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Bedrock with Persistent Memory

Welcome Mem0 Platform Open Source Cookbooks Integrations API Reference Release Notes

• Documentation

Getting Started

• Overview

Essentials

• Build a Companion with Mem0
• Partition Memories by Entity
• Control Memory Ingestion
• Set Memory Expiration
• Tag and Organize Memories
• Export Stored Memories
• Choose Vector vs Graph Memory

Companion Playbooks

• Interactive Memory Demo
• Build a Node.js Companion
• Personalized AI Tutor
• Smart Travel Assistant
• Research Assistant for YouTube
• Voice-First AI Companion
• Self-Hosted AI Companion

Ops & Automations

• Memory-Powered Support Agent
• Automated Email Intelligence
• Content Creation Workflow
• Multi-Session Research Agent
• Collaborative Task Assistant

Integrations & Platforms

• Memory-Powered Agent SDK
• Memory as OpenAI Tool
• Persistent Mastra Agents
• Healthcare Coach with ADK
• Bedrock with Persistent Memory
• Graph Memory on Neptune
• Search with Personal Context

Frameworks & Multimodal

• ReAct Agents with Memory
• Multi-Agent Collaboration
• Visual Memory Retrieval
• Persistent Eliza Characters
• Browser Extension Memory
• Gemini 3 with Mem0 MCP
• MiroFish Swarm Memory

On this page

• Installation
• Environment Setup
• Configuration and Usage
• Usage
• Add a memory
• Search a memory
• Get all memories
• Get a specific memory
• Conclusion

This example demonstrates how to configure and use the mem0ai SDK with AWS Bedrock, OpenSearch Service (AOSS), and AWS Neptune Analytics 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[graph,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
• Graph Memory guide

import boto3
from opensearchpy import 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": "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,
        }
    },
    "graph_store": {
        "provider": "neptune",
        "config": {
            "endpoint": f"neptune-graph://my-graph-identifier",
        },
    },
}

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

​ Usage

Reference Notebook example

​ 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, user_id="alice")

​ Get all memories

all_memories = m.get_all(user_id="alice")

​ Get a specific memory

memory = m.get(memory_id)

​ Conclusion

With Mem0 and AWS services like Bedrock, OpenSearch, and Neptune Analytics, 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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Neptune Analytics with Mem0 \ \ Explore graph-based memory storage with AWS Neptune Analytics.

Graph Memory Features \ \ Learn how to leverage knowledge graphs for entity relationships.

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