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Overview

The add operation is how you store memory into Mem0. Whether you’re working with a chatbot, a voice assistant, or a multi-agent system, this is the entry point to create long-term memory. Memories typically come from a user-assistant interaction and Mem0 handles the extraction, transformation, and storage for you. Mem0 offers two implementation flows:
  • Mem0 Platform (Managed, scalable, with dashboard + API)
  • Mem0 Open Source (Lightweight, fully local, flexible SDKs)
Each supports the same core memory operations, but with slightly different setup. Below, we walk through examples for both.

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Architecture

When you call add, Mem0 performs the following steps under the hood:
  1. Information Extraction The input messages are passed through an LLM that extracts key facts, decisions, preferences, or events worth remembering.
  2. Conflict Resolution Mem0 compares the new memory against existing ones to detect duplication or contradiction and handles updates accordingly.
  3. Memory Storage The result is stored in a vector database (for semantic search) and optionally in a graph structure (for relationship mapping).
You don’t need to handle any of this manually, Mem0 takes care of it with a single API call or SDK method.

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Example: Mem0 Platform

from mem0 import MemoryClient

client = MemoryClient(api_key="your-api-key")

messages = [
    {"role": "user", "content": "I'm planning a trip to Tokyo next month."},
    {"role": "assistant", "content": "Great! I’ll remember that for future suggestions."}
]

client.add(
    messages=messages,
    user_id="alice",
    version="v2"
)

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Example: Mem0 Open Source

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your-api-key"

m = Memory()

messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]

# Store inferred memories (default behavior)
result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})

# Optionally store raw messages without inference
result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)

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When Should You Add Memory?

Add memory whenever your agent learns something useful:
  • A new user preference is shared
  • A decision or suggestion is made
  • A goal or task is completed
  • A new entity is introduced
  • A user gives feedback or clarification
Storing this context allows the agent to reason better in future interactions.

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More Details

For full list of supported fields, required formats, and advanced options, see the Add Memory API Reference.

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