MongoDB

MongoDB stores data as flexible JSON-like documents. Memori integrates through PyMongo, giving you a NoSQL option for AI agent memory.

Install

pip install memori pymongo

Quick Start

from memori import Memori
from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017")

def get_db():
    return client["memori_db"]

mem = Memori(conn=get_db)
mem.config.storage.build()

Connection Strings

Environment	Connection String
Local	`mongodb://localhost:27017`
With Auth	`mongodb://user:password@localhost:27017`
Atlas (SRV)	`mongodb+srv://user:password@cluster.mongodb.net`
Replica Set	`mongodb://host1:27017,host2:27017/?replicaSet=myRS`

Complete Example

import os
from pymongo import MongoClient
from memori import Memori
from openai import OpenAI

mongo_client = MongoClient("mongodb://localhost:27017")

def get_db():
    return mongo_client["memori_db"]

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
mem = Memori(conn=get_db).llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")
mem.config.storage.build()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "I love hiking in the Rockies."}]
)
print(response.choices[0].message.content)

mem.augmentation.wait()
facts = mem.recall("hobbies and outdoor activities")
print(facts)

MongoDB Atlas

from pymongo import MongoClient

mongo_client = MongoClient(
    "mongodb+srv://user:password@cluster.mongodb.net"
    "/?retryWrites=true&w=majority"
)

def get_db():
    return mongo_client["memori_db"]

Connection Pooling

PyMongo manages its own connection pool:

mongo_client = MongoClient(
    "mongodb://localhost:27017",
    maxPoolSize=50,
    minPoolSize=5,
    maxIdleTimeMS=30000
)