Advanced AI Agents

# Setup and run
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd starter_ai_agents/ai_medical_imaging_agent
pip install -r requirements.txt
streamlit run ai_medical_imaging.py

# Discounted cash-flow income replacement model
# The agent executes Python code in E2B sandbox:

def calculate_coverage(
    age, income, dependents, debt, 
    assets, existing_cover, horizon, location
):
    # 1. Calculate income replacement need
    income_need = income * horizon
    
    # 2. Add immediate obligations
    total_need = income_need + debt
    
    # 3. Subtract available assets
    net_need = total_need - assets - existing_cover
    
    # 4. Apply location-based adjustments
    # 5. Consider dependent care costs
    # 6. Account for inflation and discount rates
    
    return coverage_recommendation

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

# Example agent setup with Ollama
from agno import Agent, Ollama

agent = Agent(
    model=Ollama(id="llama3.1:70b"),
    markdown=True,
    show_tool_calls=True
)

# Input complex reasoning queries
response = agent.run(
    "Analyze the ethical implications of AI in healthcare"
)

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

User Query (Natural Language)
    ↓
Agent (GPT-4o) interprets intent
    ↓
DuckDB Tool generates SQL
    ↓
Data Processing & Analysis
    ↓
Visualization + Insights
    ↓
Formatted Response

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

# Complex aggregations
"What are the top 5 products by revenue in Q4 2024?"

# Multi-step analysis
"Calculate year-over-year growth rate for each category"

# Statistical queries
"Show me outliers in the sales data using IQR method"

# Conditional analysis
"Find customers who purchased more than $10k but returned items"

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

# Automatic model switching with validation
models = {
    "claude": Claude35Sonnet(),
    "gpt4o": GPT4o(),
    "deepseek": DeepseekV3()
}

# Agent selects best model for task
agent = BrowserAgent(
    model=models[selected_model],
    validate_api_key=True
)

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

# Video content analysis
"What products are featured in this commercial?"

# Combined analysis
"Identify the landmarks in this video and provide historical context"

# Educational content
"Explain the scientific concepts demonstrated in this experiment video"

# Tutorial understanding
"Summarize the steps shown in this cooking tutorial"

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

"Upbeat electronic dance music with synthesizers and a driving beat"

"Calm acoustic guitar melody with soft piano, perfect for meditation"

"Epic orchestral score with strings and brass, cinematic feel"

"Jazz fusion with saxophone leads and complex drum patterns"

Blog URL Input
    ↓
Firecrawl API (Content Scraping)
    ↓
GPT-4 (Summary Generation)
    ↓
ElevenLabs TTS (Audio Synthesis)
    ↓
MP3 Output (Playback/Download)

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_startup_trend_analysis_agent
pip install -r requirements.txt
streamlit run startup_trends_agent.py

from agno import Agent

try:
    agent = Agent(
        model=OpenAI(id="gpt-4o"),
        tools=[ComplexTool()],
        retries=3,  # Retry on failure
        timeout=120  # Timeout after 2 minutes
    )
    
    response = agent.run(query)
    
except TimeoutError:
    print("Agent execution timed out")
except Exception as e:
    print(f"Agent error: {e}")

# Use context managers for cleanup
with Agent(model=OpenAI()) as agent:
    result = agent.run("Process this data")
# Agent resources automatically cleaned up

# Close connections explicitly
agent.close()

# Use cheaper models for simple tasks
simple_agent = Agent(model=OpenAI(id="gpt-4o-mini"))

# Reserve advanced models for complex reasoning
complex_agent = Agent(model=OpenAI(id="o3-mini"))

# Cache results to avoid redundant API calls
agent = Agent(
    model=OpenAI(),
    cache_responses=True
)