Integrations
AWS Bedrock
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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:
Set environment variables:
Be sure to configure your AWS credentials using environment variables, IAM roles, or the AWS CLI.
Initialize Mem0 Integration
Import necessary modules and configure Mem0:
Memory Operations
Use Mem0 with your Bedrock-powered LLM and OpenSearch storage backend:
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