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This integration demonstrates how to use Mem0 with AWS Bedrock and Amazon OpenSearch Service (AOSS) to enable persistent, semantic memory in intelligent agents.
Overview
In this guide, you’ll:
- Configure AWS credentials to enable Bedrock and OpenSearch access
- Set up the Mem0 SDK to use Bedrock for embeddings and LLM
- Store and retrieve memories using OpenSearch as a vector store
- Build memory-aware applications with scalable cloud infrastructure
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)
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...'
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)
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")
Key Features
- Serverless Memory Embeddings: Use Titan or other Bedrock models for fast, cloud-native embeddings
- Scalable Vector Search: Store and retrieve vectorized memories via OpenSearch
- Seamless AWS Auth: Uses AWS IAM or environment variables to securely authenticate
- User-specific Memory Spaces: Memories are isolated per user ID
- Persistent Memory Context: Maintain and recall history across sessions
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