> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Memori

> Memori is an open-source memory layer for AI. It automatically captures conversations, extracts meaningful facts, and makes them searchable across entities, processes, and sessions.

## Prerequisites

The following example requires the `memori` library.

```shell theme={null}
uv pip install -U memori sqlalchemy python-dotenv
```

## Example

The following agent uses Memori to maintain persistent memory across conversations with SQLite:

```python theme={null}
import os

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from dotenv import load_dotenv
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

from memori import Memori

load_dotenv()

db_path = os.getenv("DATABASE_PATH", "memori_agno.db")
engine = create_engine(f"sqlite:///{db_path}")
Session = sessionmaker(bind=engine)

model = OpenAIResponses(id="gpt-5.2")

mem = Memori(conn=Session).agno.register(openai_chat=model)
mem.attribution(entity_id="customer-456", process_id="support-agent")
mem.config.storage.build()

agent = Agent(
    model=model,
    instructions=[
        "You are a helpful customer support agent.",
        "Remember customer preferences and history from previous conversations.",
    ],
    markdown=True,
)

if __name__ == "__main__":
    print("Customer: Hi, I'd like to order a large pepperoni pizza with extra cheese")
    response1 = agent.run(
        "Hi, I'd like to order a large pepperoni pizza with extra cheese"
    )
    print(f"Agent: {response1.content}\n")

    print("Customer: Actually, can you remind me what I just ordered?")
    response2 = agent.run("Actually, can you remind me what I just ordered?")
    print(f"Agent: {response2.content}\n")

    print("Customer: Perfect! And what size was that again?")
    response3 = agent.run("Perfect! And what size was that again?")
    print(f"Agent: {response3.content}")
```

## Key Features

* **LLM Agnostic**: OpenAI, Anthropic, Bedrock, Gemini, Grok (xAI) - all modes (streamed, unstreamed, sync, async)
* **Smart Attribution**: Track memories by entity (e.g., customer) and process (e.g., support agent)
* **Advanced Augmentation**: AI-powered memory augmentation with no latency impact
* **Database Flexibility**: Supports PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, CockroachDB, Neon, Supabase, Oracle, and more

## Setup

1. **Create Database Engine**: Use SQLAlchemy to create a database connection
2. **Initialize Memori**: Create a Memori instance with the database session
3. **Register with Model**: Register Memori with your Agno agent using `.agno.register()`
4. **Set Attribution**: Define entity and process IDs for memory tracking
5. **Build Storage**: Initialize the database schema with `.config.storage.build()`

## Developer Resources

* [Memori SDK Documentation](https://memorilabs.ai/docs/)
* [Memori GitHub Repository](https://github.com/MemoriLabs/Memori)