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
TypeScript SDK
npm install @memorilabs/memoriPython SDK
pip install memoriSign 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"