Bedrock with Persistent Memory

Pair Mem0 with AWS Bedrock and OpenSearch for a managed stack.

This example demonstrates how to configure and use the mem0ai SDK with AWS Bedrock and OpenSearch Service (AOSS) for persistent memory capabilities in Python.

Installation

Install the required dependencies to include the Amazon data stack, including boto3, opensearch-py, and langchain-aws:

pip install "mem0ai[extras]"

Environment Setup

Set your AWS environment variables:

import os

# Set these in your environment or notebook
os.environ['AWS_REGION'] = 'us-west-2'
os.environ['AWS_ACCESS_KEY_ID'] = 'AK00000000000000000'
os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS00000000000000000'

# Confirm they are set
print(os.environ['AWS_REGION'])
print(os.environ['AWS_ACCESS_KEY_ID'])
print(os.environ['AWS_SECRET_ACCESS_KEY'])

Configuration and Usage

This sets up Mem0 with:

• AWS Bedrock for LLM
• AWS Bedrock for embeddings
• OpenSearch as the vector store

import boto3
from opensearchpy import RequestsHttpConnection, AWSV4SignerAuth
from mem0 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": "us.anthropic.claude-3-7-sonnet-20250219-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,
            "connection_class": RequestsHttpConnection,
            "pool_maxsize": 20,
            "use_ssl": True,
            "verify_certs": True,
            "embedding_model_dims": 1024,
        }
    },
}

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

Usage

Add a memory

messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about 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"})

Search a memory

relevant_memories = m.search(query, filters={"user_id": "alice"})

Get all memories

all_memories = m.get_all(filters={"user_id": "alice"})

Get a specific memory

memory = m.get(memory_id)

Conclusion

With Mem0 and AWS services like Bedrock and OpenSearch, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.

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Memory Evaluation

Understand how Mem0’s memory system is benchmarked and evaluated.

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