> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Shubhamsaboo/awesome-llm-apps/llms.txt
> Use this file to discover all available pages before exploring further.
# Starter AI Agents
> Single-agent applications for learning agent fundamentals
## Overview
Starter agents are single-agent applications that demonstrate core agent capabilities like tool use, reasoning, and task completion. These agents are perfect for learning agent development fundamentals before moving to more complex multi-agent systems.
## Research & Analysis Agents
### OpenAI Research Agent
A multi-agent research application that conducts comprehensive research on any topic using coordinated AI agents.
* **Triage Agent**: Plans research approach and coordinates workflow
* **Research Agent**: Searches web and gathers relevant information
* **Editor Agent**: Compiles facts into comprehensive reports
* Automatic fact collection with source attribution
* Structured report generation with citations
* Interactive Streamlit UI with tracing
```bash theme={null}
# 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
```
**Research Process:**
1. Enter research topic or select example
2. Triage agent plans the approach
3. Research agent gathers information from web
4. Editor agent compiles comprehensive report
5. View process in real-time and download report
### AI Startup Trend Analysis Agent
Generates actionable insights by identifying nascent trends, market gaps, and growth opportunities in startup sectors.
* News collection using DuckDuckGo
* Article summarization with Newspaper4k
* Pattern identification in startup funding
* Technology adoption trend analysis
* Market opportunity detection
* Built with Claude 3.5 Sonnet
```python theme={null}
# 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
```
### AI Reasoning Agent
Leverages advanced AI models for complex reasoning and decision-making tasks.
**Features:**
* Advanced reasoning with Ollama models
* Interactive playground interface
* Markdown-formatted outputs
* Customizable for different scenarios
* Local execution support
```bash theme={null}
cd starter_ai_agents/ai_reasoning_agent
pip install -r requirements.txt
python local_ai_reasoning_agent.py
```
## Data & Analysis Agents
### AI Data Analysis Agent
An AI data analyst built with Agno and OpenAI's GPT-4o that analyzes CSV and Excel files through natural language queries.
* CSV and Excel uploads
* Automatic schema inference
* Multiple file format support
* Data type detection
* Natural language to SQL
* Complex aggregations
* Statistical summaries
* Data visualizations
```bash theme={null}
# Quick start
cd starter_ai_agents/ai_data_analysis_agent
pip install -r requirements.txt
streamlit run ai_data_analyst.py
```
**Powered by DuckDB:**
```python theme={null}
# 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"
```
No SQL knowledge required - the agent converts natural language questions into efficient SQL queries using DuckDB.
### AI Data Visualization Agent
Your personal data visualization expert that generates charts and insights from natural language questions.
**Multi-Model Support:**
* Meta-Llama 3.1 405B (complex analysis)
* DeepSeek V3 (detailed insights)
* Qwen 2.5 7B (quick analysis)
* Meta-Llama 3.3 70B (advanced queries)
**Capabilities:**
* Automatic chart type selection
* Dynamic visualization generation
* Statistical analysis
* Custom plot formatting
* Interactive follow-up questions
Requires Together AI API key and E2B API key for sandbox execution.
## Web & Automation Agents
### Web Scraping AI Agent
AI-powered web scraping using ScrapeGraph AI - extract structured data with natural language prompts.
**Pros:**
* Free to use (no API costs)
* Full control over execution
* Privacy-friendly (data stays local)
* Supports GPT-4o, GPT-5, local models
**Cons:**
* Requires local installation
* Limited by hardware
* Manual updates needed
```bash theme={null}
cd starter_ai_agents/web_scraping_ai_agent
pip install -r requirements.txt
streamlit run ai_scrapper.py
```
**Pros:**
* No setup required (just API key)
* Scalable and fast
* Advanced features (SmartCrawler, SearchScraper)
* Always up-to-date
**Cons:**
* Pay-per-use (credit-based)
* Requires internet connection
```bash theme={null}
cd scrapegraph_ai_sdk/
export SGAI_API_KEY='your-api-key'
python quickstart.py
```
**Use Cases:**
```python theme={null}
# 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"
```
### AI Meme Generator Agent
Browser automation agent that creates memes using AI and direct website interaction.
**Multi-LLM Support:**
* Claude 3.5 Sonnet (Anthropic)
* GPT-4o (OpenAI)
* Deepseek v3 (Deepseek)
* Automatic model switching
**Browser Automation Features:**
* Direct interaction with imgflip.com
* Automated template search
* Dynamic caption insertion
* Image link extraction
* Multi-step quality validation
```bash theme={null}
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
```
## Specialized Domain Agents
### Medical Imaging Diagnosis Agent
AI-assisted analysis of medical images powered by Gemini 2.0 Flash built on Agno framework.
**Image Analysis:**
* Image type identification (X-ray, MRI, CT, ultrasound)
* Anatomical region detection
* Key findings observation
* Abnormality detection
* Quality assessment
**Diagnostic Output:**
* Potential diagnoses ranking
* Differential diagnoses
* Severity assessment
* Patient-friendly explanations
* Visual reference points
```bash theme={null}
cd starter_ai_agents/ai_medical_imaging_agent
pip install -r requirements.txt
streamlit run ai_medical_imaging.py
```
For educational purposes only. Not a replacement for professional medical diagnosis. Always consult qualified healthcare professionals.
### Life Insurance Coverage Advisor Agent
Helps estimate term life insurance needs and surfaces available policy options.
**Technology Stack:**
* Agno agent framework
* OpenAI GPT-5 for reasoning
* E2B sandbox for calculations
* Firecrawl for web research
**Features:**
* Minimal intake form (age, income, dependents, debt, etc.)
* Python code execution in E2B sandbox
* Discounted cash-flow income replacement model
* Latest term-life product research
* Coverage breakdown with source links
```bash theme={null}
cd starter_ai_agents/ai_life_insurance_advisor_agent
pip install -r requirements.txt
streamlit run life_insurance_advisor_agent.py
```
### xAI Finance Agent
Financial analysis agent powered by xAI's Grok model with real-time stock data.
**Capabilities:**
* Powered by Grok-4 Fast model
* Real-time stock data via YFinance
* Web search with DuckDuckGo
* Formatted tables for financial data
* Interactive playground interface
```bash theme={null}
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 Integration:**
Connect to AgentOS Control Plane for monitoring and management. See [Connecting Your OS](https://docs.agno.com/agent-os/connecting-your-os).
## Multimodal Agents
### Multimodal AI Agent
Combines video analysis and web search using Google's Gemini 2.5 model.
**Features:**
* Video analysis (Gemini 2.5 Flash/Pro)
* Web research integration (DuckDuckGo)
* Multiple video formats (MP4, MOV, AVI)
* Real-time video processing
* Combined visual and textual analysis
```bash theme={null}
cd starter_ai_agents/multimodal_ai_agent
pip install -r requirements.txt
export GOOGLE_API_KEY='your-api-key'
streamlit run multimodal_agent.py
```
## Creative Content Agents
### AI Music Generator Agent
Generates music using ModelsLab API and OpenAI's GPT-4 for prompt optimization.
**Features:**
* Detailed prompt input (genre, instruments, mood)
* MP3 format output
* In-browser playback and download
* Simple Streamlit interface
```bash theme={null}
cd starter_ai_agents/ai_music_generator_agent
pip install -r requirements.txt
streamlit run models_lab_music_generator_agent.py
```
**Required API Keys:**
* OpenAI API key
* ModelsLab API key
### Blog to Podcast Agent
Converts blog posts into podcasts using GPT-4, Firecrawl, and ElevenLabs.
**Workflow:**
1. Blog scraping with Firecrawl API
2. Summary generation with GPT-4 (max 2000 chars)
3. Audio podcast creation with ElevenLabs
4. Playback and download capabilities
```bash theme={null}
cd starter_ai_agents/ai_blog_to_podcast_agent
pip install -r requirements.txt
streamlit run blog_to_podcast_agent.py
```
## Emotional Support Agents
### Breakup Recovery Agent Team
Multi-agent system for emotional recovery support powered by Gemini 2.0 Flash.
Empathetic support and coping strategies with DuckDuckGo research tools.
Writes unsent emotional messages for cathartic release.
Daily recovery routines with balanced activities.
Direct, objective feedback with no sugar-coating.
**Features:**
* Chat screenshot analysis
* Parallel agent execution
* Secure API key management
* Team leader coordination
```bash theme={null}
cd starter_ai_agents/ai_breakup_recovery_agent
pip install -r requirements.txt
streamlit run ai_breakup_recovery_agent.py
```
## Getting Started Guide
Select an agent based on your use case:
* Research: OpenAI Research Agent
* Data Analysis: Data Analysis Agent
* Web Scraping: Web Scraping Agent
* Specialized: Medical, Finance, or Insurance agents
```bash theme={null}
# Clone repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd starter_ai_agents/
# Install dependencies
pip install -r requirements.txt
```
Set required environment variables:
```bash theme={null}
export OPENAI_API_KEY='your-key'
export GOOGLE_API_KEY='your-key' # if needed
export ANTHROPIC_API_KEY='your-key' # if needed
```
```bash theme={null}
# For Streamlit apps
streamlit run .py
# For CLI apps
python .py
```
## Common Patterns
### Single Agent with Tools
```python theme={null}
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?")
```
### Agent with Memory
```python theme={null}
from agno.storage import SqlAgentStorage
agent = Agent(
storage=SqlAgentStorage(
table_name="agent_sessions",
db_url="sqlite:///agent_data.db"
),
add_history_to_messages=True
)
```
### Streaming Responses
```python theme={null}
agent.print_response(
"Analyze this data",
stream=True
)
```
Start with research or data analysis agents to understand core patterns, then explore specialized agents for domain-specific implementations.
## Next Steps
Explore more complex single-agent implementations
Learn agent coordination and teamwork
Add voice capabilities to your agents
Integrate with external services via MCP