# User Guide

This section contains detailed guides for working with Atomic Agents.

```{toctree}
:maxdepth: 2
:caption: Guides

quickstart
memory
tools
hooks
orchestration
cookbook
error-handling
testing
deployment
performance
security
logging
faq
```

## Implementation Patterns

The framework supports various implementation patterns and use cases:

### Chatbots and Assistants

- Basic chat interfaces with any LLM provider
- Streaming responses
- Custom response schemas
- Suggested follow-up questions
- History management and context retention
- Multi-turn conversations

### RAG Systems

- Query generation and optimization
- Context-aware responses
- Document Q&A with source tracking
- Information synthesis and summarization
- Custom embedding and retrieval strategies
- Hybrid search approaches

### Specialized Agents

- YouTube video summarization and analysis
- Web search and deep research
- Recipe generation from various sources
- Multimodal interactions (text, images, etc.)
- Custom tool integration
- Custom MCP integration to support tools, resources, and prompts
- Task orchestration

## Provider Integration Guide

Atomic Agents is designed to be provider-agnostic. Here's how to work with different providers:

### Provider Selection

- Choose any provider supported by Instructor
- Configure provider-specific settings
- Handle rate limits and quotas
- Implement fallback strategies

### Local Development

- Use Ollama for local testing
- Mock responses for development
- Debug provider interactions
- Test provider switching

### Production Deployment

- Load balancing between providers
- Failover configurations
- Cost optimization strategies
- Performance monitoring

### Custom Provider Integration

- Extend Instructor for new providers
- Implement custom client wrappers
- Add provider-specific features
- Handle unique response formats

## Best Practices

### Error Handling

- Implement proper exception handling
- Add retry mechanisms
- Log provider errors
- Handle rate limits gracefully

### Performance Optimization

- Use streaming for long responses
- Implement caching strategies
- Optimize prompt lengths
- Batch operations when possible

### Security

- Secure API key management
- Input validation and sanitization
- Output filtering
- Rate limiting and quotas

## Getting Help

If you need help, you can:

1. Check our [GitHub Issues](https://github.com/BrainBlend-AI/atomic-agents/issues)
2. Join our [Reddit community](https://www.reddit.com/r/AtomicAgents/)
3. Read through our examples in the repository
4. Review the example projects in `atomic-examples/`

**See also**:
- [API Reference](/api/index) - Browse the API reference
- [Main Documentation](/index) - Return to main documentation
