Async Patterns

Memori works with async/await out of the box in both Python and TypeScript. In Python, use AsyncOpenAI or AsyncAnthropic instead of their sync counterparts — everything else stays the same. TypeScript is natively async.

When to Use Async

Scenario	Python	TypeScript	Why
Web servers	Yes	Default	Concurrent request handling
Chatbots with many users	Yes	Default	Non-blocking I/O
CLI scripts	No	Default	Sync is simpler in Python
Jupyter notebooks	No	—	Event loop already running

TypeScript is natively async — all Memori SDK calls return Promises. No special async client or asyncio setup is needed.

Basic Async Setup

Async Setup

import asyncio
from memori import Memori
from openai import AsyncOpenAI

client = AsyncOpenAI()

mem = Memori().llm.register(client)
mem.attribution(entity_id="user_123", process_id="async_agent")

async def main():
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "I prefer async Python."}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

Web Server Example

Web Server

import os
from fastapi import FastAPI
from pydantic import BaseModel
from memori import Memori
from openai import AsyncOpenAI

app = FastAPI()

class ChatRequest(BaseModel):
    message: str

@app.post("/chat/{user_id}")
async def chat(user_id: str, req: ChatRequest):
    client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    mem = Memori().llm.register(client)
    mem.attribution(entity_id=user_id, process_id="fastapi_async")

    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": req.message}]
    )
    return {"response": response.choices[0].message.content}