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AgentOps provides seamless integration with IBM Watsonx.ai Python SDK, allowing you to track and analyze all your Watsonx.ai model interactions automatically.

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Installation

pip install agentops ibm-watsonx-ai

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Setting Up API Keys

Before using IBM Watsonx.ai with AgentOps, you need to set up your API keys. You can obtain:
  • IBM_WATSONX_API_KEY: From your IBM Cloud account
  • IBM_WATSONX_URL: The URL for your Watsonx.ai instance, typically found in your IBM Cloud dashboard.
  • IBM_WATSONX_PROJECT_ID: The project ID for your Watsonx.ai project, which you can find in the Watsonx.ai console.
  • AGENTOPS_API_KEY: From your AgentOps Dashboard
Then to set them up, you can either export them as environment variables or set them in a .env file. 
export IBM_WATSONX_API_KEY="your_ibm_api_key_here"
export IBM_WATSONX_URL="your_ibm_url_here"
export IBM_WATSONX_PROJECT_ID="your_project_id_here"
export AGENTOPS_API_KEY="your_agentops_api_key_here"
Then load the environment variables in your Python code: 
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Set up environment variables with fallback values
os.environ["IBM_WATSONX_API_KEY"] = os.getenv("IBM_WATSONX_API_KEY")
os.environ["IBM_WATSONX_URL"] = os.getenv("IBM_WATSONX_URL")
os.environ["IBM_WATSONX_PROJECT_ID"] = os.getenv("IBM_WATSONX_PROJECT_ID")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")

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Usage

Initialize AgentOps at the beginning of your application to automatically track all IBM Watsonx.ai API calls: 
import agentops
from ibm_watsonx_ai import Credentials
from ibm_watsonx_ai.foundation_models import ModelInference

# Initialize AgentOps
agentops.init(api_key="")

# Initialize credentials
credentials = Credentials(
    url=os.getenv("IBM_WATSONX_URL"),
    api_key=os.getenv("IBM_WATSONX_API_KEY"),
)

# Project ID
project_id = os.getenv("IBM_WATSONX_PROJECT_ID")

# Create a model instance
model = ModelInference(
    model_id="meta-llama/llama-3-3-70b-instruct",
    credentials=credentials,
    project_id=project_id
)

# Make a completion request
response = model.generate_text("What is artificial intelligence?")
print(f"Generated Text:\n{response}")

# Don't forget to close connection when done
model.close_persistent_connection()

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Examples

Watsonx Text Chat

Basic text generation and chat

Watsonx Streaming

Demonstrates streaming responses with Watsonx.ai.

Watsonx Tokenization

Example of text tokenization with Watsonx.ai models.

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Additional Resources