Personal AI Agents

Overview

A comprehensive framework for building and managing intelligent AI agents powered by LangChain. This project provides a modular and extensible architecture for creating specialized AI agents that can assist with various tasks including data analysis, research, content analysis, and task scheduling.

Key Features

• Modular Agent Architecture: Built on a robust BaseAgent class for consistent behavior
• Specialized Agents:
  • Data Analyst: Statistical analysis and data visualization
  • Content Analyst: Text and content processing
  • Task Scheduler: Intelligent task management
  • Researcher: Information gathering and analysis
  • Mail Agent: Email processing and management
  • Code Analyst: Code analysis and quality assessment
• Built-in Utilities:
  • Advanced error handling and logging
  • Configuration management
  • Knowledge management system

Installation

Prerequisites

• Python 3.8 or higher
• Groq API key for LLM access
• Git (for cloning the repository)
• pip (Python package installer)

Quick Start

1. Clone the repository:

git clone https://github.com/FlareFPV-cz/personal_agents.git
cd personal_agents

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment: Create a .env file in the project root:

GROQ_API_KEY=your_api_key_here
AGENT_CONFIG_PATH=path/to/config.json  # Optional

Configuration Options

• GROQ_API_KEY: Your Groq API key for LLM access
• AGENT_CONFIG_PATH: Path to custom agent configuration file (optional)
• LOG_LEVEL: Logging level (DEBUG, INFO, WARNING, ERROR)
• KNOWLEDGE_BASE_PATH: Path to persistent knowledge storage

Usage Examples

Data Analysis

from agents.data_analyst import DataAnalystAgent
from utils.logger import AgentLogger

# Initialize
logger = AgentLogger("data_analyst")
agent = DataAnalystAgent()

# Load and analyze data
df = pd.read_csv("your_data.csv")
result = agent.load_data(df)
summary = agent.generate_summary()

# Create visualizations
agent.create_visualization('histogram', 'age')
agent.create_visualization('scatter', 'income', 'education')

Task Scheduling

from agents.task_scheduler import TaskSchedulerAgent

agent = TaskSchedulerAgent()

# Add tasks
task = {
    "title": "Project Review",
    "priority": "high",
    "due_date": "2024-03-20"
}
agent.add_task(task)

# Get recommendations
recommendations = agent.run("What should I focus on today?")

Code Analysis

from agents.code_analyst import CodeHelper

# Initialize analyzer
analyzer = CodeHelper(
    quality_threshold=8.5,
    model_name="llama3-70b-8192"
)

# Analyze code
results = analyzer.analyze("path/to/your/code.py")
print(f"Quality Score: {results['quality_score']}/10")

Architecture

Core Components

• BaseAgent: Foundation class implementing common agent functionality
• Knowledge Manager: Handles persistent knowledge storage and retrieval
• Config Manager: Manages agent configurations and settings
• Logger: Advanced logging with configurable outputs
• Error Handler: Standardized error management system

Agent Capabilities

• Data analysis and visualization
• Natural language task management
• Content analysis and processing
• Code quality assessment
• Research and information gathering
• Email processing and management

Project Structure

personal_agents/
├── agents/             # Specialized agent implementations
├── core/              # Core framework components
├── examples/          # Usage examples and demos
├── tests/             # Comprehensive test suite
├── utils/             # Utility modules
└── requirements.txt   # Project dependencies

Troubleshooting

Common Issues

1. API Key Issues

   • Ensure GROQ_API_KEY is properly set in .env file
   • Verify API key permissions and quota

2. Import Errors

   • Confirm all dependencies are installed: pip install -r requirements.txt
   • Check Python version compatibility

3. Agent Initialization Failures

   • Verify configuration file paths
   • Check log files for detailed error messages

Debug Mode

Enable debug logging by setting:

LOG_LEVEL=DEBUG

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Add unit tests for new features
• Update documentation as needed
• Maintain backwards compatibility

Testing

Run the test suite:

python -m unittest discover tests

For coverage report:

coverage run -m unittest discover
coverage report

Future Roadmap

• Enhanced agent collaboration capabilities
• Additional specialized agents
• Advanced memory management system
• Web interface for agent management
• Multi-modal agent interactions
• Performance optimizations

License

This project is licensed under the MIT License - see the LICENSE file for details.