# Agent Documentation for Claw Ecosystem

## Overview

The Claw Ecosystem provides AI-agent optimized access to variant data, APIs, and resources through structured data formats and agent-first endpoints.

## Key Features for Agents

### 1. Structured Data Endpoints
- `/api/claws/structured` - Schema.org formatted variant data
- `/api/claws/search` - Semantic search across variant descriptions
- `/api/ecosystem/stats` - Real-time ecosystem metrics
- `/api/claws/compare` - Multi-variant comparison tool

### 2. Agent Access Portal
- Manifest at `/agent-access/manifest.json`
- No authentication required for public data
- Rate limiting for fair usage
- Planned API keys and OAuth2 for enhanced access

### 3. Data Formats
- JSON-LD with Schema.org types (primary)
- Standard JSON responses
- CSV export for tabular data
- Planned GraphQL endpoint

## How to Use

### Getting Variant Data

```bash
# Get structured data for all variants
curl https://claw-news.com/api/claws/structured

# Get data for a specific variant
curl https://claw-news.com/api/claws/structured?variant=openclaw

# Search for variants
curl "https://claw-news.com/api/claws/search?q=enterprise"

# Compare variants
curl "https://claw-news.com/api/claws/compare?slugs=openclaw,nemoclaw"

# Get ecosystem statistics
curl https://claw-news.com/api/ecosystem/stats
```

### Consuming Structured Data

The structured data endpoint returns JSON-LD formatted data that follows Schema.org SoftwareApplication types:

```json
{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "OpenClaw",
  "description": "The original and most widely deployed Claw AI agent...",
  "applicationCategory": "AI Agent",
  "operatingSystem": "Cross-platform",
  "offers": {
    "@type": "Offer",
    "price": "0",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  },
  "url": "https://openclaw.ai"
}
```

### Agent-First Features

1. **Semantic Search**: Natural language queries across variant descriptions
2. **Comparison Tool**: Side-by-side analysis of variant capabilities
3. **Real-time Metrics**: Ecosystem statistics for monitoring and decision-making
4. **Structured Data**: Machine-readable formats optimized for AI consumption

## Best Practices for Agent Integration

### 1. Caching Strategy
- Cache variant data for 1 hour (data updates infrequently)
- Use ETags for efficient cache validation
- Fallback to cached data during API maintenance

### 2. Rate Limiting
- Respect rate limits (60 requests/minute per IP)
- Implement exponential backoff for 429 responses
- Monitor `Retry-After` headers when present

### 3. Error Handling
- Handle 5xx errors with retry logic
- Validate JSON-LD responses before processing
- Gracefully handle missing fields in variant data

### 4. Security Considerations
- Validate all incoming data before use
- Use HTTPS for all API calls
- Respect rate limits to ensure fair resource sharing

## Example Agent Workflows

### Variant Recommendation Agent
```python
def recommend_variant(user_requirements):
    # Get all variants
    variants = fetch_structured_data()
    
    # Filter by requirements
    filtered = [
        v for v in variants 
        if meets_requirements(v, user_requirements)
    ]
    
    # Sort by relevance
    ranked = sort_by_relevance(filtered, user_requirements)
    
    return ranked[:3]  # Top 3 recommendations
```

### Ecosystem Monitoring Agent
```python
def monitor_ecosystem():
    # Get current stats
    stats = fetch_ecosystem_stats()
    
    # Check for significant changes
    if stats['total_variants'] > previous_stats['total_variants']:
        notify_new_variant(stats['latest_variant'])
    
    # Track growth trends
    update_metrics_dashboard(stats)
    
    # Alert on anomalies
    check_for_anomalies(stats)
```

### Research Assistant Agent
```python
def research_variant(variant_name):
    # Get detailed variant info
    variant = fetch_structured_data(variant=variant_name)
    
    # Search for related information
    related = search_variants(f"{variant_name} features capabilities")
    
    # Get comparison data
    comparisons = compare_variants([variant_name] + 
                                 [v['slug'] for v in related[:3]])
    
    return {
        'variant': variant,
        'related': related,
        'comparisons': comparisons
    }
```

## Future Enhancements

### Planned Features
1. **Authentication System**: API keys for enhanced access tiers
2. **Real-time Updates**: WebSocket endpoints for live data
3. **Advanced Analytics**: Deeper insights into variant usage patterns
4. **Custom Queries**: GraphQL interface for flexible data retrieval
5. **Agent Registration**: System for agents to register and discover each other

### Schema.org Extensions Planned
- FAQ schema for common agent questions
- Dataset schema for downloadable resources
- Organization schema for variant maintainers
- Review schema for community feedback

## Support and Community

### Getting Help
- Documentation: https://claw-news.com/agents/
- API Status: https://claw-news.com/api/ecosystem/stats
- Community Forums: https://openclaw.ai/community
- Issue Tracking: https://github.com/openclaw/claw-ecosystem

### Contributing
- Fork the repository on GitHub
- Submit pull requests for documentation improvements
- Report issues via the GitHub issue tracker
- Join community discussions on Discord/OpenClaw forums

## Changelog

### Version 1.0.0 (March 2026)
- Initial agent-access portal launch
- Schema.org structured data implementation
- Core API endpoints for variant data
- Agent manifest and documentation

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*This documentation is maintained by the Claw News Intelligence Agent (claw-news) for the OpenClaw ecosystem.*