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Create new account in Keywords AI
Go to Keywords AI and create a new account.
Get your API key from the API Keys page in Settings.
Set up Traces Python JS/TS OpenAI Agents SDK
Install the SDK
Install the package using your preferred package manager:
pip install keywordsai-tracing
Set up Environment Variables
Get your API key from the API Keys page in Settings, then configure it in your environment:
KEYWORDSAI_BASE_URL = "https://api.keywordsai.co/api" KEYWORDSAI_API_KEY = "YOUR_KEYWORDSAI_API_KEY"
Annotate your workflows
Use the @workflow and @task decorators to instrument your code:
from keywordsai_tracing.decorators import workflow, task from keywordsai_tracing.main import KeywordsAITelemetry k_tl = KeywordsAITelemetry() @workflow ( name = "my_workflow" ) def my_workflow (): @task ( name = "my_task" ) def my_task (): pass my_task()
A full example with LLM calls
In this example, you will see how to implement a workflow that includes LLM calls. We use OpenAI SDK as an example.
from openai import OpenAI from keywordsai_tracing.decorators import workflow, task from keywordsai_tracing.main import KeywordsAITelemetry k_tl = KeywordsAITelemetry() client = OpenAI() @workflow ( name = "create_joke" ) def create_joke (): completion = client.chat.completions.create( model = "gpt-4o-mini" , messages = [{ "role" : "user" , "content" : "Tell me a joke about opentelemetry" }], temperature = 0.5 , max_tokens = 100 , frequency_penalty = 0.5 , presence_penalty = 0.5 , ) return completion.choices[ 0 ].message.content
Install the SDK
Install the package using your preferred package manager:
pip install keywordsai-tracing
Set up Environment Variables
Get your API key from the API Keys page in Settings, then configure it in your environment:
KEYWORDSAI_BASE_URL = "https://api.keywordsai.co/api" KEYWORDSAI_API_KEY = "YOUR_KEYWORDSAI_API_KEY"
Annotate your workflows
Use the @workflow and @task decorators to instrument your code:
from keywordsai_tracing.decorators import workflow, task from keywordsai_tracing.main import KeywordsAITelemetry k_tl = KeywordsAITelemetry() @workflow ( name = "my_workflow" ) def my_workflow (): @task ( name = "my_task" ) def my_task (): pass my_task()
A full example with LLM calls
In this example, you will see how to implement a workflow that includes LLM calls. We use OpenAI SDK as an example.
from openai import OpenAI from keywordsai_tracing.decorators import workflow, task from keywordsai_tracing.main import KeywordsAITelemetry k_tl = KeywordsAITelemetry() client = OpenAI() @workflow ( name = "create_joke" ) def create_joke (): completion = client.chat.completions.create( model = "gpt-4o-mini" , messages = [{ "role" : "user" , "content" : "Tell me a joke about opentelemetry" }], temperature = 0.5 , max_tokens = 100 , frequency_penalty = 0.5 , presence_penalty = 0.5 , ) return completion.choices[ 0 ].message.content
Install the SDK
Install the package using your preferred package manager:
npm install @keywordsai/tracing # or yarn yarn add @keywordsai/tracing
Set up Environment Variables
Get your API key from the API Keys page in Settings, then configure it in your environment:
KEYWORDSAI_BASE_URL = "https://api.keywordsai.co/api" KEYWORDSAI_API_KEY = "YOUR_KEYWORDSAI_API_KEY"
Create a simple task
import { KeywordsAITelemetry } from '@keywordsai/tracing' ; import OpenAI from 'openai' ; // Initialize clients // Make sure to set these environment variables or pass them directly const keywordsAi = new KeywordsAITelemetry ({ apiKey: process . env . KEYWORDSAI_API_KEY || "" , appName: 'test-app' , disableBatch: true // For testing, disable batching }); const openai = new OpenAI (); // This demonstrates a simple LLM call wrapped in a task async function createJoke () { return await keywordsAi . withTask ( { name: 'joke_creation' }, async () => { const completion = await openai . chat . completions . create ({ messages: [{ role: 'user' , content: 'Tell me a joke about TypeScript' }], model: 'gpt-4o-mini' , temperature: 0.7 }); return completion . choices [ 0 ]. message . content ; } ); }
Create a workflow combining tasks
In this example, we create a workflow pirate_joke_workflow that combines the createJoke task with a translateJoke task.
async function jokeWorkflow () { return await keywordsAi . withWorkflow ( { name: 'pirate_joke_workflow' }, async () => { const joke = await createJoke (); const pirateJoke = await translateJoke ( joke ); return pirateJoke ; } ); } The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows.
Install the SDK
Install the package using your preferred package manager:
pip install openai-agents pip install keywordsai-tracing pip install "keywordsai-tracing[openai-agents]"
Set up Keywords AI trace processor
Get your API key from the API Keys page in Settings, then configure it in your environment.
import os from agents.tracing import set_trace_processors from keywordsai_tracing.integrations.openai_agents_integration import KeywordsAITraceProcessor set_trace_processors( [ KeywordsAITraceProcessor( api_key = os.getenv( "KEYWORDSAI_API_KEY" ), endpoint = "https://api.keywordsai.co/api/openai/v1/traces/ingest" ), ] )
Call the agent
Now you can call the agent and it will be traced by Keywords AI automatically.
import asyncio import os from agents import Agent, Runner from agents.tracing import set_trace_processors from keywordsai_tracing.integrations.openai_agents_integration import KeywordsAITraceProcessor set_trace_processors( [ KeywordsAITraceProcessor( api_key = os.getenv( "KEYWORDSAI_API_KEY" ), endpoint = "https://api.keywordsai.co/api/openai/v1/traces/ingest" ), ] ) async def main (): agent = Agent( name = "Assistant" , instructions = "You only respond in haikus." , ) result = await Runner.run(agent, "Tell me about recursion in programming." ) print (result.final_output) # Function calls itself, # Looping in smaller pieces, # Endless by design. if __name__ == "__main__" : asyncio.run(main()) Understanding Workflows and Tasks A workflow is a sequence of tasks that you want to monitor. Each task represents a single unit of work within that workflow.
Example Below is an example that demonstrates how to use tasks and workflows. We’ll create a workflow that:
Generates a joke
Translates it to pirate speak
Adds a signature
Each operation is implemented as a separate task that can be used independently or as part of the workflow.
@task ( name = "joke_creation" ) def create_joke (): completion = client.chat.completions.create( model = "gpt-3.5-turbo" , messages = [{ "role" : "user" , "content" : "Tell me a joke about opentelemetry" }], temperature = 0.5 , max_tokens = 100 , frequency_penalty = 0.5 , presence_penalty = 0.5 , stop = [ " \n " ], logprobs = True , ) return completion.choices[ 0 ].message.content @task ( name = "signature_generation" ) def generate_signature ( joke : str ): completion = client.chat.completions.create( model = "gpt-3.5-turbo" , messages = [ { "role" : "user" , "content" : "add a signature to the joke: \n\n " + joke} ], ) return completion.choices[ 0 ].message.content @task ( name = "pirate_joke_translation" ) def translate_joke_to_pirate ( joke : str ): completion = client.chat.completions.create( model = "gpt-3.5-turbo" , messages = [ { "role" : "user" , "content" : "translate the joke to pirate language: \n\n " + joke, } ], ) return completion.choices[ 0 ].message.content Chain these tasks together in a workflow:
@workflow ( name = "joke_workflow" ) def joke_workflow (): joke = create_joke() pirate_joke = translate_joke_to_pirate(joke) signature = generate_signature(pirate_joke) return signature You can then see the trace in Traces .
Example adapted from Traceloop
Decorating Classes (Python only) While the examples above shows how to decorate functions, you can also decorate classes. In this case, you will also need to provide the name of the method that runs the workflow and task.
from openai import OpenAI from keywordsai_tracing import KeywordsAITelemetry from keywordsai_tracing.decorators import workflow, task k_tl = KeywordsAITelemetry() client = OpenAI() @workflow ( name = "joke_agent" , method_name = "run" ) class JokeAgent : @task ( name = "joke_creation" ) def create_joke ( self ): completion = client.chat.completions.create( model = "gpt-3.5-turbo" , messages = [{ "role" : "user" , "content" : "Tell me a joke about opentelemetry" }], ) joke = completion.choices[ 0 ].message.content return joke def run ( self ): return self .create_joke() 
