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Social Media Analyst Example with Dummy Dataset This example demonstrates how to create an agent that:
  1. Analyzes a dummy dataset of tweets
  2. Leverages LLM capabilities to perform sophisticated sentiment analysis
  3. Provides insights about the overall sentiment around a topic

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Code

cookbook/01_showcase/01_agents/social_media_analyst/agent.py
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.reasoning import ReasoningTools
from agno.tools.x import XTools
from schemas import SocialMediaReport
from agno.db.sqlite import SqliteDb

SYSTEM_MESSAGE = """\
You are a senior Brand Intelligence Analyst specializing in social media listening
on the X (Twitter) platform. Your job is to transform raw tweet content and
engagement metrics into executive-ready intelligence reports.

## Core Responsibilities

1. **Retrieve & Analyze**: Use X tools to retrieve tweets and analyze both text
   and metrics (likes, retweets, replies)
2. **Classify Sentiment**: Categorize every tweet as Positive / Negative / Neutral / Mixed
3. **Detect Patterns**: Identify engagement patterns:
   - Viral advocacy (high likes & retweets, low replies)
   - Controversy (low likes, high replies)
   - Influence concentration (verified or high-reach accounts)
4. **Extract Themes**: Identify recurring topics:
   - Feature praise / pain points
   - UX / performance issues
   - Customer service interactions
   - Pricing & ROI perceptions
   - Competitor mentions
5. **Generate Recommendations**: Prioritized actions (Immediate, Short-term, Long-term)

## Analysis Framework

### Sentiment Classification
- **Positive**: Praise, recommendations, satisfaction
- **Negative**: Complaints, frustration, criticism
- **Neutral**: Information sharing, questions, news
- **Mixed**: Contains both positive and negative elements

### Engagement Analysis
- Reply-to-like ratio > 0.5 indicates controversy
- High retweets with positive sentiment indicates advocacy
- Verified accounts get 1.5x weight in analysis

### Brand Health Score (1-10)
- 9-10: Overwhelmingly positive, strong advocacy
- 7-8: Mostly positive, minor issues
- 5-6: Mixed sentiment, notable concerns
- 3-4: Predominantly negative, significant issues
- 1-2: Crisis level negativity

## Report Structure

Your report should include:

1. **Executive Snapshot**
   - Brand health score
   - Net sentiment percentage
   - Top 3 positive and negative drivers
   - Red flags requiring urgent attention

2. **Quantitative Dashboard**
   - Sentiment distribution with counts and percentages
   - Average engagement metrics per sentiment category

3. **Key Themes**
   - Theme name and description
   - Sentiment trend for theme
   - Representative tweets
   - Key metrics

4. **Risk Analysis**
   - Potential crises
   - Churn indicators
   - Trust concerns

5. **Opportunity Landscape**
   - Features users love
   - Advocacy opportunities
   - Untapped use cases

6. **Strategic Recommendations**
   - Immediate (within 48 hours)
   - Short-term (1-2 weeks)
   - Long-term (1-3 months)

Use the think tool to plan your analysis approach.
Use the analyze tool to validate your findings before presenting.
"""

social_media_agent = Agent(
    name="Social Media Analyst",
   model=OpenAIResponses(id="gpt-5.2"),
    system_message=SYSTEM_MESSAGE,
    output_schema=SocialMediaReport,
    tools=[
        XTools(
            include_post_metrics=True,
            wait_on_rate_limit=True,
        ),
        ReasoningTools(add_instructions=True),
    ],
    add_datetime_to_context=True,
    add_history_to_context=True,
    num_history_runs=5,
    enable_agentic_memory=True,
    markdown=True,
    db=SqliteDb(db_file="tmp/data.db"),
)

def analyze_brand(brand: str, tweet_count: int = 10) -> SocialMediaReport:
    """Analyze brand sentiment on X (Twitter).

    Args:
        brand: Brand or topic to analyze.
        tweet_count: Number of tweets to analyze.

    Returns:
        SocialMediaReport with analysis results.
    """
    prompt = f"Analyze the sentiment of {brand} on X (Twitter) for the past {tweet_count} tweets"

    response = social_media_agent.run(prompt)

    if response.content and isinstance(response.content, SocialMediaReport):
        return response.content
    else:
        raise ValueError("Failed to generate social media report")


if __name__ == "__main__":
    social_media_agent.cli_app(stream=True)

​
Usage

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Set your API key

export OPENAI_API_KEY=xxx
export X_BEARER_TOKEN=xxx
export X_CONSUMER_KEY=xxx
export X_CONSUMER_SECRET=xxx
export X_ACCESS_TOKEN=xxx
export X_ACCESS_TOKEN_SECRET=xxx
3

Install dependencies

uv pip install -U agno openai tweepy
4

Run Agent

python cookbook/01_showcase/01_agents/social_media_analyst/agent.py