Advanced AI Agents

Complex agent implementations with sophisticated capabilities

Overview

Advanced agents demonstrate sophisticated AI capabilities including complex reasoning, specialized domain expertise, and advanced tool integration. These agents build upon starter patterns with enhanced decision-making, multimodal processing, and professional-grade implementations.

While technically single agents, these implementations showcase advanced patterns that bridge the gap between basic agents and full multi-agent systems.

Medical & Healthcare Agents

Medical Imaging Diagnosis Agent

A comprehensive medical imaging analysis agent built on Agno and powered by Gemini 2.0 Flash that acts as a medical imaging diagnosis expert.

Comprehensive Analysis Pipeline

Image Type Identification:

• X-ray detection and analysis
• MRI scan interpretation
• CT scan evaluation
• Ultrasound assessment

Anatomical Analysis:

• Automatic region detection
• Key findings identification
• Abnormality highlighting
• Quality assessment

Diagnostic Output:

• Potential diagnoses with ranking
• Differential diagnosis considerations
• Severity level assessment
• Patient-friendly explanations
• Research and reference citations

# 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

Analysis Components:

Image Type & Region

• Identifies imaging modality automatically
• Specifies anatomical region being scanned
• Validates image quality and completeness

Key Findings

• Systematic listing of observations
• Detailed appearance descriptions
• Abnormality identification and highlighting
• Comparison to normal anatomy

Diagnostic Assessment

• Potential diagnoses ranked by likelihood
• Differential diagnoses considered
• Severity assessment with clinical context
• Recommended follow-up actions

Patient Communication

• Simplified medical terminology
• First-principles explanations
• Visual reference points
• Actionable next steps

Technical Details:

• Uses Gemini 2.0 Flash for multimodal analysis
• 1,500 free requests per day from Google
• Requires stable internet connection
• Real-time image processing

Medical Disclaimer: This tool is for educational and informational purposes only. All analyses should be reviewed by qualified healthcare professionals. Do not make medical decisions based solely on this analysis.

Financial & Insurance Agents

Life Insurance Coverage Advisor Agent

An intelligent advisor that estimates term life insurance needs and surfaces available policy options using advanced calculation methods.

Technology Stack:

Agno Framework

Agent orchestration and workflow management

OpenAI GPT-5

Core reasoning and decision-making

E2B Sandbox

Secure code execution environment

Firecrawl

Live web research and product discovery

Python

Financial calculations and modeling

Streamlit

Interactive user interface

Calculation Methodology:

# 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

Features:

• Minimal intake form with essential fields
• Deterministic coverage calculations
• Real-time policy research
• Up to 3 product suggestions with source links
• Calculation breakdown transparency

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

Required API Keys:

Service	Purpose	Get It From
OpenAI (GPT-5-mini)	Core reasoning	https://platform.openai.com/api-keys
Firecrawl	Web search + crawl	https://www.firecrawl.dev/app/api-keys
E2B	Code execution sandbox	https://e2b.dev

Financial Disclaimer: This project is for educational and prototyping purposes only and does not provide licensed financial advice. Always validate output with qualified professionals and confirm details directly with insurance providers.

xAI Finance Agent

Financial analysis agent powered by xAI’s Grok model with real-time market data integration.

Key Capabilities:

• Powered by Grok-4 Fast model
• Real-time stock data analysis via YFinance
• Web search capabilities through DuckDuckGo
• Formatted output with tables
• Interactive playground interface
• AgentOS integration for monitoring

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

AgentOS Control Plane: Connect your running agent to AgentOS for management and monitoring:

1. Visit Documentation

   Go to Connecting Your OS

2. Register AgentOS

   Follow steps to register your local instance

3. Establish Connection

   Connect through the browser interface

4. Monitor Performance

   Track agent interactions, costs, and performance

Advanced Reasoning Agents

AI Reasoning Agent

Leverages advanced AI models to provide deep reasoning and decision-making capabilities.

Features:

Advanced Reasoning

• Complex reasoning tasks
• Multi-step problem solving
• Logical deduction
• Structured analysis

Interactive Playground

• User-friendly interface
• Real-time processing
• Markdown output support
• Query history tracking

Configuration:

# 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

Customization Options:

• Model selection (Ollama, OpenAI, Anthropic)
• Temperature and sampling parameters
• Output format preferences
• Context window configuration

Data & Analytics Agents

AI Data Analysis Agent

Advanced data analysis agent using Agno and OpenAI GPT-4o with DuckDB for efficient data processing.

Architecture:

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

Supported Operations:

File Operations

# Automatic schema inference
- CSV file parsing
- Excel workbook reading
- Data type detection
- Column name normalization

Query Types

-- Aggregation queries
"What's the total revenue by region?"

-- Filtering and sorting
"Show me top 10 customers by sales"

-- Statistical analysis
"Calculate average, median, and std dev"

-- Time series
"Show monthly trends for the past year"

Visualizations

• Bar charts for comparisons
• Line charts for trends
• Scatter plots for correlations
• Pie charts for distributions
• Heatmaps for matrices

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

Natural Language Examples:

# 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"

AI Data Visualization Agent

Specialized visualization agent with multi-model support for generating insights and charts.

Model Selection:

Model	Best For	Speed	Quality
Meta-Llama 3.1 405B	Complex analysis	Slow	Excellent
DeepSeek V3	Detailed insights	Medium	Very Good
Qwen 2.5 7B	Quick analysis	Fast	Good
Meta-Llama 3.3 70B	Advanced queries	Medium	Excellent

Visualization Intelligence:

• Automatic chart type selection based on data
• Dynamic axis scaling and formatting
• Color scheme optimization
• Multi-plot compositions
• Interactive elements

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

Required Services:

• Together AI API key (free tier available)
• E2B API key for sandbox execution

Web Automation Agents

AI Meme Generator Agent (Browser Use)

Advanced browser automation agent that creates memes using multi-LLM capabilities and direct website manipulation.

Multi-LLM Architecture:

# 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
)

Browser Automation Workflow:

1. Action Verb Extraction

   Analyze prompt to identify key action verbs and concepts

2. Template Search

   Navigate to imgflip.com and search for relevant meme templates

3. Metaphorical Matching

   Use AI to match prompt intent with meme format

4. Caption Generation

   Generate witty top and bottom text captions

5. Dynamic Insertion

   Automate text entry into meme generator

6. Quality Validation

   Multi-step validation and retry mechanism

7. Link Extraction

   Extract and return generated meme URL

Features:

• Model configuration sidebar
• API key management per model
• Direct meme preview with clickable links
• Responsive error handling
• Automatic retry on failures

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

The agent uses Playwright for browser automation. Make sure to install browser dependencies with --with-deps flag.

Multimodal Agents

Multimodal AI Agent

Combines video analysis and web search capabilities using Google’s Gemini 2.5 model.

Capabilities:

Video Analysis

• Multiple format support (MP4, MOV, AVI)
• Real-time processing
• Scene understanding
• Object detection
• Activity recognition

Web Integration

• DuckDuckGo search integration
• Contextual information retrieval
• Combined visual + textual analysis
• Source verification

Example Use Cases:

# 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

Supported Models:

• Gemini 2.5 Flash (fast processing)
• Gemini 2.5 Pro (enhanced accuracy)

Content Generation Agents

AI Music Generator Agent

Generates custom music using ModelsLab API with GPT-4 powered prompt optimization.

Features:

• Detailed prompt customization:
  • Genre selection
  • Instrument specification
  • Mood and atmosphere
  • Tempo and rhythm
  • Musical structure
• MP3 format output
• In-browser playback
• Download capability
• Preview before generation

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

Example Prompts:

"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 to Podcast Agent

Converts written blog content into professional audio podcasts.

Processing Pipeline:

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

Features:

• Full blog content scraping
• Intelligent summarization (2000 char limit)
• High-quality voice synthesis
• Multiple voice options
• Audio player integration
• Download functionality

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

Required API Keys:

• OpenAI (GPT-4)
• ElevenLabs (TTS)
• Firecrawl (Content scraping)

Trend Analysis Agents

AI Startup Trend Analysis Agent

Generates actionable insights for entrepreneurs by analyzing startup trends and market gaps.

Analysis Pipeline:

1. News Collection

   Gather recent startup news, funding rounds, and market analyses using DuckDuckGo

2. Content Processing

   Extract and summarize verified information using Newspaper4k

3. Pattern Identification

   Identify emerging patterns in:

   • Startup funding trends
   • Technology adoption rates
   • Market opportunities
   • Competitive landscape
4. Insight Generation

   Generate actionable recommendations using Claude 3.5 Sonnet

Use Cases:

• Validate startup ideas
• Spot market opportunities
• Identify technology trends
• Analyze competitive landscape
• Track funding patterns

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

Requires Anthropic API key for Claude 3.5 Sonnet. Get your key from Anthropic’s website.

Best Practices

Error Handling

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}")

Resource Management

# 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()

Cost Optimization

# 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
)

Next Steps

Multi-Agent Teams

Learn to coordinate multiple specialized agents

Voice Agents

Add voice capabilities to your agents

MCP Integration

Connect agents to external services

Game Playing Agents

Build autonomous game-playing systems