt  # Encrypted .carry file
carrymem unpack my_memories.carry       # Unpack .carry file
carrymem backup                         # Manual backup
carrymem backup --list                  # List backups
carrymem backup --restore <file>        # Restore from backup
carrymem version                        # Show version
# Rule Engine commands
carrymem rules add "use SSL" --trigger "database" --type avoid  # Add a rule
carrymem rules list --status active                              # List active rules
carrymem rules suggest                                           # Suggest rules
# Also available: carrymem add-rule, carrymem list-rules (legacy aliases)
carrymem rules pack rules.json --name team-conventions   # Pack rules as Skill
carrymem rules install team-conventions.json --scope company  # Install Skill
carrymem rules verify team-conventions.json              # Verify Skill integrity

Core Features (powering the 3 advantages)

Memory That Understands You

Auto-Classification (7 Memory Types)

CarryMem automatically identifies what kind of information you're sharing:

Type	Icon	Example
`user_preference`	⭐	"I prefer dark mode"
`correction`	🔧	"No, I meant Python 3.11 not 3.10"
`decision`	🎯	"Let's use React for the frontend"
`fact_declaration`	📌	"Python 3.12 is the runtime version"
`relationship`	❓	"Sarah is my manager"
`task_pattern`	🔄	"I always write tests first"
`sentiment_marker`	💭	"This build is too slow"

Semantic Recall (Cross-Language)

cm.classify_and_remember("我偏好使用PostgreSQL")

# All of these find it:
cm.recall_memories("PostgreSQL")     # Exact match
cm.recall_memories("数据库")          # Synonym expansion
cm.recall_memories("Postgres")       # Spell correction
cm.recall_memories("データベース")    # Cross-language (Japanese)

Identity Layer (whoami)

identity = cm.whoami()
print(identity["preferences"])   # ["I prefer dark mode", ...]
print(identity["decisions"])     # ["Let's use React", ...]
print(identity["corrections"])   # ["The port should be 5432", ...]

$ carrymem whoami

  Who You Are (according to your AI)
  ==================================================

  Your Preferences:
    ⭐ I prefer dark mode for all editors
    ⭐ I use PostgreSQL for databases
    ⭐ I always use Python for data analysis

  Your Decisions:
    🎯 Let's use React for the frontend

  Your Corrections:
    🔧 The port should be 5432, not 3306

  Memory Profile:
    Total: 19 | Dominant: user_preference | Avg Confidence: 73%

Preference Injection (advantage #1)

Version Chain — Preferences Evolve, Old Versions Auto-Archived

cm.update_memory(key, "Updated content")     # Creates version 2
history = cm.get_memory_history(key)          # [v1, v2]
cm.rollback_memory(key, version=1)            # Restore v1

Scope-Aware Injection — Only Inject Relevant Preferences Per Context

Preferences are injected based on context scope, so your database preferences don't clutter frontend discussions.

Token Budget — 60% Budget for Preferences, Never Truncated

CarryMem allocates 60% of the token budget to preferences, ensuring they're never cut off. This is the key to achieving 83.0% on PrefEval — structured preference injection beats simple reminders.

Memory Lifecycle (advantage #2)

Importance Scoring — Confidence × Type × Recency × Access

Every memory has an importance score that evolves over time:

importance = confidence × type_weight × recency_factor × access_factor

30-day half-life decay — old memories fade unless accessed
Access reinforcement — frequently recalled memories stay fresh
Type weighting — corrections (1.3x) > decisions (1.2x) > preferences (1.1x)

Consolidation (P0/P1/P2) — Dedup + Decay + Pattern → Rules + Semantic Merge

Automatic memory lifecycle management with three phases:

# Preview what consolidation would do
report = cm.consolidate(dry_run=True)
print(f"Duplicates: {report['stats']['duplicates_found']}")
print(f"Decayed: {len(report['to_decay'])}")

# Run consolidation (P0: dedup + decay, P1: pattern promotion, P2: semantic merge)
report = cm.consolidate(dry_run=False, run_p1=True, run_p2=True)

Phase	Function	Mechanism
P0	Dedup + Decay	Jaccard similarity dedup, exponential half-life decay (preferences: 270d, facts: 90d, sentiments: 45d)
P1	Pattern → Rules	Detect repeated patterns → generate rule candidates for review
P2	Semantic Merge	Cluster related memories → request host LLM to consolidate

Preferences are always preserved — never decayed or deduplicated.

Scheduled Consolidation — Automatic Background Maintenance

Run consolidation automatically on a recurring interval:

# Schedule consolidation every hour (runs in background thread)
cm.schedule_consolidation(interval_hours=1.0)

# Stop the scheduled consolidation
cm.stop_consolidation()

CLI:

carrymem consolidate --schedule 1h   # Run consolidation every hour
carrymem consolidate --stop          # Stop scheduled consolidation

Security & Portability (advantage #3)

Auto-Redaction — 24 Sensitive Patterns

Automatically detects and redacts API keys, passwords, tokens, and 21 other sensitive patterns before storage.

Encryption — AES-128 at Rest

Feature	Description
Encryption	AES-128 (Fernet) or HMAC-CTR fallback, zero-dep
Encrypted .carry files	`pack --encrypt` for password-encrypted portable files
Auto-Backup	Every 20 writes, VACUUM INTO backup, max 5 retained
Backup/Restore	Manual backup, list, and restore via `carrymem backup`
Audit Log	SQLite-persisted operation history (~/.carrymem/audit.db)
Version History	Every edit tracked, rollback supported
Input Validation	SQL injection, XSS, path traversal protection

cm = CarryMem(encryption_key="my-secret-key")
# All content encrypted at rest, decrypted on read

Backup/Restore — Auto-Backup + Manual Control

Auto-backup triggers every 20 write operations (VACUUM INTO), retaining up to 5 backup files. Manual control via CLI:

carrymem backup                  # Create manual backup
carrymem backup --list           # List all backups
carrymem backup --restore <file> # Restore from a specific backup

Pack/Unpack — USB Carry with Encryption

# Pack memories into a portable .carry file
carrymem pack -o my_memories.carry

# With password encryption for sensitive data
carrymem pack -o my_memories.carry --encrypt

# Unpack on any machine (auto-detects encryption)
carrymem unpack my_memories.carry

SHA-256 checksum ensures file integrity. v1.0 .carry format is backward compatible with a warning.

Export/Import — Identity Follows You Across Devices

# Export your AI identity
cm.export_profile(output_path="my_identity.json")

# On another device or AI tool
cm.import_memories(input_path="backup.json")

Supporting Features

Error Code System (v0.4.0 New)

Structured error handling with bilingual messages:

from carrymem.errors import CarryMemError

# Error code ranges:
# CM-001~099: Configuration & Initialization
# CM-100~199: Storage Adapter
# CM-200~299: Memory Operations
# CM-300~399: Classification & Rule Engine
# CM-400~499: Security & Encryption
# CM-500~599: Import / Export
# CM-600~699: CLI / TUI / MCP Entry Points

try:
    cm.classify_and_remember("test")
except CarryMemError as e:
    print(e.code)     # "CM-001"
    print(e.message)  # "存储适配器未配置。" (Chinese)
    print(e.hint)     # "💡 Use CarryMem(storage='sqlite')..."

Features:

37 error codes with Chinese + English messages
from_cause() factory maps low-level exceptions → friendly codes
Actionable hints for every error
7 concrete error subclasses for programmatic handling

Monitoring Framework (v0.4.0 New)

Production-ready monitoring with Prometheus export:

from carrymem.monitoring import HealthChecker, MetricsCollector, AlertManager, MonitoringHTTPServer

# Health checks
health = HealthChecker()
health.register_check("storage", lambda: cm._adapter is not None)
status = health.check()  # {"status": "ok", "checks": {...}, "slo": [...]}

# Metrics collection
metrics = MetricsCollector()
metrics.increment("classify_and_remember")
metrics.record_latency("recall", 12.5)
print(metrics.to_prometheus())  # Prometheus text format

# SLO alerts
alerts = AlertManager()
alert_list = alerts.check_alerts(metrics.get_snapshot())

# HTTP server (optional)
server = MonitoringHTTPServer(port=8766, health_checker=health, metrics_collector=metrics)

SLO Targets:

classify_and_remember P99 < 200ms
recall P99 < 500ms
Startup time < 2s

Plugin System (v0.4.0 New)

Extensible plugin architecture with hook points:

from carrymem.plugins import PluginProtocol, PluginManager, HookPoint

class MyPlugin:
    name = "my-plugin"
    version = "1.0.0"

    def on_load(self, carrymem):
        print(f"Loaded into CarryMem")

    def on_memory_stored(self, memory):
        print(f"Memory stored: {memory.content}")

    def on_unload(self):
        print("Plugin unloaded")

manager = PluginManager(plugin_dir="./plugins")
manager.set_carrymem(cm)
manager.load("my-plugin")

Hook Points: on_memory_stored | on_memory_recalled | on_classified | on_error

Permission System (v0.4.0 New)

Lightweight access control MVP:

from carrymem.security.permissions import Permission, AccessPolicy

policy = AccessPolicy(owner_id="user-123")

# Check permissions
policy.check("user-123", Permission.READ)   # True
policy.check("other-user", Permission.WRITE)  # False

# Require permission (raises SecurityError on denial)
policy.require("user-123", Permission.DELETE, resource="memory")

i18n Internationalization (v0.4.0 New)

Multi-language support without gettext dependency:

from carrymem.i18n import I18nManager, set_locale, _

# Switch language
set_locale("zh-CN")

# Translate with variable interpolation
print(_("memory.stored", count=3))
# → "已记住 3 条记忆"

# Available locales: en, zh-CN

MCP Integration (One-Line Setup)

# Configure for Cursor
carrymem setup-mcp --tool cursor

# Configure for Claude Code
carrymem setup-mcp --tool claude-code

# Configure for all
carrymem setup-mcp --tool all

28 MCP tools available: Core (3) · Storage (3) · Knowledge (3) · Profile (2) · Prompt (2) · Consolidation (3) · Rules (11) · Health (1)

Client Compatibility:

Status	Clients	Setup
✅ Direct	Cursor, Claude Code, TRAE, Windsurf, Cline	`setup-mcp --global`
✅ Auto-detect	OpenClaw, Kimi Code CLI, CodeX	`setup-mcp --global` (falls back to Claude Code format)
📋 Marketplace	WorkBuddy, CodeBuddy	Submit to MCP Marketplace (pending)
❌ Not supported	Kimi Desktop, DeepSeek Desktop, Qianwen, Doubao, TiGong, ChatGLM	Closed platforms, no MCP interface

🔒 Your memories stay on your machine. CarryMem stores all data locally in ~/.carrymem/ (SQLite). Each user gets an independent database — just like Git, everyone uses the same tool but keeps their own repos. No cloud sync, no shared state, no cross-user conflicts.

Rule Engine with Scopes

Behavioral rules with three scope levels for team/organization alignment:

from carrymem.rules import RuleEngine

engine = RuleEngine()

# Company-mandated rules (highest priority, cannot be overridden)
engine.add_rule("database", "Always use SSL", scope="company", override=True)

# Personal preferences (lowest priority)
engine.add_rule("database", "Prefer PostgreSQL", scope="personal")

# Scope-aware matching
results = engine.match("database design", scopes=["company"])

Scope	Priority	Description
`company`	3 (highest)	Organization-mandated, cannot be overridden
`negotiated`	2	Adapted from company rules
`personal`	1 (lowest)	User-created preferences

Skill Format — Portable Rule Bundles

Share rule sets across teams with cryptographic integrity:

# Pack rules into a portable Skill bundle
bundle = engine.skill_pack(
    name="team-conventions",
    version="1.0.0",
    scope="company",
    author="team-lead",
)

# Verify integrity before installing
result = engine.skill_verify(bundle)
assert result["valid"] is True

# Install on another machine
engine.skill_install(bundle, scope_override="company", mode="skip")

Merge Protocol — Conflict Resolution

Three strategies for merging rules from different sources:

Strategy	Description
`company_overrides`	Higher scope always wins
`negotiate`	Conflicting rules adapted to "negotiated" scope
`keep_both`	Both rules kept for manual review

Quality Management

carrymem check                    # Check all
carrymem check --conflicts        # Detect contradictions
carrymem check --quality          # Find low-quality memories
carrymem check --expired          # Find expired memories
carrymem clean --expired --dry-run # Preview cleanup

Terminal UI

pip install textual
carrymem tui

Interactive terminal interface with sidebar filters, search, add, delete (d), and edit (e).

VS Code Extension

Rule management directly in your editor:

Rule sidebar with scope badges
Add/edit/delete rules via webview
Effectiveness report panel
Skill pack/install from file dialogs

Comparison

By Scenario

Scenario	Mem0	ima	CarryMem
AI remembers what I said	✅	⚠️ Manual	✅ Automatic
Switch AI tools, still remembers	❌	❌	✅ One file follows you
Don't want AI to remember something	❌	⚠️ Limited	✅ Delete anytime, separate zones
Remember without spending tokens	❌	❌	✅ 88% zero-cost
Own your own data	⚠️ Self-host only	❌ Cloud	✅ Local file

Feature Matrix

	CarryMem	Mem0	OpenChronicle	ima
Key Differentiator	Zero-LLM + Rule Engine	Vector DB + Cloud	Local-first	Cloud notes
Zero Dependencies	✅ SQLite only	⚠️ Vector DB optional	✅	❌ Cloud
Auto-Classification	✅ 7 types	❌	❌ Manual	❌
Identity Portrait	✅ whoami	❌	❌	❌
Rule Engine	✅ Scopes + Skills	❌	❌	❌
Pack / Unpack	✅ One file	❌	❌	❌
Encrypted Carry	✅ --encrypt	❌	❌	❌
Auto-Backup	✅ Every 20 writes	❌	❌	❌
Cross-Language Recall	✅ EN/CN/JP	❌	❌	❌
Encryption	✅ Built-in	❌	❌	❌
Data Ownership	✅ Local files	⚠️ Self-hostable	✅ Local	❌ Cloud

Note: Comparison based on publicly available information. Products evolve rapidly — please verify latest features.

Key Difference: Other products store what you read. CarryMem stores who you are.

🏆 PrefEval — Preference Adherence Benchmark

Condition	Accuracy	Acknowledged	Violated	Hallucinated	Unhelpful
Zero-shot	71.5%	160	27	3	31
Reminder	80.0%	199	2	1	38
CarryMem	83.0%	173	7	4	28

Protocol: PrefEval (ICLR 2025 Oral, Amazon Science)
Sample: 200 items, 10 inter-turns, Claude Sonnet 4

Why this matters: Reminder injects "remember user preference" in every turn. CarryMem injects structured preferences in system prompt — more precise, more persistent, 24% fewer unhelpful responses.

	Advantage	Result
💰	Zero-LLM Ingestion	88% memories need no LLM tokens
⚡	P99 Latency	1.3ms — 93x faster than Mem0
🪶	Dependencies	SQLite only — no vector DB
🛡️	Rule Engine	Only system with rule engine (competitors: 0%)

Architecture

Three-Layer: Mixin + Facade + Protocol (v0.4.0)

┌─────────────────────────────────────────────────────────┐
│                    Facade Layer                          │
│  CarryMem (unified entry point, health_check, version)  │
├─────────────────────────────────────────────────────────┤
│                   Mixin Layer (8 modules)                │
│  Lifecycle │ MemoryCRUD │ Classification │ Recall       │
│  Backup    │ ProfileExport │ Maintenance │ PromptDelegate│
├─────────────────────────────────────────────────────────┤
│                  Protocol Layer (10 Protocols)            │
│  LifecycleOps │ BackupOps │ RecallOps │ ... │ CarryMemOps│
└─────────────────────────────────────────────────────────┘

Data Flow:

User Input
    ↓
Auto-Classification (7 types, 4 tiers)
    ↓
Importance Scoring (confidence × type × recency × access)
    ↓
Smart Storage (SQLite + FTS5, WAL mode, thread-local pool, dedup, TTL, encryption)
    ↓
Memory Consolidation (P0: dedup+decay → P1: pattern→rules → P2: semantic merge)
    ↓
Semantic Recall (FTS5 + synonyms + spell fix + cross-language)
    ↓
Context Injection (token budget, relevance ranking)
    ↓
AI Tool (Cursor / Claude Code / any MCP client)

Three-Tier Classification:

Rule Engine (60%+) → Pattern Analysis (30%) → Semantic (10%)
     ↓                      ↓                      ↓
 Zero cost            Near-zero cost          Token cost

Module Overview

Module	Path	Description
Core Engine
`_lifecycle`	`src/carrymem/core/_lifecycle.py`	Lifecycle: `__init__`, `close`, context-manager, properties
`_memory_crud`	`src/carrymem/core/_memory_crud.py`	Core CRUD: `classify_and_remember`, declare, forget, update, merge
`_classification`	`src/carrymem/core/_classification.py`	Classification pipeline internals + rule-delegate methods
`_recall`	`src/carrymem/core/_recall.py`	Recall operations: memories, aggregated, timeline, knowledge
`_backup`	`src/carrymem/core/_backup.py`	Backup & audit operations
`_maintenance`	`src/carrymem/core/_maintenance.py`	Maintenance: conflict detection, quality scoring, expiry, consolidation
`_profile_export`	`src/carrymem/core/_profile_export.py`	Profile, stats, export, import operations
`_prompt_delegate`	`src/carrymem/core/_prompt_delegate.py`	Prompt delegation and LLM-powered features
`_protocols`	`src/carrymem/core/_protocols.py`	Protocol interfaces for Mixin composition (structural typing)
Error Handling (v0.4.0 New)
`errors`	`src/carrymem/errors.py`	CarryMemError base class, 7 error ranges (CM-001~999), from_cause() factory
`error_messages`	`src/carrymem/error_messages.py`	37 bilingual error messages (Chinese + English) with actionable hints
Adapters
`base`	`src/carrymem/adapters/base.py`	StorageAdapter ABC + MemoryEntry/StoredMemory dataclasses
`sqlite_adapter`	`src/carrymem/adapters/sqlite_adapter.py`	SQLite adapter (re-exports from `adapters/sqlite/`)
`json_adapter`	`src/carrymem/adapters/json_adapter.py`	JSON file-based storage adapter (zero-dependency)
`obsidian_adapter`	`src/carrymem/adapters/obsidian_adapter.py`	Obsidian vault knowledge-base adapter
Monitoring (v0.4.0 New)
`monitoring`	`src/carrymem/monitoring/__init__.py`	HealthChecker, MetricsCollector, AlertManager, MonitoringHTTPServer, LatencyTimer
Plugins (v0.4.0 New)
`plugins`	`src/carrymem/plugins/__init__.py`	PluginProtocol, PluginManager, HookPoint definitions, event dispatch
Security
`permissions`	`src/carrymem/security/permissions.py`	Permission constants & AccessPolicy (owner-based MVP)
i18n (v0.4.0 New)
`i18n`	`src/carrymem/i18n/__init__.py`	I18nManager dictionary-based translation, locale switching, variable interpolation
Coordinators
`classification_pipeline`	`src/carrymem/coordinators/classification_pipeline.py`	Multi-phase classification orchestration

Advanced Usage

Obsidian Knowledge Base

from carrymem import CarryMem, ObsidianAdapter

cm = CarryMem(knowledge_adapter=ObsidianAdapter("/path/to/vault"))
cm.index_knowledge()
results = cm.recall_from_knowledge("Python design patterns")

Async API

from carrymem import AsyncCarryMem

async with AsyncCarryMem() as cm:
    await cm.classify_and_remember("I prefer dark mode")
    memories = await cm.recall_memories("theme")

JSON Adapter (No SQLite)

from carrymem import CarryMem, JSONAdapter

cm = CarryMem(adapter=JSONAdapter(path="/path/to/memories.json"))

Memory Versioning

cm.update_memory(key, "Updated content")     # Creates version 2
history = cm.get_memory_history(key)          # [v1, v2]
cm.rollback_memory(key, version=1)            # Restore v1

Export Identity for Other AIs

# Export your AI identity
cm.export_profile(output_path="my_identity.json")

# On another device or AI tool
cm.import_memories(input_path="backup.json")

Who Is This For?

Tired of repeating yourself?
You use Cursor, Claude Code, ChatGPT daily. You've told AI your stack, your style, your decisions a hundred times. And it still asks "what framework do you prefer?" CarryMem makes your AI remember — so you don't have to keep reminding it.

Maintaining CLAUDE.md by hand?
You already know AI needs memory. You have prompt files everywhere. They conflict, they go stale, and they don't follow you between tools. CarryMem auto-classifies your preferences, decisions, and corrections — and keeps them fresh automatically.

Building AI agents?
Your agents forget users between sessions. You need a memory layer that's lightweight, local, and works with any LLM. CarryMem gives you 5-line integration, 7 memory types, and a rule engine — with zero dependencies beyond SQLite.

Documentation

Project Status

Current Version: v0.4.0
Tests: 3521+ passing
Coverage: 82%+

Changelog:

v0.4.0: Quality Sprint — 134 unit tests for core Mixins, health_check MCP tool (28 total), TUI delete/edit, CLI rules subcommand grouping, audit SQLite persistence, removed @runtime_checkable, merged StorageAdapterProtocol, all Any types replaced (24→0), mypy+bandit CI
v0.4.0: Protocol & Maturity Sprint — Mixin+Facade+Protocol 三层架构, 10个 Protocol 接口, 错误码体系 (CM-001~999), SQLite 连接池 (WAL+线程缓存), 加密升级 (PBKDF2 260K), E2E 测试补全 (+78), 监控框架 MVP, 插件系统 MVP, 权限系统 MVP, i18n 框架, 类型注解 ~82%, 72 new tests
v0.3.0: Maturity & Architecture Sprint — God Class→8 Mixin, exception narrowing (173→15), TUI enhancement (+453 lines, Morandi palette), constants.py (28 named), lazy import cache, ghost feature audit, 71 new tests
v0.2.5: Integration/E2E audit, ghost feature deprecation warnings, version chain validation, 83 new tests
v0.2.4: Beta release — CI root fix, 24 security fixes, Glama TDQS boost, 6-gate CI pipeline
v0.2.0: USB carry encryption, auto-backup, concurrent safety, PrefEval 83.0% (200 items), 8-client MCP setup
v0.2.3 (pre-reset): Consolidation scheduling (schedule/stop), PrefEval standardization
v0.2.2 (pre-reset): Token budget + dead code fix + security, PrefEval 87.9%
v0.2.1 (pre-reset): Coreference resolution, auto-redaction, QA prompt optimization

Contributing

git clone https://github.com/lulin70/carrymem.git
cd carrymem
pip install -e ".[dev]"
pytest

See Contributing Guide for details.

Citation

If you use CarryMem in your research, please cite:

@software{carrymem2026,
  title = {CarryMem: Persistent Memory for AI Agents with Preference Injection},
  author = {CarryMem Team},
  year = {2026},
  url = {https://github.com/carrymem/carrymem},
  note = {Preference injection accuracy 83.0\% measured by PrefEval protocol}
}

@inproceedings{chuang2025prefeval,
  title = {PrefEval: A Preference Evaluation Benchmark for LLMs},
  author = {Chuang, Yun-Nung and others},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  note = {Oral presentation, Amazon Science}
}

Experimental Results (200 items, 10 inter-turns, Claude Sonnet 4)

Condition	Accuracy	Acknowledged	Violated	Hallucinated	Unhelpful
Zero-shot	71.5%	160	27	3	31
Reminder	80.0%	199	2	1	38
CarryMem	83.0%	173	7	4	28

Key insight: CarryMem achieves the highest accuracy while producing 24% fewer unhelpful responses than reminder-based approaches, demonstrating that proactive memory injection is more precise than full-context reminding.

License

MIT License — see LICENSE

CarryMem — Your AI finally remembers who you are. Only you own the data.