Make Platform Memory Operations Smarter

Prerequisites

Platform workspace with API key
Python 3.10+ and Node.js 18+

Need a refresher on the core concepts first? Review the Add Memory overview, then come back for the advanced flow.

Install and authenticate

Python
TypeScript

Install the SDK

pip install mem0ai

Export your API key

export MEM0_API_KEY="sk-platform-..."

Create an async client

import os
from mem0 import AsyncMemoryClient

memory = AsyncMemoryClient(api_key=os.environ["MEM0_API_KEY"])

Install the OSS SDK

npm install mem0ai

Load your API key

export MEM0_API_KEY="sk-platform-..."

Instantiate the client

import MemoryClient from 'mem0ai';

const memory = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });

Add memories with metadata

Python
TypeScript

Record conversations with metadata

conversation = [
    {"role": "user", "content": "I'm Morgan, planning a 3-week trip to Japan in May."},
    {"role": "assistant", "content": "Great! I'll track dietary notes and cities you mention."},
    {"role": "user", "content": "Please remember I avoid shellfish and prefer boutique hotels in Tokyo."},
]

result = await memory.add(
    conversation,
    user_id="traveler-42",
    metadata={"trip": "japan-2025", "preferences": ["boutique", "no-shellfish"]},
    run_id="planning-call-1",
)

Capture context-rich memories

const conversation = [
  { role: "user", content: "I'm Morgan, planning a 3-week trip to Japan in May." },
  { role: "assistant", content: "Great! I'll track dietary notes and cities you mention." },
  { role: "user", content: "Please remember I avoid shellfish and love boutique hotels in Tokyo." },
];

const result = await memory.add(conversation, {
  userId: "traveler-42",
  metadata: { trip: "japan-2025", preferences: ["boutique", "no-shellfish"] },
  runId: "planning-call-1",
});

Successful calls return memories tagged with the metadata you passed. In the dashboard, verify the trip=japan-2025 tag exists on the new memory.

Retrieve and refine

Python
TypeScript

Filter by metadata + reranker

matches = await memory.search(
    "Any food alerts?",
    filters={"user_id": "traveler-42", "metadata.trip": "japan-2025"},
    rerank=True,
)

Update a memory inline

await memory.update(
    memory_id=matches["results"][0]["id"],
    text="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
)

Search with metadata filters

const matches = await memory.search("Any food alerts?", {
  filters: { user_id: "traveler-42", "metadata.trip": "japan-2025" },
  rerank: true,
});

Apply an update

await memory.update(matches.results[0].id, {
  text: "Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
});

Clean up

Python
TypeScript

Delete scoped memories

await memory.delete_all(user_id="traveler-42", run_id="planning-call-1")

Remove the run

await memory.deleteAll({ userId: "traveler-42", runId: "planning-call-1" });

Quick recovery

Empty results with filters: log filters values and confirm metadata keys match (case-sensitive).

Metadata keys become part of your filtering schema. Stick to lowercase snake_case (trip_id, preferences) to avoid collisions down the road.

Getting Started

Core Concepts

Features

Support

Migration

Advanced Memory Operations

Make Platform Memory Operations Smarter

Install and authenticate

Add memories with metadata

Retrieve and refine

Clean up

Quick recovery

Tune Metadata Filtering

Explore Reranker Search

​Make Platform Memory Operations Smarter

​Install and authenticate

​Add memories with metadata

​Retrieve and refine

​Clean up

​Quick recovery

Tune Metadata Filtering

Explore Reranker Search

Make Platform Memory Operations Smarter

Install and authenticate

Add memories with metadata

Retrieve and refine

Clean up

Quick recovery