---
title: Custom Prompts
description: 'Enhance your product experience by adding custom prompts tailored to your needs'
icon: "pencil"
iconType: "solid"
---

## Introduction to Custom Prompts

Custom prompts allow you to tailor the behavior of your Mem0 instance to specific use cases or domains. 
By defining a custom prompt, you can control how information is extracted, processed, and stored in your memory system.

To create an effective custom prompt:
1. Be specific about the information to extract.
2. Provide few-shot examples to guide the LLM.
3. Ensure examples follow the format shown below.

Example of a custom prompt:

<CodeGroup>
```python Python
custom_prompt = """
Please only extract entities containing customer support information, order details, and user information. 
Here are some few shot examples:

Input: Hi.
Output: {{"facts" : []}}

Input: The weather is nice today.
Output: {{"facts" : []}}

Input: My order #12345 hasn't arrived yet.
Output: {{"facts" : ["Order #12345 not received"]}}

Input: I'm John Doe, and I'd like to return the shoes I bought last week.
Output: {{"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}}

Input: I ordered a red shirt, size medium, but received a blue one instead.
Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}}

Return the facts and customer information in a json format as shown above.
"""
```

```typescript TypeScript
const customPrompt = `
Please only extract entities containing customer support information, order details, and user information. 
Here are some few shot examples:

Input: Hi.
Output: {"facts" : []}

Input: The weather is nice today.
Output: {"facts" : []}

Input: My order #12345 hasn't arrived yet.
Output: {"facts" : ["Order #12345 not received"]}

Input: I am John Doe, and I would like to return the shoes I bought last week.
Output: {"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}

Input: I ordered a red shirt, size medium, but received a blue one instead.
Output: {"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}

Return the facts and customer information in a json format as shown above.
`;
```
</CodeGroup>

Here we initialize the custom prompt in the config:

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

config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o",
            "temperature": 0.2,
            "max_tokens": 2000,
        }
    },
    "custom_prompt": custom_prompt,
    "version": "v1.1"
}

m = Memory.from_config(config_dict=config, user_id="alice")
```

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

const config = {
  version: 'v1.1',
  llm: {
    provider: 'openai',
    config: {
      apiKey: process.env.OPENAI_API_KEY || '',
      model: 'gpt-4-turbo-preview',
      temperature: 0.2,
      maxTokens: 1500,
    },
  },
  customPrompt: customPrompt
};

const memory = new Memory(config);
```
</CodeGroup>

### Example 1

In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.

<CodeGroup>
```python Python
m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
```

```typescript TypeScript
await memory.add('Yesterday, I ordered a laptop, the order id is 12345', { userId: "user123" });
```

```json Output
{
  "results": [
    {
      "memory": "Ordered a laptop",
      "event": "ADD"
    },
    {
      "memory": "Order ID: 12345",
      "event": "ADD"
    },
    {
      "memory": "Order placed yesterday",
      "event": "ADD"
    }
  ],
  "relations": []
}
```
</CodeGroup>

### Example 2

In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
Hence, the memory is not added.

<CodeGroup>
```python Python
m.add("I like going to hikes", user_id="alice")
```

```typescript TypeScript
await memory.add('I like going to hikes', { userId: "user123" });
```

```json Output
{
  "results": [],
  "relations": []
}
```
</CodeGroup>

The custom prompt will process both the user and assistant messages to extract relevant information according to the defined format.