Marketplace Development Guide
This guide provides best practices, patterns, and resources for developing and monetizing agents for the AI Agent Orchestration Platform marketplace.
1. Introduction to the Agent Marketplace
The marketplace enables developers to publish, distribute, and monetize their agents, templates, and plugins while allowing users to discover and integrate these components into their workflows. Key benefits include:
- Monetization: Generate revenue from your agent development work
- Distribution: Reach a wider audience for your specialized agents
- Discovery: Find agents that solve specific problems
- Integration: Easily incorporate marketplace agents into workflows
- Quality Assurance: Benefit from community ratings and certification
- Ecosystem Growth: Contribute to a vibrant agent ecosystem
2. Marketplace Architecture
2.1 Marketplace Components
marketplace/
├── registry/
│ ├── agent_registry.py
│ ├── metadata_validator.py
│ └── search_index.py
├── monetization/
│ ├── pricing_models.py
│ ├── billing_manager.py
│ └── revenue_sharing.py
├── quality/
│ ├── certification.py
│ ├── testing_framework.py
│ └── rating_system.py
├── discovery/
│ ├── search_engine.py
│ ├── recommendation_system.py
│ └── category_manager.py
└── integration/
├── deployment_manager.py
├── version_manager.py
└── dependency_resolver.py
2.2 Agent Package Structure
agent-package/
├── agent.py # Main agent implementation
├── requirements.txt # Dependencies
├── metadata.json # Agent metadata and marketplace info
├── README.md # Documentation
├── LICENSE # License information
├── tests/ # Test suite
│ ├── test_agent.py
│ └── test_integration.py
├── examples/ # Example usage
│ ├── basic_example.py
│ └── advanced_example.py
└── resources/ # Additional resources
├── models/ # Pre-trained models
├── data/ # Sample data
└── images/ # Images for documentation
3. Agent Development for the Marketplace
3.1 Agent Design Principles
- Single Responsibility: Focus on doing one thing well
- Clear Interface: Define clear inputs and outputs
- Robust Error Handling: Gracefully handle edge cases
- Comprehensive Documentation: Provide clear usage instructions
- Thorough Testing: Include comprehensive test suite
- Minimal Dependencies: Reduce external dependencies
- Configurable: Allow customization through parameters
- Versioning: Follow semantic versioning
- Security: Follow security best practices
- Privacy: Respect user data privacy
3.2 Agent Metadata Schema
{
"agent_id": "unique-agent-identifier",
"name": "Agent Name",
"version": "1.0.0",
"description": "A concise description of what the agent does",
"long_description": "A more detailed description of the agent's capabilities, use cases, and limitations",
"author": {
"name": "Author Name",
"email": "author@example.com",
"url": "https://author-website.com"
},
"license": "MIT",
"categories": ["category1", "category2"],
"tags": ["tag1", "tag2", "tag3"],
"inputs": [
{
"name": "input_parameter",
"type": "string",
"description": "Description of the input parameter",
"required": true,
"default": null
}
],
"outputs": [
{
"name": "output_parameter",
"type": "object",
"description": "Description of the output parameter"
}
],
"resources": {
"memory_mb": 256,
"cpu_units": 1,
"gpu_required": false,
"disk_mb": 100
},
"pricing": {
"model": "per_use",
"price_per_use": 0.01,
"currency": "USD",
"volume_discounts": [
{"threshold": 1000, "discount_percentage": 10},
{"threshold": 10000, "discount_percentage": 20}
]
},
"documentation_url": "https://docs.example.com/agent",
"repository_url": "https://github.com/example/agent",
"support_url": "https://support.example.com/agent",
"examples": [
{
"name": "Basic Example",
"description": "A simple example of how to use the agent",
"code": "import agent\nresult = agent.process('input')\nprint(result)"
}
],
"compatibility": {
"platform_version": ">=1.0.0",
"python_version": ">=3.8",
"frameworks": ["tensorflow>=2.0.0", "pytorch>=1.8.0"]
},
"certifications": [],
"rating": {
"average": 0,
"count": 0
},
"created_at": "2023-01-01T00:00:00Z",
"updated_at": "2023-01-01T00:00:00Z"
}
3.3 Agent Implementation Template
from typing import Dict, Any, List, Optional
import logging
class MarketplaceAgent:
"""Template for a marketplace-ready agent."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
"""Initialize the agent with configuration.
Args:
config: Optional configuration dictionary
"""
self.config = config or {}
self.logger = logging.getLogger(self.__class__.__name__)
self._setup()
def _setup(self):
"""Set up the agent (load models, initialize resources, etc.)."""
try:
# Implementation-specific setup
self.logger.info("Agent setup complete")
except Exception as e:
self.logger.error(f"Error during agent setup: {str(e)}")
raise
def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
"""Process input data and return results.
Args:
input_data: Input data dictionary
Returns:
Dictionary containing the processing results
Raises:
ValueError: If input data is invalid
RuntimeError: If processing fails
"""
try:
# Validate input
self._validate_input(input_data)
# Process input (implementation-specific)
result = self._process_implementation(input_data)
# Validate output
self._validate_output(result)
return result
except ValueError as e:
self.logger.error(f"Input validation error: {str(e)}")
raise
except Exception as e:
self.logger.error(f"Processing error: {str(e)}")
raise RuntimeError(f"Failed to process input: {str(e)}")
def _validate_input(self, input_data: Dict[str, Any]):
"""Validate input data.
Args:
input_data: Input data to validate
Raises:
ValueError: If input data is invalid
"""
# Implementation-specific validation
pass
def _process_implementation(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
"""Implementation-specific processing logic.
Args:
input_data: Validated input data
Returns:
Processing results
"""
# Implementation-specific processing
return {"result": "Not implemented"}
def _validate_output(self, output_data: Dict[str, Any]):
"""Validate output data.
Args:
output_data: Output data to validate
Raises:
ValueError: If output data is invalid
"""
# Implementation-specific validation
pass
def get_metadata(self) -> Dict[str, Any]:
"""Get agent metadata.
Returns:
Dictionary containing agent metadata
"""
return {
"name": self.__class__.__name__,
"description": self.__doc__ or "No description available",
"version": getattr(self, "__version__", "0.1.0"),
"author": getattr(self, "__author__", "Unknown"),
"license": getattr(self, "__license__", "Unknown"),
"inputs": self._get_input_schema(),
"outputs": self._get_output_schema()
}
def _get_input_schema(self) -> List[Dict[str, Any]]:
"""Get input schema.
Returns:
List of input parameter definitions
"""
# Implementation-specific input schema
return []
def _get_output_schema(self) -> List[Dict[str, Any]]:
"""Get output schema.
Returns:
List of output parameter definitions
"""
# Implementation-specific output schema
return []
def cleanup(self):
"""Clean up resources used by the agent."""
# Implementation-specific cleanup
self.logger.info("Agent cleanup complete")
4. Monetization Strategies
4.1 Pricing Models
- Per-Use: Charge per agent invocation
- Subscription: Charge a recurring fee for unlimited usage
- Tiered: Offer different pricing tiers based on usage limits
- Freemium: Offer basic functionality for free with premium features
- Pay-What-You-Want: Allow users to determine the price
- Usage-Based: Charge based on resource consumption
- Outcome-Based: Charge based on the value of the outcome
4.2 Revenue Sharing Model
The platform implements a revenue sharing model where:
- Developer Share: 80% of revenue goes to the agent developer
- Platform Fee: 20% of revenue goes to the platform
- Payment Processing: Payment processing fees are deducted from the platform fee
- Payout Schedule: Payments are processed monthly for balances over $50
- Tax Reporting: Developers are responsible for tax reporting
4.3 Monetization Implementation
import uuid
import time
from enum import Enum
from typing import Dict, List, Any, Optional
class PricingModel(Enum):
PER_USE = "per_use"
SUBSCRIPTION = "subscription"
TIERED = "tiered"
FREEMIUM = "freemium"
PAY_WHAT_YOU_WANT = "pay_what_you_want"
USAGE_BASED = "usage_based"
OUTCOME_BASED = "outcome_based"
class AgentMonetization:
"""Manage agent monetization."""
def __init__(self, agent_id, pricing_config):
"""Initialize monetization for an agent.
Args:
agent_id: Unique identifier for the agent
pricing_config: Pricing configuration
"""
self.agent_id = agent_id
self.pricing_config = pricing_config
self.pricing_model = PricingModel(pricing_config.get("model", "per_use"))
self.usage_log = []
def calculate_price(self, usage_context=None):
"""Calculate the price for agent usage.
Args:
usage_context: Context for the usage (e.g., resource consumption)
Returns:
Price for the usage
"""
if self.pricing_model == PricingModel.PER_USE:
return self._calculate_per_use_price(usage_context)
elif self.pricing_model == PricingModel.SUBSCRIPTION:
return self._calculate_subscription_price(usage_context)
elif self.pricing_model == PricingModel.TIERED:
return self._calculate_tiered_price(usage_context)
elif self.pricing_model == PricingModel.FREEMIUM:
return self._calculate_freemium_price(usage_context)
elif self.pricing_model == PricingModel.PAY_WHAT_YOU_WANT:
return self._calculate_pay_what_you_want_price(usage_context)
elif self.pricing_model == PricingModel.USAGE_BASED:
return self._calculate_usage_based_price(usage_context)
elif self.pricing_model == PricingModel.OUTCOME_BASED:
return self._calculate_outcome_based_price(usage_context)
else:
return 0.0
def _calculate_per_use_price(self, usage_context):
"""Calculate per-use price."""
base_price = self.pricing_config.get("price_per_use", 0.0)
# Apply volume discounts if applicable
volume_discounts = self.pricing_config.get("volume_discounts", [])
user_usage_count = self._get_user_usage_count(usage_context.get("user_id"))
discount_percentage = 0
for discount in sorted(volume_discounts, key=lambda d: d["threshold"], reverse=True):
if user_usage_count >= discount["threshold"]:
discount_percentage = discount["discount_percentage"]
break
discounted_price = base_price * (1 - discount_percentage / 100)
return discounted_price
def _calculate_subscription_price(self, usage_context):
"""Calculate subscription price."""
# For subscription model, individual usages are free
# The subscription fee is charged separately
return 0.0
def _calculate_tiered_price(self, usage_context):
"""Calculate tiered price."""
tiers = self.pricing_config.get("tiers", [])
user_usage_count = self._get_user_usage_count(usage_context.get("user_id"))
for tier in tiers:
if user_usage_count < tier.get("limit", float("inf")):
return tier.get("price_per_use", 0.0)
return tiers[-1].get("price_per_use", 0.0) if tiers else 0.0
def _calculate_freemium_price(self, usage_context):
"""Calculate freemium price."""
features_used = usage_context.get("features", [])
premium_features = self.pricing_config.get("premium_features", {})
total_price = 0.0
for feature in features_used:
if feature in premium_features:
total_price += premium_features[feature]
return total_price
def _calculate_pay_what_you_want_price(self, usage_context):
"""Calculate pay-what-you-want price."""
return usage_context.get("user_specified_price", 0.0)
def _calculate_usage_based_price(self, usage_context):
"""Calculate usage-based price."""
resource_usage = usage_context.get("resource_usage", {})
resource_rates = self.pricing_config.get("resource_rates", {})
total_price = 0.0
for resource, usage in resource_usage.items():
if resource in resource_rates:
total_price += usage * resource_rates[resource]
return total_price
def _calculate_outcome_based_price(self, usage_context):
"""Calculate outcome-based price."""
outcome_value = usage_context.get("outcome_value", 0.0)
value_percentage = self.pricing_config.get("value_percentage", 0.0)
return outcome_value * (value_percentage / 100)
def _get_user_usage_count(self, user_id):
"""Get the number of times a user has used this agent."""
if not user_id:
return 0
return sum(1 for log in self.usage_log if log.get("user_id") == user_id)
def log_usage(self, user_id, usage_context=None):
"""Log agent usage.
Args:
user_id: ID of the user using the agent
usage_context: Context for the usage
Returns:
Usage record ID
"""
usage_id = str(uuid.uuid4())
usage_record = {
"usage_id": usage_id,
"agent_id": self.agent_id,
"user_id": user_id,
"timestamp": time.time(),
"context": usage_context or {},
"price": self.calculate_price(usage_context)
}
self.usage_log.append(usage_record)
# In a real implementation, this would be persisted to a database
print(f"[MONETIZATION] USAGE LOGGED - Agent: {self.agent_id}, User: {user_id}, Price: {usage_record['price']}")
return usage_id
def get_revenue_report(self, start_time=None, end_time=None):
"""Get revenue report for a time period.
Args:
start_time: Start time for the report (timestamp)
end_time: End time for the report (timestamp)
Returns:
Revenue report
"""
filtered_logs = self.usage_log
if start_time:
filtered_logs = [log for log in filtered_logs if log["timestamp"] >= start_time]
if end_time:
filtered_logs = [log for log in filtered_logs if log["timestamp"] <= end_time]
total_revenue = sum(log["price"] for log in filtered_logs)
usage_count = len(filtered_logs)
developer_share = total_revenue * 0.8 # 80% to developer
platform_fee = total_revenue * 0.2 # 20% to platform
return {
"agent_id": self.agent_id,
"start_time": start_time,
"end_time": end_time,
"usage_count": usage_count,
"total_revenue": total_revenue,
"developer_share": developer_share,
"platform_fee": platform_fee
}
5. Quality Assurance
5.1 Testing Requirements
- Unit Tests: Test individual agent components
- Integration Tests: Test agent integration with the platform
- Performance Tests: Test agent performance under load
- Security Tests: Test agent security
- Edge Case Tests: Test agent behavior with unusual inputs
- Documentation Tests: Test agent documentation
5.2 Certification Process
- Submission: Submit agent for certification
- Automated Testing: Agent undergoes automated testing
- Manual Review: Platform team reviews agent code and documentation
- Security Scan: Agent undergoes security scanning
- Performance Benchmarking: Agent performance is benchmarked
- Certification Decision: Agent is certified or rejected with feedback
- Publication: Certified agent is published to the marketplace
5.3 Rating System
- User Ratings: Users can rate agents on a 1-5 scale
- Review Comments: Users can leave detailed reviews
- Usage Statistics: Usage statistics are displayed
- Certification Badge: Certified agents display a badge
- Featured Agents: High-quality agents are featured
6. Publishing Process
6.1 Publishing Steps
- Develop Agent: Implement agent following best practices
- Create Metadata: Create agent metadata file
- Write Documentation: Write comprehensive documentation
- Create Tests: Write thorough tests
- Package Agent: Package agent for distribution
- Submit for Review: Submit agent for platform review
- Address Feedback: Address any feedback from review
- Set Pricing: Configure pricing model
- Publish: Publish agent to the marketplace
- Maintain: Provide updates and support
6.2 Version Management
- Semantic Versioning: Follow semantic versioning (MAJOR.MINOR.PATCH)
- Changelog: Maintain a detailed changelog
- Deprecation Policy: Clearly communicate deprecation plans
- Backward Compatibility: Maintain backward compatibility when possible
- Version Support: Define support period for each version
7. Discovery and Integration
7.1 Discovery Optimization
- Descriptive Title: Use a clear, descriptive title
- Concise Description: Write a concise, compelling description
- Relevant Categories: Choose appropriate categories
- Descriptive Tags: Use relevant tags
- Quality Documentation: Provide comprehensive documentation
- Example Use Cases: Include example use cases
- Demo Videos: Create demo videos
- Screenshots: Include screenshots of the agent in action
7.2 Integration Best Practices
- Clear Interface: Define clear inputs and outputs
- Comprehensive Documentation: Document integration steps
- Example Code: Provide example integration code
- Error Handling: Document error handling
- Versioning: Support multiple versions
- Dependencies: Minimize and document dependencies
- Configuration: Document configuration options
- Testing: Provide integration tests
8. Resources and References
- Platform Documentation: Platform Docs
- API Reference: API Reference
- Developer Forum: Developer Forum
- GitHub Repository: GitHub
- Sample Agents: Sample Agents
- Monetization Guide: Monetization Guide
- Quality Assurance Guide: QA Guide
This guide will evolve as the platform's marketplace capabilities expand. Contribute your insights and improvements to help build a robust marketplace ecosystem.