This integration demonstrates how to use Mem0 with AWS Bedrock and Amazon OpenSearch Service (AOSS) to enable persistent, semantic memory in intelligent agents.

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Overview

In this guide, you’ll:
  1. Configure AWS credentials to enable Bedrock and OpenSearch access
  2. Set up the Mem0 SDK to use Bedrock for embeddings and LLM
  3. Store and retrieve memories using OpenSearch as a vector store
  4. Build memory-aware applications with scalable cloud infrastructure

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Prerequisites

  • AWS account with access to:
    • Bedrock foundation models (e.g., Titan, Claude)
    • OpenSearch Service with a configured domain
  • Python 3.8+
  • Valid AWS credentials (via environment or IAM role)

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Setup and Installation

Install required packages: 
pip install mem0ai boto3 opensearch-py
Set environment variables: Be sure to configure your AWS credentials using environment variables, IAM roles, or the AWS CLI. 
import os

os.environ['AWS_REGION'] = 'us-west-2'
os.environ['AWS_ACCESS_KEY_ID'] = 'AKIA...'
os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS...'

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Initialize Mem0 Integration

Import necessary modules and configure Mem0: 
import boto3
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
from mem0.memory.main import Memory

region = 'us-west-2'
service = 'aoss'
credentials = boto3.Session().get_credentials()
auth = AWSV4SignerAuth(credentials, region, service)

config = {
    "embedder": {
        "provider": "aws_bedrock",
        "config": {
            "model": "amazon.titan-embed-text-v2:0"
        }
    },
    "llm": {
        "provider": "aws_bedrock",
        "config": {
            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
            "temperature": 0.1,
            "max_tokens": 2000
        }
    },
    "vector_store": {
        "provider": "opensearch",
        "config": {
            "collection_name": "mem0",
            "host": "your-opensearch-domain.us-west-2.es.amazonaws.com",
            "port": 443,
            "http_auth": auth,
            "embedding_model_dims": 1024,
            "connection_class": RequestsHttpConnection,
            "pool_maxsize": 20,
            "use_ssl": True,
            "verify_certs": True
        }
    }
}

# Initialize memory system
m = Memory.from_config(config)

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Memory Operations

Use Mem0 with your Bedrock-powered LLM and OpenSearch storage backend: 
# Store conversational context
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller?"},
    {"role": "user", "content": "I prefer sci-fi."},
    {"role": "assistant", "content": "Noted! I'll suggest sci-fi movies next time."}
]

m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})

# Search for memory
relevant = m.search("What kind of movies does Alice like?", user_id="alice")

# Retrieve all user memories
all_memories = m.get_all(user_id="alice")

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Key Features

  1. Serverless Memory Embeddings: Use Titan or other Bedrock models for fast, cloud-native embeddings
  2. Scalable Vector Search: Store and retrieve vectorized memories via OpenSearch
  3. Seamless AWS Auth: Uses AWS IAM or environment variables to securely authenticate
  4. User-specific Memory Spaces: Memories are isolated per user ID
  5. Persistent Memory Context: Maintain and recall history across sessions

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