revenium-middleware-anthropic 0.2.3

A Python library that meters Anthropic usage to Revenium.

🤖 Revenium Middleware for Anthropic

A middleware library for metering and monitoring Anthropic API usage in Python applications. 🐍✨

✨ Features

• 📊 Precise Usage Tracking: Monitor tokens, costs, and request counts across all Anthropic API endpoints
• 🔌 Seamless Integration: Drop-in middleware that works with minimal code changes
• ⚙️ Flexible Configuration: Customize metering behavior to suit your application needs

📥 Installation

pip install revenium-middleware-anthropic

🔧 Usage

🔄 Zero-Config Integration

Simply export your REVENIUM_METERING_API_KEY and import the middleware. Your Anthropic calls will be metered automatically:

import anthropic
import revenium_middleware_anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-3-7-sonnet-20250219",
    max_tokens=20000,
    temperature=1,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                     "text": "What is the meaning of life, the universe and everything?",
                }
            ]
        }
    ]
)
print(message.content)

The middleware automatically intercepts Anthropic API calls and sends metering data to Revenium without requiring any changes to your existing code. Make sure to set the REVENIUM_METERING_API_KEY environment variable for authentication with the Revenium service.

📈 Enhanced Tracking with Metadata

For more granular usage tracking and detailed reporting, add the usage_metadata parameter:

import anthropic
import revenium_middleware_anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-3-7-sonnet-20250219",
    max_tokens=20000,
    temperature=1,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What is the meaning of life, the universe and everything?",
                }
            ]
        }
    ],
    usage_metadata={
        "trace_id": "conv-28a7e9d4-1c3b-4e5f-8a9b-7d1e3f2c1b4a",
        "task_id": "chat-summary-af23c910",
        "task_type": "text-classification",
        "subscriber_identity": "[e-mail]",
        "organization_id": "acme-corporation-12345",
        "subscription_id": "startup-plan-quarterly-2025-Q1",
        "product_id": "intelligent-document-processor-v3",
        "source_id": "mobile-app-ios-v4.2",
        "ai_provider_key_name": "anthropic-production-key1",
        "agent": "customer-support-assistant-v2",
    }
)
print(message.content)

🏷️ Metadata Fields

The usage_metadata parameter supports the following fields:

Field	Description	Use Case
trace_id	Unique identifier for a conversation or session	Track multi-turn conversations
task_id	Identifier for a specific AI task	Group related API calls for a single task
task_type	Classification of the AI operation	Categorize usage by purpose (e.g., classification, summarization)
subscriber_identity	End-user identifier	Track usage by individual users
organization_id	Customer or department identifier	Allocate costs to business units
subscription_id	Reference to a billing plan	Associate usage with specific subscriptions
product_id	The product or feature using AI	Track usage across different products
source_id	Origin of the request	Monitor usage by platform or app version
ai_provider_key_name	Identifier for the API key used	Track usage by different API keys
agent	Identifier for the specific AI agent	Compare performance across different AI agents

All metadata fields are optional. Adding them enables more detailed reporting and analytics in Revenium.

🔄 Compatibility

• 🐍 Python 3.8+
• 🤖 Anthropic Python SDK

📚 Documentation

Full documentation is available at https://revenium-middleware-anthropic.readthedocs.io/

👥 Contributing

Contributions are welcome! Please check out our contributing guidelines for details.

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

📄 License

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

🙏 Acknowledgments

• 🔥 Thanks to the Anthropic team for creating an excellent API
• 💖 Built with ❤️ by the Revenium team