> ## 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.

# AWS Bedrock

> Configure AWS Bedrock as an LLM provider in Mem0 with IAM authentication and Claude model support.

### Setup

* Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
* 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 theme={null}
  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 theme={null}
  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](../config).