Advanced Memory Operations - Mem0

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

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"])

TypeScript

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

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

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.",
)

TypeScript

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

Delete scoped memories

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

TypeScript

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.

Tune Metadata Filtering

Explore Reranker Search