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To get started, create an account by clicking “Log in” on PromptLayer. Once logged in, click the button to create an API key and save this in a secure location (Guide to Using Env Vars). Once you have that all set up, install PromptLayer using pip. 
pip install promptlayer
PromptLayer python library has support for both OpenAI and Anthropic LLMs! Set up a PromptLayer client in your python file. 
from promptlayer import PromptLayer
promptlayer_client = PromptLayer()
Optionally, you can specify the API key and base URL in the client. 
promptlayer_client = PromptLayer(api_key="pl_****", base_url="https://api.promptlayer.com")

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OpenAI

In the Python file where you use OpenAI APIs, add the following. This allows us to keep track of your requests without needing any other code changes. 
from promptlayer import PromptLayer
promptlayer_client = PromptLayer()

OpenAI = promptlayer_client.openai.OpenAI
client = OpenAI()
You can then use openai as you would if you had imported it directly.
Your OpenAI API Key is never sent to our servers. All OpenAI requests are made locally from your machine, PromptLayer just logs the request.
There is only one difference… PromptLayer allows you to add tags through the pl_tags argument. This allows you to track and group requests in the dashboard. Tags are not required but we recommend them! 
completion = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are an AI."},
    {"role": "user", "content": "Compose a poem please."}
  ],
  pl_tags=["getting-started"]
)
After making your first few requests, you should be able to see them in the PromptLayer dashboard! Here is a complete code snippet: 
from promptlayer import PromptLayer
promptlayer_client = PromptLayer()

# Swap out 'from openai import OpenAI'
OpenAI = promptlayer_client.openai.OpenAI

client = OpenAI()
completion = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are an AI."},
    {"role": "user", "content": "Compose a poem please."}
  ],
  pl_tags=["getting-started"]
)
print(completion.choices[0].message)

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Anthropic

Using Anthropic with PromptLayer is very similar to how to one would use OpenAI. Below is an example code snippet of the one line replacement: 
from promptlayer import PromptLayer
promptlayer_client = PromptLayer()

# Swap out 'from anthropic import Anthropic'
anthropic = promptlayer_client.anthropic

client = anthropic.Anthropic()

completion = client.completions.create(
    prompt=f'{anthropic.HUMAN_PROMPT} How many toes do dogs have? {anthropic.AI_PROMPT}',
    stop_sequences=[anthropic.HUMAN_PROMPT],
    model='claude-v1-100k',
    max_tokens_to_sample=100,
    pl_tags=['animal-toes']
)

print(completion)
Here is how it would look like on the dashbaord:

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Error Handling

PromptLayer provides robust error handling with specialized exception classes and configurable error behavior.

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Exception Classes

The library includes specific exception types following industry best practices: 
from promptlayer import (
    PromptLayerAPIError,              # General API errors
    PromptLayerBadRequestError,       # 400 errors
    PromptLayerAuthenticationError,   # 401 errors
    PromptLayerNotFoundError,         # 404 errors
    PromptLayerValidationError,       # Input validation errors
    PromptLayerAPIConnectionError,    # Connection failures
    PromptLayerAPITimeoutError,       # Timeout errors
    PromptLayerRateLimitError,        # 429 rate limit errors
)

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Using throw_on_error

By default, PromptLayer throws exceptions when errors occur. You can control this behavior using the throw_on_error parameter: 
from promptlayer import PromptLayer

# Default behavior: throws exceptions on errors
promptlayer_client = PromptLayer(api_key="pl_****", throw_on_error=True)

# Alternative: logs warnings instead of throwing exceptions
promptlayer_client = PromptLayer(api_key="pl_****", throw_on_error=False)
Example with exception handling: 
from promptlayer import PromptLayer, PromptLayerNotFoundError, PromptLayerValidationError

promptlayer_client = PromptLayer()

try:
    # Attempt to get a template that might not exist
    template = promptlayer_client.templates.get("NonExistentTemplate")
except PromptLayerNotFoundError as e:
    print(f"Template not found: {e}")
except PromptLayerValidationError as e:
    print(f"Invalid input: {e}")
Example with warnings (throw_on_error=False): 
from promptlayer import PromptLayer

# Initialize with throw_on_error=False to get warnings instead of exceptions
promptlayer_client = PromptLayer(throw_on_error=False)

# This will log a warning instead of throwing an exception if the template doesn't exist
template = promptlayer_client.templates.get("NonExistentTemplate")
# Returns None if not found, with a warning logged

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Automatic Retry Mechanism

PromptLayer includes a built-in retry mechanism to handle transient failures gracefully. This ensures your application remains resilient when temporary issues occur. Retry Behavior:
  • Total Attempts: 4 attempts (1 initial + 3 retries)
  • Exponential Backoff: Retries wait progressively longer between attempts (2s, 4s, 8s)
  • Max Wait Time: 15 seconds maximum wait between retries
What Triggers Retries:
  • 5xx Server Errors: Internal server errors, service unavailable, etc.
  • 429 Rate Limit Errors: When rate limits are exceeded
What Fails Immediately (No Retries):
  • Connection Errors: Network connectivity issues
  • Timeout Errors: Request timeouts
  • 4xx Client Errors (except 429): Bad requests, authentication errors, not found, etc.
The retry mechanism operates transparently in the background. You don’t need to implement retry logic yourself - PromptLayer handles it automatically for recoverable errors.

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Logging

PromptLayer uses Python’s built-in logging module for all log output: 
import logging
from promptlayer import PromptLayer

# Configure logging to see PromptLayer logs
logging.basicConfig(level=logging.INFO)

promptlayer_client = PromptLayer()

# Now you'll see log output from PromptLayer operations
Setting log levels: 
import logging

# Get the PromptLayer logger
logger = logging.getLogger("promptlayer")

# Set to WARNING to only see warnings and errors
logger.setLevel(logging.WARNING)

# Set to DEBUG to see detailed information
logger.setLevel(logging.DEBUG)
Viewing Retry Logs: When retries occur, PromptLayer logs warnings before each retry attempt: 
import logging
from promptlayer import PromptLayer

# Set up logging to see retry attempts
logging.basicConfig(
    level=logging.WARNING,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

promptlayer_client = PromptLayer()

# If a retry occurs, you'll see log messages like:
# "Retrying in 2 seconds..."
# "Retrying in 4 seconds..."

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Async Support

PromptLayer supports asynchronous operations, ideal for managing concurrent tasks in non-blocking environments like web servers, microservices, or Jupyter notebooks.

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Initializing the Async Client

To use asynchronous non-blocking methods, initialize AsyncPromptLayer as shown: 
from promptlayer import AsyncPromptLayer

# Initialize an asynchronous client with your API key
async_promptlayer_client = AsyncPromptLayer(api_key="pl_****")

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Async Usage Examples

The asynchronous client functions similarly to the synchronous version, but allows for non-blocking execution with asyncio. Below are example uses.

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Example 1: Async Template Management

Use asynchronous methods to manage templates: 
import asyncio
from promptlayer import AsyncPromptLayer

async def main():
    async_promptlayer_client = AsyncPromptLayer(api_key="pl_****")

    # Fetch a template asynchronously
    template = await async_promptlayer_client.templates.get("Test1")
    print(template)

    # Fetch all templates asynchronously
    templates = await async_promptlayer_client.templates.all()
    print(templates)

# Run the async function
asyncio.run(main())

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Example 2: Async Agent Execution

Run Agents asynchronously for better efficiency: 
import asyncio
from promptlayer import AsyncPromptLayer

async def main():
    async_promptlayer_client = AsyncPromptLayer(api_key="pl_****")

    response = await async_promptlayer_client.run_workflow(
        workflow_name="example_agent",
        workflow_version=1,
        input_variables={"num1": "1", "num2": "2"},
        return_all_outputs=True,
    )
    print(response)

# Run the async function
asyncio.run(main())

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Example 3: Async Tracking and Logging

Track and log requests asynchronously: 
import asyncio
from promptlayer import AsyncPromptLayer

async def main():
    async_promptlayer_client = AsyncPromptLayer(api_key="pl_****")

    # Track metadata asynchronously
    request_id = "pl_request_id_example"
    await async_promptlayer_client.track.metadata(request_id, {"key": "value"})

    # Log request asynchronously (for detailed logging, refer to the custom logging page)
    await async_promptlayer_client.log_request(
        provider="openai",
        model="gpt-3.5-turbo",
        input=prompt_template,
        output=output_template,
        request_start_time=1630945600,
        request_end_time=1630945605,
    )

# Run the async function
asyncio.run(main())
For more information on custom logging, please visit our Custom Logging Documentation.

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Example 4: Asynchronous Prompt Execution with run Method

You can execute prompt templates asynchronously using the run method. This allows you to run a prompt template by name with given input variables. 
import asyncio
from promptlayer import AsyncPromptLayer

async def main():
    async_promptlayer_client = AsyncPromptLayer(api_key="pl_****")

    # Execute a prompt template asynchronously
    response = await async_promptlayer_client.run(
        prompt_name="TestPrompt",
        input_variables={"variable1": "value1", "variable2": "value2"}
    )
    print(response)

# Run the async function
asyncio.run(main())

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Example 5: Asynchronous Streaming Prompt Execution with run Method

You can run streaming prompt template using the run method as well. 

import asyncio
import os
from promptlayer import AsyncPromptLayer


async def main():
    async_promptlayer_client = AsyncPromptLayer(
        api_key=os.environ.get("PROMPTLAYER_API_KEY")
    )

    response_generator = await async_promptlayer_client.run(
        prompt_name="TestPrompt",
        input_variables={"variable1": "value1", "variable2": "value2"}, stream=True
    )

    final_response = ""
    async for response in response_generator:
        # Access raw streaming response
        print("Raw streaming response:", response["raw_response"])
        
        # Access progressively built prompt blueprint
        if response["prompt_blueprint"]:
            current_response = response["prompt_blueprint"]["prompt_template"]["messages"][-1]
            if current_response.get("content"):
                print(f"Current response: {current_response['content']}")

# Run the async function
asyncio.run(main())
In this example, replace “TestPrompt” with the name of your prompt template, and provide any required input variables. When streaming is enabled, each chunk includes both the raw streaming response and the progressively built prompt_blueprint, allowing you to track how the response is constructed in real-time.

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Supported Methods: Synchronous vs. Asynchronous

The following table provides an overview of the methods currently available in both synchronous and asynchronous versions of the PromptLayer client:
MethodDescriptionSynchronous VersionAsynchronous Version
templates.get()Retrieves a template by name.promptlayer_client.templates.get()async_promptlayer_client.templates.get()
templates.all()Retrieves all templates.promptlayer_client.templates.all()async_promptlayer_client.templates.all()
run()Executes a prompt template.promptlayer_client.run()async_promptlayer_client.run()
run_workflow()Executes an Agent.promptlayer_client.run_workflow()async_promptlayer_client.run_workflow()
track.metadata()Tracks metadata.promptlayer_client.track.metadata()async_promptlayer_client.track.metadata()
track.group()Tracks a group.promptlayer_client.track.group()async_promptlayer_client.track.group()
track.prompt()Tracks a prompt.promptlayer_client.track.prompt()async_promptlayer_client.track.prompt()
track.score()Tracks a score.promptlayer_client.track.score()async_promptlayer_client.track.score()
group.create()Creates a new group.promptlayer_client.group.create()async_promptlayer_client.group.create()
log_request()Logs a request.promptlayer_client.log_request()async_promptlayer_client.log_request()
Note: All asynchronous methods require an active event loop. Use them within an async function and run the function using asyncio.run() or another method suitable for managing event loops (e.g., await in Jupyter notebooks).
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