aimemo 1.0.1

Memory System for AI Conversations

AIMemo

Memory System for AI Conversations

AIMemo is a lightweight memory layer that enables AI agents to remember context across conversations. Build AI applications that truly understand and remember your users.

🚀 Features

• Automatic Memory: Intercepts LLM calls and injects relevant context
• Multiple Backends: SQLite, PostgreSQL support out of the box
• Zero Config: Works with sensible defaults, configure when needed
• LLM Agnostic: Supports OpenAI, Anthropic, and more
• Namespace Isolation: Perfect for multi-user applications
• Full-Text Search: Fast memory retrieval with FTS5/PostgreSQL search

📦 Installation

pip install aimemo

For PostgreSQL support:

pip install aimemo[postgres]

⚡ Quick Start

from aimemo import AIMemo
from openai import OpenAI

# Initialize AIMemo
aimemo = AIMemo()
aimemo.enable()

# Use OpenAI normally - memory is automatic!
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "I'm building a FastAPI project"}]
)

# Later conversation - context is automatically injected
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "How do I add authentication?"}]
)
# The model remembers your FastAPI project!

💡 Use Cases

• Personal AI Assistants: Remember user preferences and history
• Customer Support Bots: Maintain context across support sessions
• Research Assistants: Keep track of research topics and findings
• Multi-Agent Systems: Share memory between multiple AI agents
• Learning Apps: Track student progress and learning patterns

🔧 Configuration

Database Options

SQLite (default):

from aimemo import AIMemo

aimemo = AIMemo()  # Uses aimemo.db by default

PostgreSQL:

from aimemo import AIMemo, PostgresStore

store = PostgresStore("postgresql://user:pass@localhost/aimemo")
aimemo = AIMemo(store=store)

Environment Variables

export AIMEMO_DB_PATH="./my_memory.db"
export AIMEMO_MAX_CONTEXT=10

Manual Memory Management

from aimemo import AIMemo

aimemo = AIMemo(namespace="user_123")

# Add memories manually
aimemo.add_memory(
    content="User prefers dark mode",
    tags=["preference", "ui"],
    metadata={"priority": "high"}
)

# Search memories
results = aimemo.search("dark mode", limit=5)

# Get formatted context
context = aimemo.get_context("user interface preferences")

Multi-User Applications

from aimemo import AIMemo

# Each user gets their own namespace
user_memory = AIMemo(namespace=f"user_{user_id}")
user_memory.enable()

# Memories are isolated per user

📚 Examples

Check out the examples/ directory:

• basic_usage.py - Simple conversation with memory
• manual_memory.py - Manual memory management
• postgres_example.py - Using PostgreSQL backend
• context_manager.py - Context manager pattern

🧪 Testing

pip install pytest
pytest tests/

🛠️ Development

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black aimemo tests examples

📖 Documentation

Core API

AIMemo

• enable() - Start intercepting LLM calls
• disable() - Stop intercepting
• add_memory(content, metadata, tags) - Add memory manually
• search(query, limit) - Search memories
• get_context(query, limit) - Get formatted context
• clear(namespace) - Clear memories

Storage Backends

• SQLiteStore(db_path) - SQLite storage
• PostgresStore(connection_string) - PostgreSQL storage

Architecture

AIMemo works by intercepting LLM API calls:

1. Pre-call: Searches relevant memories based on user query
2. Injection: Adds context to the conversation
3. Post-call: Stores the conversation for future reference

All automatically, with zero code changes!

🤝 Contributing

Contributions welcome! Please feel free to submit a Pull Request.

📄 License

MIT License - see LICENSE file for details.

🙋 Support

• GitHub Issues: Bug reports and feature requests
• Email: [e-mail]

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