---
title: Content Creation Workflow
description: "Store voice guidelines once and apply them across every draft."
---


This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.

## Why Use Mem0?

Integrating Mem0 into your writing workflow helps you:

1. **Store persistent writing preferences** ensuring consistent tone, formatting, and structure.
2. **Automate content refinement** by retrieving preferences when rewriting or reviewing content.
3. **Scale your writing style** so it applies consistently across multiple documents or sessions.

## Setup

<Tabs>
  <Tab title="Platform">
```python
import os
from openai import OpenAI
from mem0 import MemoryClient

os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"


# Set up Mem0 and OpenAI client
client = MemoryClient()
openai = OpenAI()

USER_ID = "content_writer"
RUN_ID = "smart_editing_session"
```
  </Tab>
  <Tab title="Open Source">
Here we use **Mem0 open source** (`Memory`): all local, no API keys needed for memory. Vectors in **Qdrant**, LLM and embeddings via **Ollama**. The **OpenAI** Python SDK calls Ollama's **OpenAI-compatible** `/v1` endpoint for content rewriting.

### Installation

Install the required dependencies:

```bash
pip install mem0ai qdrant-client openai ollama
```

Then start Qdrant and pull the Ollama models:

```bash
docker run -d -p 6333:6333 qdrant/qdrant
ollama pull llama3.1:latest
ollama pull nomic-embed-text:latest
```

<Note>You can swap `nomic-embed-text` for any Ollama-supported embedding model (e.g., `snowflake-arctic-embed`, `mxbai-embed-large`). Just update the `model` in the `embedder` config and set `embedding_model_dims` in the Qdrant config to match the model's output dimensions (768 for `nomic-embed-text`).</Note>

```python
from openai import OpenAI
from mem0 import Memory

OLLAMA_URL = "http://localhost:11434"
CHAT_MODEL = "llama3.1:latest"

# Set up Mem0 with local providers
memory = Memory.from_config({
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "content_writing",
            "host": "localhost",
            "port": 6333,
            "embedding_model_dims": 768,
        },
    },
    "llm": {
        "provider": "ollama",
        "config": {
            "model": CHAT_MODEL,
            "temperature": 0,
            "max_tokens": 2000,
            "ollama_base_url": OLLAMA_URL,
        },
    },
    "embedder": {
        "provider": "ollama",
        "config": {
            "model": "nomic-embed-text:latest",
            "ollama_base_url": OLLAMA_URL,
        },
    },
})

# OpenAI SDK pointed at Ollama for content rewriting
ollama_chat = OpenAI(base_url=f"{OLLAMA_URL}/v1", api_key="ollama")

USER_ID = "content_writer"
RUN_ID = "smart_editing_session"
```
  </Tab>
</Tabs>

## Storing Your Writing Preferences in Mem0

<Tabs>
  <Tab title="Platform">
```python
def store_writing_preferences():
    """Store your writing preferences in Mem0."""

    preferences = """My writing preferences:
1. Use headings and sub-headings for structure.
2. Keep paragraphs concise (8–10 sentences max).
3. Incorporate specific numbers and statistics.
4. Provide concrete examples.
5. Use bullet points for clarity.
6. Avoid jargon and buzzwords."""

    messages = [
        {"role": "user", "content": "Here are my writing style preferences."},
        {"role": "assistant", "content": preferences}
    ]

    response = client.add(
        messages,
        user_id=USER_ID,
        run_id=RUN_ID,
        metadata={"type": "preferences", "category": "writing_style"}
    )

    return response
```
  </Tab>
  <Tab title="Open Source">
```python
def store_writing_preferences():
    """Store your writing preferences in Mem0."""

    preferences = """My writing preferences:
1. Use headings and sub-headings for structure.
2. Keep paragraphs concise (8–10 sentences max).
3. Incorporate specific numbers and statistics.
4. Provide concrete examples.
5. Use bullet points for clarity.
6. Avoid jargon and buzzwords."""

    messages = [
        {"role": "user", "content": "Here are my writing style preferences."},
        {"role": "assistant", "content": preferences},
    ]

    response = memory.add(
        messages,
        user_id=USER_ID,
        run_id=RUN_ID,
        metadata={"type": "preferences", "category": "writing_style"},
    )

    return response
```
  </Tab>
</Tabs>

## Editing Content Using Stored Preferences

<Tabs>
  <Tab title="Platform">
```python
def apply_writing_style(original_content):
    """Use preferences stored in Mem0 to guide content rewriting."""

    results = client.search(
        query="What are my writing style preferences?",
        filters={
            "AND": [
                {"user_id": USER_ID},
                {"run_id": RUN_ID}
            ]
        }
    )

    if not results:
        print("No preferences found.")
        return None

    preferences = "\n".join(r["memory"] for r in results.get('results', []))

    system_prompt = f"""
You are a writing assistant.

Apply the following writing style preferences to improve the user's content:

Preferences:
{preferences}
"""

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"""Original Content:
    {original_content}"""}
    ]

    response = openai.chat.completions.create(
        model="gpt-5-mini",
        messages=messages
    )
    clean_response = response.choices[0].message.content.strip()

    return clean_response
```
  </Tab>
  <Tab title="Open Source">
```python
def apply_writing_style(original_content):
    """Use preferences stored in Mem0 to guide content rewriting."""

    results = memory.search(
        query="What are my writing style preferences?",
        filters={"user_id": USER_ID, "run_id": RUN_ID},
    )

    if not results:
        print("No preferences found.")
        return None

    preferences = "\n".join(r["memory"] for r in results.get("results", []))

    system_prompt = f"""
You are a writing assistant.

Apply the following writing style preferences to improve the user's content:

Preferences:
{preferences}
"""

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"""Original Content:
    {original_content}"""},
    ]

    # Ollama via OpenAI-compatible API
    response = ollama_chat.chat.completions.create(
        model=CHAT_MODEL,
        messages=messages,
    )
    clean_response = response.choices[0].message.content.strip()

    return clean_response
```
  </Tab>
</Tabs>

## Complete Workflow: Content Editing

```python
def content_writing_workflow(content):
    """Automated workflow for editing a document based on writing preferences."""

    # Store writing preferences (if not already stored)
    store_writing_preferences()  # Ideally done once, or with a conditional check

    # Edit the document with Mem0 preferences
    edited_content = apply_writing_style(content)

    if not edited_content:
        return "Failed to edit document."

    # Display results
    print("\n=== ORIGINAL DOCUMENT ===\n")
    print(content)

    print("\n=== EDITED DOCUMENT ===\n")
    print(edited_content)

    return edited_content
```

## Example Usage

```python
# Define your document
original_content = """Project Proposal

The following proposal outlines our strategy for the Q3 marketing campaign.
We believe this approach will significantly increase our market share.

Increase brand awareness
Boost sales by 15%
Expand our social media following

We plan to launch the campaign in July and continue through September.
"""

# Run the workflow
result = content_writing_workflow(original_content)
```

## Expected Output

Your document will be transformed into a structured, well-formatted version based on your preferences.

### Original Document
```
Project Proposal

The following proposal outlines our strategy for the Q3 marketing campaign.
We believe this approach will significantly increase our market share.

Increase brand awareness
Boost sales by 15%
Expand our social media following

We plan to launch the campaign in July and continue through September.
```

### Edited Document

```
# Project Proposal

## Q3 Marketing Campaign Strategy

This proposal outlines our strategy for the Q3 marketing campaign. We aim to significantly increase our market share with this approach.

### Objectives

- **Increase Brand Awareness**: Implement targeted advertising and community engagement to enhance visibility.
- **Boost Sales by 15%**: Increase sales by 15% compared to Q2 figures.
- **Expand Social Media Following**: Grow our social media audience by 20%.

### Timeline

- **Launch Date**: July
- **Duration**: July – September

### Key Actions

- **Targeted Advertising**: Utilize platforms like Google Ads and Facebook to reach specific demographics.
- **Community Engagement**: Host webinars and live Q&A sessions.
- **Content Creation**: Produce engaging videos and infographics.

### Supporting Data

- **Previous Campaign Success**: Our Q2 campaign increased sales by 12%. We will refine similar strategies for Q3.
- **Social Media Growth**: Last year, our Instagram followers grew by 25% during a similar campaign.

### Conclusion

We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let's work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
```

Mem0 enables a seamless, intelligent content-writing workflow, perfect for content creators, marketers, and technical writers looking to scale their personal tone and structure across work.

---

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