Supported LLM Providers

Quick Start Examples

OpenAI

from memori import Memori
from openai import OpenAI
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

client = OpenAI()

mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

OpenAI Responses Client

from memori import Memori
from openai import OpenAI
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

client = OpenAI()

mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")

response = client.responses.create(
    model="gpt-4o-mini",
    input="Hello!",
    instructions="You are a helpful assistant."
)

Anthropic

from anthropic import Anthropic
from memori import Memori
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///anthropic_demo.db")
SessionLocal = sessionmaker(bind=engine)

client = Anthropic()

mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="claude_assistant")

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}]
)

Google

import os

from memori import Memori
import google.generativeai as genai
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///gemini_demo.db")
SessionLocal = sessionmaker(bind=engine)

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
client = genai.GenerativeModel("gemini-2.0-flash-exp")

mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="gemini_assistant")

response = client.generate_content("Hello")

LangChain

from langchain_openai import ChatOpenAI
from memori import Memori
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///langchain_demo.db")
SessionLocal = sessionmaker(bind=engine)

client = ChatOpenAI(model="gpt-4o-mini")

mem = Memori(conn=SessionLocal).llm.register(chatopenai=client)
mem.attribution(entity_id="user_123", process_id="langchain_agent")

response = client.invoke("Hello")

Pydantic AI

from memori import Memori
from pydantic_ai import Agent
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///pydantic_demo.db")
SessionLocal = sessionmaker(bind=engine)

agent = Agent("openai:gpt-4o-mini")

mem = Memori(conn=SessionLocal).llm.register(agent)
mem.attribution(entity_id="user_123", process_id="pydantic_agent")

result = agent.run_sync("Hello")

Nebius AI Studio

from memori import Memori
from openai import OpenAI
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine("sqlite:///memori.db")
SessionLocal = sessionmaker(bind=engine)

client = OpenAI(
    base_url="https://api.studio.nebius.com/v1/",
    api_key=os.getenv("NEBIUS_API_KEY"),
)

mem = Memori(conn=SessionLocal).llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)