fix(sdk): removing deprecating param from our sdk and docs changes with it (#4740)
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Kartik
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---
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title: Custom Instructions
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description: Tailor fact extraction so Mem0 stores only the details you care about.
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icon: "wand-magic-sparkles"
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---
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Custom instructions 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.
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<Info>
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**You'll use this when...**
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- A project needs domain-specific facts (order numbers, customer info) without storing casual chatter.
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- You already have a clear schema for memories and want the LLM to follow it.
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- You must prevent irrelevant details from entering long-term storage.
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</Info>
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<Warning>
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Prompts that are too broad cause unrelated facts to slip through. Keep instructions tight and test them with real transcripts.
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</Warning>
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<Note>
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The `custom_fact_extraction_prompt` parameter has been renamed to `custom_instructions`. If you are upgrading from an older version, update your configuration accordingly.
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</Note>
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---
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## Feature anatomy
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- **Prompt instructions:** Describe which entities or phrases to keep. Specific guidance keeps the extractor focused.
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- **Few-shot examples:** Show positive and negative cases so the model copies the right format.
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- **Structured output:** Responses return JSON with a `facts` array that Mem0 converts into individual memories.
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- **LLM configuration:** `custom_instructions` (Python) or `customInstructions` (TypeScript) lives alongside your model settings.
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<AccordionGroup>
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<Accordion title="Prompt blueprint">
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1. State the allowed fact types.
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2. Include short examples that mirror production messages.
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3. Show both empty (`[]`) and populated outputs.
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4. Remind the model to return JSON with a `facts` key only.
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</Accordion>
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</AccordionGroup>
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---
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## Configure it
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### Write the custom prompt
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<CodeGroup>
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```python Python
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custom_instructions = """
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Please only extract entities containing customer support information, order details, and user information.
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Here are some few shot examples:
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Input: Hi.
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Output: {"facts" : []}
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Input: The weather is nice today.
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Output: {"facts" : []}
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Input: My order #12345 hasn't arrived yet.
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Output: {"facts" : ["Order #12345 not received"]}
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Input: I'm John Doe, and I'd like to return the shoes I bought last week.
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Output: {"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}
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Input: I ordered a red shirt, size medium, but received a blue one instead.
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Output: {"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}
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Return the facts and customer information in a json format as shown above.
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"""
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```
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```ts TypeScript
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const customInstructions = `
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Please only extract entities containing customer support information, order details, and user information.
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Here are some few shot examples:
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Input: Hi.
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Output: {"facts" : []}
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Input: The weather is nice today.
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Output: {"facts" : []}
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Input: My order #12345 hasn't arrived yet.
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Output: {"facts" : ["Order #12345 not received"]}
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Input: I am John Doe, and I would like to return the shoes I bought last week.
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Output: {"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}
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Input: I ordered a red shirt, size medium, but received a blue one instead.
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Output: {"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}
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Return the facts and customer information in a json format as shown above.
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`;
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```
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</CodeGroup>
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<Tip>
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Keep example pairs short and mirror the capitalization, punctuation, and tone you see in real user messages.
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</Tip>
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### Load the prompt in configuration
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<CodeGroup>
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```python Python
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from mem0 import Memory
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config = {
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4.1-nano-2025-04-14",
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"temperature": 0.2,
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"max_tokens": 2000,
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}
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},
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"custom_instructions": custom_instructions,
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"version": "v1.1"
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}
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m = Memory.from_config(config_dict=config)
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```
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```ts TypeScript
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import { Memory } from "mem0ai/oss";
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const config = {
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version: "v1.1",
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llm: {
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provider: "openai",
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config: {
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apiKey: process.env.OPENAI_API_KEY ?? "",
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model: "gpt-4-turbo-preview",
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temperature: 0.2,
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maxTokens: 1500,
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},
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},
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customInstructions: customInstructions,
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};
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const memory = new Memory(config);
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```
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</CodeGroup>
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<Info icon="check">
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After initialization, run a quick `add` call with a known example and confirm the response splits into separate facts.
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</Info>
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---
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## See it in action
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### Example: Order support memory
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<CodeGroup>
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```python Python
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m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
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```
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```ts TypeScript
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await memory.add("Yesterday, I ordered a laptop, the order id is 12345", { userId: "user123" });
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```
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```json Output
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{
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"results": [
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{"memory": "Ordered a laptop", "event": "ADD"},
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{"memory": "Order ID: 12345", "event": "ADD"},
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{"memory": "Order placed yesterday", "event": "ADD"}
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],
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"relations": []
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}
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```
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</CodeGroup>
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<Info icon="check">
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The output contains only the facts described in your prompt, each stored as a separate memory entry.
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</Info>
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### Example: Irrelevant message filtered out
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<CodeGroup>
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```python Python
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m.add("I like going to hikes", user_id="alice")
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```
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```ts TypeScript
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await memory.add("I like going to hikes", { userId: "user123" });
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```
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```json Output
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{
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"results": [],
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"relations": []
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}
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```
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</CodeGroup>
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<Tip>
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Empty `results` show the prompt successfully ignored content outside your target domain.
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</Tip>
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---
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## Verify the feature is working
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- Log every call during rollout and confirm the `facts` array matches your schema.
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- Check that unrelated messages return an empty `results` array.
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- Run regression samples whenever you edit the prompt to ensure previously accepted facts still pass.
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---
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## Best practices
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1. **Be precise:** Call out the exact categories or fields you want to capture.
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2. **Show negative cases:** Include examples that should produce `[]` so the model learns to skip them.
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3. **Keep JSON strict:** Avoid extra keys; only return `facts` to simplify downstream parsing.
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4. **Version prompts:** Track prompt changes with a version number so you can roll back quickly.
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5. **Review outputs regularly:** Spot-check stored memories to catch drift early.
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---
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<CardGroup cols={2}>
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<Card title="Review Add Operations" icon="list" href="/core-concepts/memory-operations/add">
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Refresh how Mem0 stores memories and how prompts influence fact creation.
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</Card>
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<Card title="Automate Support Triage" icon="inbox" href="/cookbooks/operations/support-inbox">
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Apply custom extraction to route customer requests in a full workflow.
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</Card>
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</CardGroup>
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