> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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**:

```bash theme={null}
pip install "mem0ai[extras]"
```

## Environment Setup

Set your AWS environment variables:

```python theme={null}
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](https://docs.mem0.ai/components/llms/models/aws_bedrock)
* [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
* [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)

```python theme={null}
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

```python theme={null}
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

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

### Get all memories

```python theme={null}
all_memories = m.get_all(filters={"user_id": "alice"})
```

### Get a specific memory

```python theme={null}
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.

***

<CardGroup cols={2}>
  <Card title="Memory Evaluation" icon="chart-line" href="/core-concepts/memory-evaluation">
    Understand how Mem0's memory system is benchmarked and evaluated.
  </Card>
</CardGroup>