paid-python 0.0.5a6

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Paid is the all-in-one, drop-in Business Engine for AI Agents that handles your pricing, subscriptions, margins, billing, and renewals with just 5 lines of code. The Paid Python library provides convenient access to the Paid API from Python applications.

Documentation

See the full API docs here

Installation

You can install the package using pip:

pip install paid-python

Usage

The client needs to be configured with your account's API key, which is available in the Paid dashboard.

from paid import Paid

client = Paid(token="API_KEY")

client.customers.create(
    name="name"
)

Request And Response Types

The SDK provides Python classes for all request and response types. These are automatically handled when making API calls.

# Example of creating a customer
response = client.customers.create(
    name="John Doe",
)

# Access response data
print(response.name)
print(response.email)

Exception Handling

When the API returns a non-success status code (4xx or 5xx response), the SDK will raise an appropriate error.

try:
    client.customers.create(...)
except paid.Error as e:
    print(e.status_code)
    print(e.message)
    print(e.body)
    print(e.raw_response)

Logging

Supported log levels are DEBUG, INFO, WARNING, ERROR, and CRITICAL.

For example, to set the log level to debug, you can set the environment variable:

export PAID_LOG_LEVEL=DEBUG

Falls back to no logging.

Cost Tracking

It's possible to track usage costs by using Paid wrappers around you AI provider API. As of now, the following OpenAI python APIs are supported:

chat.completions.create()
responses.create()
images.generate()
embeddings.create()

Example usage:

from openai import OpenAI
from paid import Paid
from paid.tracing.wrappers import PaidOpenAI

# Initialize Paid SDK
client = Paid(token="PAID_API_KEY")

openAIClient = PaidOpenAI(OpenAI(
    # This is the default and can be omitted
    api_key="<OPENAI_API_KEY>",
))

# Initialize tracing, must be after initializeing Paid SKD
client.initialize_tracing()

def image_generate():
    response = openAIClient.images.generate(
        model="dall-e-3",
        prompt="A sunset over mountains",
        size="1024x1024",
        quality="hd",
        style="vivid",
        n=1
    )
    return response

# Finally, capture the traces!
_ = client.trace(external_customer_id = "<your_external_customer_id>",
                external_agent_id = "<your_external_agent_id>",  # can optionally include external_agent_id to enable agent-level cost tracking
                fn = lambda: image_generate())

Manual Cost Tracking

Manual cost tracking allow to insert your own costs to the usage data and cost traces will be created based on that info.

from paid import Paid, Signal

client = Paid(token="<PAID_API_KEY>")

signal = Signal(
    event_name="<your_signal_name>",
    agent_id="<your_agent_id>",
    customer_id="<your_external_customer_id>",
    data = {
        "costData": {
            "vendor": "<any_vendor_name>", # can be anything
            "cost": {
                "amount": 0.002,
                "currency": "USD"
            },
            "gen_ai.response.model": "<ai_model_name>",
        }
    }
)

_ = client.usage.record_bulk(signals=[signal])

Contributing

While we value open-source contributions to this SDK, this library is generated programmatically. Additions made directly to this library would have to be moved over to our generation code, otherwise they would be overwritten upon the next generated release. Feel free to open a PR as a proof of concept, but know that we will not be able to merge it as-is. We suggest opening an issue first to discuss with us!

On the other hand, contributions to the README are always very welcome!