> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pilottai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Customer service

# PilottAI Agent Examples

This directory contains example implementations showing how to create and use different types of agents with the PilottAI framework.

## Overview

The examples demonstrate how to:

* Set up multiple specialized agents
* Create and configure tools
* Execute jobs across different agents
* Use the PilottAI Serve orchestrator

## Installation

1. Install PilottAI:

```bash theme={null}
pip install pilott
```

2. Set up your OpenAI API key:

```bash theme={null}
export OPENAI_API_KEY="your-api-key"
```

## Included Examples

### Agents

* **Customer Service Agent**: Handles customer inquiries and support requests
* **Document Processor**: Processes and analyzes documents
* **Research Analyst**: Conducts research and provides insights

### Tools

* **Email Sender**: Tool for sending emails to customers
* **Document Processor**: Tool for document analysis and processing

## Usage

Run the examples:

```python theme={null}
from examples.agents import main

# Run the example
import asyncio
asyncio.run(main())
```

## Example Output

```
Job: Handle refund request
Result: Customer refund request processed successfully

Job: Analyze quarterly report
Result: Document analysis complete: 3 key insights found

Job: Research competitor pricing
Result: Market research analysis completed
```

## Creating Your Own Agents

1. Configure the agent:

```python theme={null}
agent_config = AgentConfig(
    title="your_agent_title",
    goal="your_agent_goal",
    tools=["tool1", "tool2"]
)
```

2. Add to PilottAI:

```python theme={null}
agent = await pilott.add_agent(
    title=agent_config.title,
    goal=agent_config.goal,
    tools=agent_config.tools,
    llm_config=llm_config
)
```

## Best Practices

1. **Agent Design**
   * Give each agent a clear, focused title
   * Provide specific goals and tools
   * Use appropriate LLM configurations

2. **Tool Management**
   * Create reusable tools
   * Define clear tool interfaces
   * Handle tool errors gracefully

3. **Job Execution**
   * Group related jobs
   * Set appropriate priorities
   * Monitor execution results

## Configuration Options

### LLM Configuration

```python theme={null}
llm_config = LLMConfig(
    model_name="gpt-4",  # or other models
    provider="openai",   # or other providers
    temperature=0.7     # adjust based on needs
)
```

### Tool Configuration

```python theme={null}
tool = Tool(
    name="tool_name",
    description="tool_description",
    function=your_function,
    parameters={
        "param1": "type1",
        "param2": "type2"
    }
)
```

## Error Handling

The examples include basic error handling. In production, you should:

* Add comprehensive error handling
* Implement retries for failed jobs
* Log errors appropriately
* Handle API rate limits

## Contributing

Feel free to:

* Add new agent examples
* Create additional tools
* Improve documentation
* Report issues
* Submit pull requests

## Code

Ready to use code [customer\_service.py](../../pilott/agents/customer_service.py)