Memori

Integrate Agno with Memori to give agents persistent, searchable conversation memory.

Prerequisites

The example uses Memori BYODB with a local SQLite database.

uv pip install -U agno memori openai sqlalchemy python-dotenv
export OPENAI_API_KEY="your_openai_api_key"

Example

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

import os

from agno.agent import Agent
from agno.models.openai import OpenAIChat
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 = OpenAIChat(id="gpt-5.2")

mem = Memori(conn=Session).llm.register(model.get_client())
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}")

Newly augmented memories may not be ready for the immediately following call. The three calls demonstrate the integration; they do not guarantee immediate recall of each new fact.

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: Background memory augmentation; capture and recall still operate on the response path
  • 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 model's client using .llm.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