mem0/docs/llms.mdx

---
title: 🤖 Overview
---

## Overview

Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.

<CardGroup cols={4}>
  <Card title="OpenAI" href="#openai"></Card>
  <Card title="Groq" href="#groq"></Card>
  <Card title="Together" href="#together"></Card>
  <Card title="AWS Bedrock" href="#aws_bedrock"></Card>
  <Card title="Litellm" href="#litellm"></Card>
</CardGroup>

## OpenAI

To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).

Once you have obtained the key, you can use it like this:

```python
import os
from mem0 import Memory

os.environ['OPENAI_API_KEY'] = 'xxx'

config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o",
            "temperature": 0.2,
            "max_tokens": 1500,
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

## Groq

[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.

In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.

```python
import os
from mem0 import Memory

os.environ['GROQ_API_KEY'] = 'xxx'

config = {
    "llm": {
        "provider": "groq",
        "config": {
            "model": "mixtral-8x7b-32768",
            "temperature": 0.1,
            "max_tokens": 1000,
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

## TogetherAI

To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).

Once you have obtained the key, you can use it like this:

```python
import os
from mem0 import Memory

os.environ['TOGETHER_API_KEY'] = 'xxx'

config = {
    "llm": {
        "provider": "togetherai",
        "config": {
            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
            "temperature": 0.2,
            "max_tokens": 1500,
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

## AWS Bedrock

### 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).
- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.

```python
import os
from mem0 import Memory

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

config = {
    "llm": {
        "provider": "aws_bedrock",
        "config": {
            "model": "arn:aws:bedrock:us-east-1:123456789012:model/your-model-name",
            "temperature": 0.2,
            "max_tokens": 1500,
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

## Litellm

[Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models]((https://litellm.vercel.app/docs/providers)) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.

```python
import os
from mem0 import Memory

config = {
    "llm": {
        "provider": "litellm",
        "config": {
            "model": "gpt-3.5-turbo",
            "temperature": 0.2,
            "max_tokens": 1500,
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```