Starter AI Agents

# Setup and run
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd starter_ai_agents/openai_research_agent
pip install -r requirements.txt
export OPENAI_API_KEY='your-api-key-here'
streamlit run openai_researcher_agent.py

# Example usage pattern
# 1. Input specific startup sector (e.g., "AI healthcare")
# 2. Agent gathers recent news and funding data
# 3. Generates summaries of verified information
# 4. Identifies emerging patterns across stories

cd starter_ai_agents/ai_reasoning_agent
pip install -r requirements.txt
python local_ai_reasoning_agent.py

# Quick start
cd starter_ai_agents/ai_data_analysis_agent
pip install -r requirements.txt
streamlit run ai_data_analyst.py

# Example natural language queries:
# "What are the top 5 products by revenue?"
# "Show me the average sales by region"
# "Create a visualization of monthly trends"

# E-commerce scraping
prompt = "Extract product names, prices, and availability"

# Content aggregation
prompt = "Extract article title, author, date, and main content"

# Competitive intelligence
prompt = "Extract pricing, features, and updates"

# Lead generation
prompt = "Find company names, emails, and phone numbers"

cd starter_ai_agents/ai_meme_generator_agent_browseruse
pip install -r requirements.txt
python -m playwright install --with-deps
streamlit run ai_meme_generator_agent.py

cd starter_ai_agents/ai_medical_imaging_agent
pip install -r requirements.txt
streamlit run ai_medical_imaging.py

cd starter_ai_agents/ai_life_insurance_advisor_agent
pip install -r requirements.txt
streamlit run life_insurance_advisor_agent.py

cd starter_ai_agents/xai_finance_agent
pip install -r requirements.txt
export XAI_API_KEY='your-api-key-here'
python xai_finance_agent.py

cd starter_ai_agents/multimodal_ai_agent
pip install -r requirements.txt
export GOOGLE_API_KEY='your-api-key'
streamlit run multimodal_agent.py

cd starter_ai_agents/ai_music_generator_agent
pip install -r requirements.txt
streamlit run models_lab_music_generator_agent.py

cd starter_ai_agents/ai_blog_to_podcast_agent
pip install -r requirements.txt
streamlit run blog_to_podcast_agent.py

cd starter_ai_agents/ai_breakup_recovery_agent
pip install -r requirements.txt
streamlit run ai_breakup_recovery_agent.py

from agno import Agent
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    name="Financial Analyst",
    role="Analyze stocks and market trends",
    tools=[DuckDuckGoTools(), YFinanceTools()],
    show_tool_calls=True,
    markdown=True
)

response = agent.run("What's the current price of AAPL?")

from agno.storage import SqlAgentStorage

agent = Agent(
    storage=SqlAgentStorage(
        table_name="agent_sessions",
        db_url="sqlite:///agent_data.db"
    ),
    add_history_to_messages=True
)

agent.print_response(
    "Analyze this data",
    stream=True
)