AWS Bedrock - Mem0 — ContextQMD

Setup

Before using the AWS Bedrock LLM, make sure you have the appropriate model access from Bedrock Console.
Model availability is per-region. anthropic.claude-sonnet-4-20250514-v1:0 supports on-demand inference in us-east-1 and ap-southeast-4; from any other region, use the cross-region inference profile ID us.anthropic.claude-sonnet-4-20250514-v1:0 instead.
Install the AWS SDK for your language: pip install boto3 (Python) or npm install @aws-sdk/client-bedrock-runtime (TypeScript).
Both SDKs fall back to the standard AWS credential chain (environment variables, ~/.aws/credentials, or an attached IAM role), so exporting AWS_REGION, AWS_ACCESS_KEY_ID, and AWS_SECRET_ACCESS_KEY is the quickest way to get started. In TypeScript you can also pass credentials inline with awsRegion, awsAccessKeyId, awsSecretAccessKey, and awsSessionToken, as shown below.

Usage

<CodeGroup> ```python Python import os from mem0 import Memory

os.environ['AWS_REGION'] = 'us-east-1' os.environ["AWS_ACCESS_KEY_ID"] = "xx" os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"

config = { "llm": { "provider": "aws_bedrock", "config": { "model": "anthropic.claude-sonnet-4-20250514-v1:0", "temperature": 0.2, "max_tokens": 2000, } } }

m = Memory.from_config(config) 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."} ] m.add(messages, user_id="alice", metadata={"category": "movies"})


```typescript TypeScript
import { Memory } from 'mem0ai/oss';

const config = {
  llm: {
    provider: 'aws_bedrock',
    config: {
      model: 'anthropic.claude-sonnet-4-20250514-v1:0',
      temperature: 0.2,
      maxTokens: 2000,
      // Optional. Omit these to use the default AWS credential chain.
      awsRegion: process.env.AWS_REGION,
      awsAccessKeyId: process.env.AWS_ACCESS_KEY_ID,
      awsSecretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
    },
  },
};

const memory = new Memory(config);
const 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."}
];
await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });

</CodeGroup> <Note> `@aws-sdk/client-bedrock-runtime` is an optional peer dependency of `mem0ai`, so npm will not install it for you. The TypeScript provider loads it lazily and throws a clear error on the first request if the package is missing. </Note> <Note> The TypeScript provider calls the Bedrock [Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), a single uniform interface across the current Bedrock model families. Streaming and `InvokeModel`-only models are not supported yet. </Note>

Config

All available parameters for the aws_bedrock config are present in Master List of All Params in Config.