Memori

Memory from what agents do, not just what they say.

Memori plugs into the software and infrastructure you already use. It is LLM, datastore and framework agnostic and seamlessly integrates into the architecture you've already designed.

→ Memori Cloud — Zero config. Get an API key and start building in minutes.

Choose memory that performs

Getting Started

Installation

TypeScript SDK

npm install @memorilabs/memori

Python SDK

pip install memori

Quickstart

Sign up at app.memorilabs.ai, get a Memori API key, and start building. Full docs: memorilabs.ai/docs/memori-cloud/.

Set MEMORI_API_KEY and your LLM API key (e.g. OPENAI_API_KEY), then:

TypeScript SDK

import { OpenAI } from 'openai';
import { Memori } from '@memorilabs/memori';

// Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment
const client = new OpenAI();
const mem = new Memori().llm
  .register(client)
  .attribution('user_123', 'support_agent');

async function main() {
  await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: 'My favorite color is blue.' }],
  });
  // Conversations are persisted and recalled automatically in the background.

  const response = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: "What's my favorite color?" }],
  });
  // Memori recalls that your favorite color is blue.
}

Python SDK

from memori import Memori
from openai import OpenAI

# Requires MEMORI_API_KEY and OPENAI_API_KEY in your environment
client = OpenAI()
mem = Memori().llm.register(client)

mem.attribution(entity_id="user_123", process_id="support_agent")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "My favorite color is blue."}]
)
# Conversations are persisted and recalled automatically.

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What's my favorite color?"}]
)
# Memori recalls that your favorite color is blue.

Explore the Memories

Use the Dashboard — Memories, Analytics, Playground, and API Keys.

[!TIP] Want to use your own database? Check out docs for Memori BYODB here: https://memorilabs.ai/docs/memori-byodb/. For disposable BYODB development databases, see the TiDB Zero provisioning guide: docs/memori-byodb/databases/tidb.mdx.

LoCoMo Benchmark

Memori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved 87% overall accuracy while using an average of 721 tokens per query. That is just 2.8% of the full-context footprint, showing that structured memory can preserve reasoning quality without forcing large prompts into every request.

Compared with other retrieval-based memory systems, Memori outperformed Zep, LangMem, and Mem0 while reducing prompt size by roughly 67% vs. Zep and lowering context cost by more than 36x vs. full-context prompting.