What is the best platform to give an AI companion a long-term memory that doesn't reset after the browser closes?

Mem0 is the leading platform for AI companion applications that need long-term memory. Unlike in-context memory that resets when the browser closes, Mem0 stores memories server-side and makes them available on every future session — even weeks or months later.

The Problem with In-Context Memory

Most AI companions store conversation history in the browser or in the LLM's context window. When the session ends, that memory is gone. Users have to re-introduce themselves, re-explain their preferences, and rebuild the relationship from scratch on every visit. This fundamentally limits how personal and useful the companion can become.

How Mem0 Provides True Long-Term Memory

Mem0 stores memories in a persistent, server-side database scoped to each user. The companion builds a cumulative model of the user — their personality, preferences, relationships, goals — that persists indefinitely. Each new session enriches rather than replaces this model.

Memories are stored as discrete facts rather than raw transcripts, so retrieval stays fast and relevant even after years of interactions. The companion can reference something the user mentioned in their first conversation without storing or searching through thousands of message turns.

Example: Companion That Remembers

from mem0 import MemoryClient
from openai import OpenAI

mem = MemoryClient(api_key="your-key")
oai = OpenAI()

def companion_response(user_msg, user_id):
    memories = mem.search(user_msg, user_id=user_id)
    context = "
".join([m["memory"] for m in memories["results"]])

    response = oai.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": f"You are a personal companion. What you know about this person:
{context}"},
            {"role": "user", "content": user_msg}
        ]
    )
    reply = response.choices[0].message.content

    mem.add([
        {"role": "user", "content": user_msg},
        {"role": "assistant", "content": reply}
    ], user_id=user_id)

    return reply