🧠 Memory System

A flexible memory system for AI agents with optional embedding support and multiple storage backends.

Features

• Private & Shared Memories - Private by default, shared memories for multi-agent collaboration
• Embedding Search - Semantic search using sentence-transformers
• Multiple Backends - Local file / SQLite / GitHub / Gitee
• LLM Summarization - Auto-extract key info from conversations
• Memory Maintenance - Review, consolidate, tag suggestions
• Templates - Quick memory creation with templates

Installation

pip install sentence-transformers scikit-learn pyyaml numpy

Quick Start

Python API

from memory_system import MemoryManager

# Initialize (local storage)
mm = MemoryManager(backend='local')

# Add 
mm.add("User prefers dark theme", tags=["preference"])

# Search
results = mm.search("dark theme preference")

# List
entries = mm.list(tags=["preference"])

CLI

# Add 
python3 memory_cli.py add "User feedback: slow page load" --tags "bug,performance"

# List
python3 memory_cli.py list

# Search
python3 memory_cli.py -e search "performance issue"

# Semantic search (with embedding)
python3 memory_cli.py -e search "dark mode"

Private vs Shared Memory

Default: All memories are private. Use shared add only when other agents need to know.

# Private memory - user says "remember..."
mm.add("User name is Zhang San")

# Shared memory - user says "tell other agents..."
smm.add("Team decision: use React", agent_id="agent_a")

Storage Backends

Local (Default)

mm = MemoryManager(backend='local')

SQLite (High Performance)

mm = MemoryManager(backend='sqlite', base_path='./memory.db')

GitHub

export GITHUB_TOKEN="your_token"

mm = MemoryManager(
    backend='github',
    repo='owner/repo',
    branch='main'
)

Gitee

export GITEE_TOKEN="your_token"

mm = MemoryManager(
    backend='gitee',
    repo='owner/repo',
    branch='master'
)

Embedding & Semantic Search

Embedding is optional and auto-downloads on first use.

# Enable embedding
mm = MemoryManager(backend='local', use_embedding=True)

# Add (auto-generates vector)
mm.add("User works from 9am to 6pm")

# Semantic search - finds similar content
results = mm.search("what time does user work")

CLI with embedding:

python3 memory_cli.py -e search "working hours"

Shared Memory (Multi-Agent)

from memory_system import SharedMemoryManager

# Initialize
smm = SharedMemoryManager(backend='local', shared_path='./shared_memory')