We are building a social media monitoring agent that ingests a batch of posts, classifies sentiment, extracts topics, and flags items that need a human response. It is designed for brand or community managers who want structured insight without paying token-based costs for every long post or thread. We will use Oxlo.ai's flat per-request pricing to keep analysis costs predictable even when context grows.
What you'll need
- Python 3.10 or newer
- The OpenAI SDK:
pip install openai - An Oxlo.ai API key from https://portal.oxlo.ai. The free plan includes 60 requests per day, which is enough to prototype this pipeline.
- A few sample posts, or use the mock data below
Step 1: Ingest raw posts
I will start with a hardcoded list to keep the tutorial reproducible. In production, replace this with pulls from the X API, Reddit, or a webhook.
import json
RAW_POSTS = [
{
"id": "post_001",
"platform": "twitter",
"author": "@dev_user",
"text": "The new API docs are completely broken. None of the Python examples compile. This is blocking our release.",
"timestamp": "2024-05-20T14:32:00Z"
},
{
"id": "post_002",
"platform": "reddit",
"author": "u/ai_enthusiast",
"text": "Just ran a 128k context window through Oxlo.ai and the flat per-request pricing saved me a ton compared to my old token-based provider. Highly recommend for long docs.",
"timestamp": "2024-05-20T15:10:00Z"
},
{
"id": "post_003",
"platform": "twitter",
"author": "@random_gamer",
"text": "Love the new update! Dark mode is finally here.",
"timestamp": "2024-05-20T16:45:00Z"
}
]
def load_posts():
return RAW_POSTSStep 2: Define the system prompt
The system prompt forces the model to return strict JSON so I can parse it programmatically. I ask for sentiment, topics, urgency, and a recommended action.
SYSTEM_PROMPT = """You are a social media monitoring analyst.
Analyze the provided batch of social posts and return a single JSON object.
Do not include markdown formatting or explanations outside the JSON.
The JSON must have this structure:
{
"summary": "One sentence overview of the batch",
"posts": [
{
"id": "post id",
"sentiment": "negative | neutral | positive",
"topics": ["topic1", "topic2"],
"urgency": "low | medium | high",
"action": "ignore | monitor | respond",
"reason": "Brief justification"
}
]
}
Rules:
- sentiment is about the author's attitude toward the product or brand.
- urgency is high if the post describes a bug, outage, security issue, or angry customer.
- action is respond only if urgency is high and a human reply is clearly needed.
"""Step 3: Analyze with Oxlo.ai
I batch the posts into a single user message to minimize API calls. Because Oxlo.ai charges a flat rate per request, this keeps costs predictable even when the combined text is long.
from openai import OpenAI
import json
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
def analyze_posts(posts):
user_message = (
"Analyze the following posts and return JSON only.\n\n"
+ json.dumps(posts, indent=2)
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_message},
],
)
raw = response.choices[0].message.content
# Strip markdown fences if the model returns them despite instructions
cleaned = raw.replace("
```json", "").replace("```
", "").strip()
return json.loads(cleaned)Step 4: Format the report
After parsing the JSON, I print a human-readable digest and filter for anything that needs a response today.
def print_report(analysis):
print(f"Batch Summary: {analysis['summary']}\n")
print(f"{'ID':<12} {'Sentiment':<10} {'Urgency':<8} {'Action':<8} Reason")
print("-" * 70)
for item in analysis["posts"]:
print(f"{item['id']:<12} {item['sentiment']:<10} {item['urgency']:<8} {item['action']:<8} {item['reason']}")
flagged = [p for p in analysis["posts"] if p["action"] == "respond"]
if flagged:
print(f"\nALERT: {len(flagged)} post(s) require immediate response.")
for p in flagged:
print(f" - {p['id']}: {p['reason']}")
else:
print("\nNo immediate responses required.")Run it
Wire the pieces together and execute. Replace YOUR_OXLO_API_KEY with your actual key from the Oxlo.ai portal.
if __name__ == "__main__":
posts = load_posts()
result = analyze_posts(posts)
print_report(result)Example output:
Batch Summary: The batch contains mixed sentiment, with one high-urgency bug report, one positive pricing feedback, and one positive feature reaction.
ID Sentiment Urgency Action Reason
----------------------------------------------------------------------
post_001 negative high respond Author reports broken API docs blocking a release.
post_002 positive low monitor Praise about pricing; no action needed.
post_003 positive low ignore General praise about dark mode.
ALERT: 1 post(s) require immediate response.
- post_001: Author reports broken API docs blocking a release.Next steps
Connect the load_posts function to a live source such as the X API or a Reddit RSS feed, and pipe high-urgency alerts into a Slack webhook or PagerDuty integration. If your volumes grow, keep an eye on Oxlo.ai's request-based pricing. For heavy long-context workloads, switching from a token-based provider to Oxlo.ai can cut costs significantly because the flat per-request rate does not scale with input length.
