---
title: Custom Fact Extraction Prompt
description: Tailor fact extraction so Mem0 stores only the details you care about.
icon: "wand-magic-sparkles"
---
Custom fact extraction prompts let you decide exactly which facts Mem0 records from a conversation. Define a focused prompt, give a few examples, and Mem0 will add only the memories that match your use case.
**You’ll use this when…**
- A project needs domain-specific facts (order numbers, customer info) without storing casual chatter.
- You already have a clear schema for memories and want the LLM to follow it.
- You must prevent irrelevant details from entering long-term storage.
Prompts that are too broad cause unrelated facts to slip through. Keep instructions tight and test them with real transcripts.
---
## Feature anatomy
- **Prompt instructions:** Describe which entities or phrases to keep. Specific guidance keeps the extractor focused.
- **Few-shot examples:** Show positive and negative cases so the model copies the right format.
- **Structured output:** Responses return JSON with a `facts` array that Mem0 converts into individual memories.
- **LLM configuration:** `custom_fact_extraction_prompt` (Python) or `customPrompt` (TypeScript) lives alongside your model settings.
1. State the allowed fact types.
2. Include short examples that mirror production messages.
3. Show both empty (`[]`) and populated outputs.
4. Remind the model to return JSON with a `facts` key only.
---
## Configure it
### Write the custom prompt
```python Python
custom_fact_extraction_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.
"""
```
```ts 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.
`;
```
Keep example pairs short and mirror the capitalization, punctuation, and tone you see in real user messages.
### Load the prompt in configuration
```python Python
from mem0 import Memory
config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4.1-nano-2025-04-14",
"temperature": 0.2,
"max_tokens": 2000,
}
},
"custom_fact_extraction_prompt": custom_fact_extraction_prompt,
"version": "v1.1"
}
m = Memory.from_config(config_dict=config)
```
```ts 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);
```
After initialization, run a quick `add` call with a known example and confirm the response splits into separate facts.
---
## See it in action
### Example: Order support memory
```python Python
m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
```
```ts 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": []
}
```
The output contains only the facts described in your prompt, each stored as a separate memory entry.
### Example: Irrelevant message filtered out
```python Python
m.add("I like going to hikes", user_id="alice")
```
```ts TypeScript
await memory.add("I like going to hikes", { userId: "user123" });
```
```json Output
{
"results": [],
"relations": []
}
```
Empty `results` show the prompt successfully ignored content outside your target domain.
---
## Verify the feature is working
- Log every call during rollout and confirm the `facts` array matches your schema.
- Check that unrelated messages return an empty `results` array.
- Run regression samples whenever you edit the prompt to ensure previously accepted facts still pass.
---
## Best practices
1. **Be precise:** Call out the exact categories or fields you want to capture.
2. **Show negative cases:** Include examples that should produce `[]` so the model learns to skip them.
3. **Keep JSON strict:** Avoid extra keys; only return `facts` to simplify downstream parsing.
4. **Version prompts:** Track prompt changes with a version number so you can roll back quickly.
5. **Review outputs regularly:** Spot-check stored memories to catch drift early.
---
Refresh how Mem0 stores memories and how prompts influence fact creation.
Apply custom extraction to route customer requests in a full workflow.