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Memori

The Open-Source Memory Layer for AI Agents & Multi-Agent Systems

Philosophy

Second-memory for all your LLM work - Never repeat context again. Simple, reliable architecture that just works out of the box.

Why Memori?

Give your AI agents structured, persistent memory with professional-grade architecture:

# Before: Repeating context every time
response = completion(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a Python expert..."},
        {"role": "user", "content": "Remember, I use Flask and pytest..."},
        {"role": "user", "content": "Help me with authentication"}
    ]
)

# After: Automatic context injection
from memori import Memori

memori = Memori(openai_api_key="your-key")
memori.enable()  # Auto-records ALL LLM conversations

# Context automatically injected from memory
response = completion(
    model="gpt-4", 
    messages=[{"role": "user", "content": "Help me with authentication"}]
)

✨ Key Features

  • 🎯 Universal Integration: Works with ANY LLM library (LiteLLM, OpenAI, Anthropic)
  • 🧠 Intelligent Processing: Pydantic-based memory with entity extraction
  • 🔄 Auto-Context Injection: Relevant memories automatically added to conversations
  • 📊 Multiple Memory Types: Short-term, long-term, rules, and entity relationships
  • 🔍 Advanced Search: Full-text search with semantic ranking
  • ⚙️ Production-Ready: Comprehensive error handling, logging, and configuration
  • 🗄️ Database Support: SQLite, PostgreSQL, MySQL
  • 🛡️ Type Safety: Full Pydantic validation and type checking

🚀 Quick Start

Installation

pip install memorisdk

Basic Usage with LiteLLM

  1. Install litellm:
pip install litellm
  1. Set OpenAI API Key:
export OPENAI_API_KEY="sk-your-openai-key-here"
  1. Run the following Python script:
from memori import Memori
from litellm import completion

# Initialize memory
memori = Memori(conscious_ingest=True)
memori.enable()

# First conversation - establish context
response1 = completion(
    model="gpt-4o-mini",
    messages=[{
        "role": "user", 
        "content": "I'm working on a Python FastAPI project"
    }]
)
print("Assistant:", response1.choices[0].message.content)

# Second conversation - memory provides context  
response2 = completion(
    model="gpt-4o-mini", 
    messages=[{
        "role": "user",
        "content": "Help me add user authentication"
    }]
)
print("Assistant:", response2.choices[0].message.content)

📋 Memory Types

Type Purpose Retention Use Case
Short-term Recent conversations 7-30 days Context for current session
Long-term Important insights Permanent User preferences, key facts
Rules User preferences/constraints Permanent "I prefer Python", "Use pytest"
Entities People, projects, technologies Tracked Relationship mapping

🔌 Universal Integration

Works with ANY LLM library:

memori.enable()  # Enable universal recording

# LiteLLM (recommended)
from litellm import completion
completion(model="gpt-4", messages=[...])

# OpenAI
import openai
client = openai.OpenAI()
client.chat.completions.create(...)

# Anthropic  
import anthropic
client = anthropic.Anthropic()
client.messages.create(...)

# All automatically recorded and contextualized!

🔧 Configuration

Simple Setup

from memori import Memori

memori = Memori(
    database_connect="sqlite:///my_memory.db",
    conscious_ingest=True,
    openai_api_key="sk-..."
)

Advanced Configuration

from memori import ConfigManager

# Load from memori.json or environment
config = ConfigManager()
config.auto_load()

memori = Memori()
memori.enable()

📚 Next Steps

Made for developers who want their AI agents to remember and learn