mkdir social-intel && cd social-intel
python3 -m venv venv
source venv/bin/activate     # If you are on Windows, use: venv\Scripts\activate

# Install dependencies
pip install "agno[infra]" openai exa_py python-dotenv

4. Create a new project with [AgentOS](/agent-os/introduction):

```bash
ag infra create              # Choose: [1] agent-infra-docker (default)
# Infra Name: social-intel

export OPENAI_API_KEY=sk-your-openai-api-key-here
export X_API_KEY=your-x-api-key
export X_API_SECRET=your-x-api-secret
export X_ACCESS_TOKEN=your-access-token
export X_ACCESS_TOKEN_SECRET=your-access-token-secret
export X_BEARER_TOKEN=your-bearer-token
export EXA_API_KEY=your-exa-api-key

mkdir -p app
touch app/social_media_agent.py

from pathlib import Path
from dotenv import load_dotenv
from agno.models.openai import OpenAIChat

# Load infrastructure secrets
load_dotenv(dotenv_path=Path(__file__).resolve().parents[1] / "infra" / "secrets" / ".env")

# Choose the AI model for your agent
model = OpenAIChat(id="gpt-5-mini")
print(f"Model selected: {model.id}")

# Quick test to verify model works
if __name__ == "__main__":
    test_response = model.invoke("Explain social media sentiment analysis in one sentence.")
    print(f"Model test: {test_response}")

from agno.tools.x import XTools

# Configure X Tools for social media data
x_tools = XTools(
    include_post_metrics=True,    # Critical: gets likes, retweets, replies for influence analysis
    wait_on_rate_limit=True,      # Handles API limits gracefully
)

print("XTools configured with post metrics enabled")

from agno.tools.exa import ExaTools

# Configure Exa for broader web intelligence
exa_tools = ExaTools(
    num_results=10,               # Comprehensive but not overwhelming
    include_domains=["reddit.com", "news.ycombinator.com", "medium.com"],
)

print("ExaTools configured for web search")

from textwrap import dedent

# Define how the agent should gather data
data_collection_strategy = dedent("""
    DATA COLLECTION STRATEGY:
    - Use X Tools to gather direct social media mentions with full engagement metrics
    - Use Exa Tools to find broader web discussions, articles, and forum conversations
    - Cross-reference findings between social and web sources for comprehensive coverage
""")

print("Data collection strategy defined")

# Define how the agent should analyze the data
analysis_framework = dedent("""
    ANALYSIS FRAMEWORK:
    - Classify sentiment as Positive/Negative/Neutral/Mixed with detailed reasoning
    - Weight analysis by engagement volume and author influence (verified accounts = 1.5x)
    - Identify engagement patterns: viral advocacy, controversy, influence concentration
    - Extract cross-platform themes and recurring discussion points
""")

print("Analysis framework defined")

# Define how to turn analysis into actionable insights
intelligence_synthesis = dedent("""
    INTELLIGENCE SYNTHESIS:
    - Detect crisis indicators through sentiment velocity and coordination patterns
    - Identify competitive positioning and feature gap discussions
    - Surface growth opportunities and advocacy moments
    - Generate strategic recommendations with clear priority levels
""")

print("Intelligence synthesis defined")

# Define the expected output structure
report_format = dedent("""
    REPORT FORMAT:

    ### Executive Dashboard
    - **Brand Health Score**: [1-10] with supporting evidence
    - **Net Sentiment**: [%positive - %negative] with trend analysis
    - **Key Drivers**: Top 3 positive and negative factors
    - **Alert Level**: Normal/Monitor/Crisis with threshold reasoning

    ### Quantitative Metrics
    | Sentiment | Posts | % | Avg Engagement | Influence Score |
    |-----------|-------|---|----------------|-----------------|
    [Detailed breakdown with engagement weighting]

    ### Strategic Recommendations
    **IMMEDIATE (≤48h)**: Crisis response, high-impact replies
    **SHORT-TERM (1-2 weeks)**: Content strategy, community engagement
    **LONG-TERM (1-3 months)**: Product positioning, market strategy
""")

print("Report format defined")

# Define the quality standards for analysis
analysis_principles = dedent("""
    ANALYSIS PRINCIPLES:
    - Evidence-based conclusions with supporting metrics
    - Actionable insights that drive business decisions
    - Cross-platform correlation analysis
    - Influence-weighted sentiment scoring
    - Proactive risk and opportunity identification
""")

print("Analysis principles defined")

# Combine all instruction components
complete_instructions = f"""
You are a Senior Social Media Intelligence Analyst specializing in cross-platform
brand monitoring and strategic analysis.

CORE METHODOLOGY:

{data_collection_strategy}

{analysis_framework}

{intelligence_synthesis}

{report_format}

{analysis_principles}
"""

print(f"Complete instructions created: {len(complete_instructions)} characters")

from agno.agent import Agent

# Combine model, tools, and instructions into a complete agent
social_media_agent = Agent(
    name="Social Media Intelligence Analyst",
    model=model,                 # The GPT-5 mini model we chose
    tools=tools,                 # The X and Exa tools we configured
    instructions=complete_instructions,  # The strategy we defined
    markdown=True,               # Enable rich formatting for reports
    show_tool_calls=True,        # Show transparency in data collection
)

print(f"Agent created: {social_media_agent.name}")

def analyze_brand_sentiment(query: str, tweet_count: int = 20):
    """
    Execute comprehensive social media intelligence analysis.

    Args:
        query: Brand or topic search query (e.g., "Tesla OR @elonmusk")
        tweet_count: Number of recent tweets to analyze
    """

    # Create a detailed prompt for the agent
    analysis_prompt = f"""
    Conduct comprehensive social media intelligence analysis for: "{query}"

    ANALYSIS PARAMETERS:
    - Twitter Analysis: {tweet_count} most recent tweets with engagement metrics
    - Web Intelligence: Related articles, discussions, and broader context via Exa
    - Cross-Platform Synthesis: Correlate social sentiment with web discussions
    - Strategic Focus: Brand positioning, competitive analysis, risk assessment

    METHODOLOGY:
    1. Gather direct social media mentions and engagement data
    2. Search for related web discussions and broader context
    3. Analyze sentiment patterns and engagement indicators
    4. Identify cross-platform themes and influence networks
    5. Generate strategic recommendations with evidence backing

    Provide comprehensive intelligence report following the structured format.
    """

    # Execute the analysis
    return social_media_agent.print_response(analysis_prompt, stream=True)

print("Analysis function created")

def test_agent():
    """Test the complete agent with a sample query."""
    print("Testing social media intelligence agent...")
    analyze_brand_sentiment("Agno OR AgnoAGI", tweet_count=10)

print("Test function ready")

"""
Complete Social Media Intelligence Agent
Built with Agno framework
"""
from pathlib import Path
from textwrap import dedent
from dotenv import load_dotenv

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.x import XTools
from agno.tools.exa import ExaTools

# Load infrastructure secrets
load_dotenv(dotenv_path=Path(__file__).resolve().parents[1] / "infra" / "secrets" / ".env")

# Step 1: Choose the AI model
model = OpenAIChat(id="gpt-5-mini")

# Step 2: Configure tools
tools = [
    XTools(
        include_post_metrics=True,
        wait_on_rate_limit=True,
    ),
    ExaTools(
        num_results=10,
        include_domains=["reddit.com", "news.ycombinator.com", "medium.com"],
        exclude_domains=["spam-site.com"],
    )
]

# Step 3: Define instructions
complete_instructions = dedent("""
    You are a Senior Social Media Intelligence Analyst specializing in cross-platform
    brand monitoring and strategic analysis.

    CORE METHODOLOGY:

    DATA COLLECTION STRATEGY:
    - Use X Tools to gather direct social media mentions with full engagement metrics
    - Use Exa Tools to find broader web discussions, articles, and forum conversations
    - Cross-reference findings between social and web sources for comprehensive coverage

    ANALYSIS FRAMEWORK:
    - Classify sentiment as Positive/Negative/Neutral/Mixed with detailed reasoning
    - Weight analysis by engagement volume and author influence (verified accounts = 1.5x)
    - Identify engagement patterns: viral advocacy, controversy, influence concentration
    - Extract cross-platform themes and recurring discussion points

    INTELLIGENCE SYNTHESIS:
    - Detect crisis indicators through sentiment velocity and coordination patterns
    - Identify competitive positioning and feature gap discussions
    - Surface growth opportunities and advocacy moments
    - Generate strategic recommendations with clear priority levels

    REPORT FORMAT:

    ### Executive Dashboard
    - **Brand Health Score**: [1-10] with supporting evidence
    - **Net Sentiment**: [%positive - %negative] with trend analysis
    - **Key Drivers**: Top 3 positive and negative factors
    - **Alert Level**: Normal/Monitor/Crisis with threshold reasoning

    ### Quantitative Metrics
    | Sentiment | Posts | % | Avg Engagement | Influence Score |
    |-----------|-------|---|----------------|-----------------|
    [Detailed breakdown with engagement weighting]

    ### Strategic Recommendations
    **IMMEDIATE (≤48h)**: Crisis response, high-impact replies
    **SHORT-TERM (1-2 weeks)**: Content strategy, community engagement
    **LONG-TERM (1-3 months)**: Product positioning, market strategy

    ANALYSIS PRINCIPLES:
    - Evidence-based conclusions with supporting metrics
    - Actionable insights that drive business decisions
    - Cross-platform correlation analysis
    - Influence-weighted sentiment scoring
    - Proactive risk and opportunity identification
""")

# Step 4: Create the complete agent
social_media_agent = Agent(
    name="Social Media Intelligence Analyst",
    model=model,
    tools=tools,
    instructions=complete_instructions,
    markdown=True,
    show_tool_calls=True,
)

def analyze_brand_sentiment(query: str, tweet_count: int = 20):
    """Execute comprehensive social media intelligence analysis."""
    prompt = f"""
    Conduct comprehensive social media intelligence analysis for: "{query}"

    ANALYSIS PARAMETERS:
    - Twitter Analysis: {tweet_count} most recent tweets with engagement metrics
    - Web Intelligence: Related articles, discussions, and broader context via Exa
    - Cross-Platform Synthesis: Correlate social sentiment with web discussions
    - Strategic Focus: Brand positioning, competitive analysis, risk assessment

    METHODOLOGY:
    1. Gather direct social media mentions and engagement data
    2. Search for related web discussions and broader context
    3. Analyze sentiment patterns and engagement indicators
    4. Identify cross-platform themes and influence networks
    5. Generate strategic recommendations with evidence backing

    Provide comprehensive intelligence report following the structured format.
    """

    return social_media_agent.print_response(prompt, stream=True)

if __name__ == "__main__":
    # Test the complete agent
    analyze_brand_sentiment("Agno OR AgnoAGI", tweet_count=25)

ag infra up

python app/social_media_agent.py

# Your AgentOS API is running on localhost:7777
curl http://localhost:7777/v1/agents

curl -X POST http://localhost:7777/v1/agents/social_media_agent/runs \
  -H "Content-Type: application/json" \
  -d '{
    "message": "Analyze social media sentiment for: Tesla OR @elonmusk",
    "stream": false
  }'

{
  "run_id": "run_123",
  "content": "### Executive Dashboard\n- **Brand Health Score**: 7.2/10...",
  "metrics": {
    "tokens_used": 1250,
    "tools_called": ["x_tools", "exa_tools"],
    "analysis_time": "23.4s"
  }
}