We are building a multilingual writing assistant that rewrites rough drafts, fixes grammar, and explains its changes. I run it on Oxlo.ai because the flat per-request pricing means a 2,000-word document costs the same as a tweet. If you are new to LLMs, this is the fastest way to ship a useful language tool.
What you'll need
- Python 3.10 or newer
- An Oxlo.ai API key from https://portal.oxlo.ai
- The OpenAI SDK:
pip install openai
Step 1: Configure the Oxlo.ai client
Create a file named assistant.py. Import the SDK and point it at Oxlo.ai. I use qwen-3-32b in this project because it handles mixed-language input reliably.
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
print("Oxlo.ai client ready.")Step 2: Define the system prompt
The system prompt is the only logic this app needs. I force JSON output so the response is machine readable without regex hacks.
SYSTEM_PROMPT = """You are a professional writing assistant. When the user provides a draft:
1. Rewrite it for clarity, correct grammar, and appropriate tone.
2. If the draft is not in English, translate it to polished English unless the user asks otherwise.
3. Provide a brief list of the top 3 changes you made.
4. Return ONLY a JSON object with this exact structure:
{
"rewritten": "the improved text",
"changes": ["change 1", "change 2", "change 3"]
}
Do not add markdown code fences around the JSON."""Step 3: Build the draft processor
This function sends the draft to Oxlo.ai and parses the JSON response. Enabling json_object mode makes the model return valid JSON.
import json
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
SYSTEM_PROMPT = """You are a professional writing assistant. When the user provides a draft:
1. Rewrite it for clarity, correct grammar, and appropriate tone.
2. If the draft is not in English, translate it to polished English unless the user asks otherwise.
3. Provide a brief list of the top 3 changes you made.
4. Return ONLY a JSON object with this exact structure:
{
"rewritten": "the improved text",
"changes": ["change 1", "change 2", "change 3"]
}
Do not add markdown code fences around the JSON."""
def improve_draft(draft: str) -> dict:
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": draft},
],
response_format={"type": "json_object"},
)
raw = response.choices[0].message.content
return json.loads(raw)
# Quick smoke test
sample = "i didnt recieve the package yet, its been 2 week and im getting angry!!"
print(improve_draft(sample))Run it
Save the full script below as assistant.py and run python assistant.py. The loop processes three drafts and prints the results.
import json
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
SYSTEM_PROMPT = """You are a professional writing assistant. When the user provides a draft:
1. Rewrite it for clarity, correct grammar, and appropriate tone.
2. If the draft is not in English, translate it to polished English unless the user asks otherwise.
3. Provide a brief list of the top 3 changes you made.
4. Return ONLY a JSON object with this exact structure:
{
"rewritten": "the improved text",
"changes": ["change 1", "change 2", "change 3"]
}
Do not add markdown code fences around the JSON."""
def improve_draft(draft: str) -> dict:
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": draft},
],
response_format={"type": "json_object"},
)
raw = response.choices[0].message.content
return json.loads(raw)
drafts = [
"i didnt recieve the package yet, its been 2 week and im getting angry!!",
"Estoy muy contento con el producto, pero el envío fue demasiado lento.",
"The report is due ASAP, please review the attached file and give feedback.",
]
for d in drafts:
result = improve_draft(d)
print("Original:", d)
print("Rewritten:", result["rewritten"])
print("Changes:", result["changes"])
print("-" * 40)Expected output:
Original: i didnt recieve the package yet, its been 2 week and im getting angry!!
Rewritten: I have not received the package yet. It has been two weeks, and I am becoming concerned.
Changes: ["Fixed spelling of 'receive'", "Expanded contractions for professional tone", "Replaced 'angry' with 'concerned' to soften tone"]
----------------------------------------
Original: Estoy muy contento con el producto, pero el envío fue demasiado lento.
Rewritten: I am very pleased with the product, but the delivery was far too slow.
Changes: ["Translated from Spanish to English", "Maintained positive opening while clarifying complaint", "Used 'delivery' instead of 'shipment' for natural English"]
----------------------------------------
Original: The report is due ASAP, please review the attached file and give feedback.
Rewritten: The report is due as soon as possible. Please review the attached file and provide your feedback.
Changes: ["Expanded 'ASAP' for clarity", "Replaced 'give' with 'provide' for formality", "Added 'your' for polite directness"]Wrap-up
That is the entire pipeline. Two concrete next steps: add a FastAPI endpoint so other services can POST drafts to it, or swap the model to llama-3.3-70b if your users write primarily in English and you want the strongest general reasoning. Both changes take about ten minutes because the client is fully OpenAI compatible.
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