OpenLit Trace Export

Export OpenLit traces to the Judgment platform.

OpenLit integration sends traces from your OpenLit-instrumented applications to Judgment. If you’re already using OpenLit for observability, this integration forwards those traces to Judgment without requiring additional instrumentation.

Quickstart

uv add openlit judgeval openai

pip install openlit judgeval openai

from judgeval import Tracer
from judgeval.integrations.openlit import Openlit

Tracer.init(project_name="openlit_project")
Openlit.initialize()

from openai import OpenAI
from judgeval import Tracer  
from judgeval.integrations.openlit import Openlit  

Tracer.init(project_name="openlit_project")  
Openlit.initialize()  

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[{"role": "user", "content": "Hello, world!"}]
)

print(response.choices[0].message.content)

Example: Multi-Workflow Application

from judgeval import Tracer
from judgeval.integrations.openlit import Openlit
from openai import OpenAI

Tracer.init(project_name="openlit_project")
Openlit.initialize()

client = OpenAI()

def analyze_text(text: str) -> str:
    response = client.chat.completions.create(
        model="gpt-5.2",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
            {"role": "user", "content": f"Analyze: {text}"}
        ]
    )
    return response.choices[0].message.content

def summarize_text(text: str) -> str:
    response = client.chat.completions.create(
        model="gpt-5.2",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
            {"role": "user", "content": f"Summarize: {text}"}
        ]
    )
    return response.choices[0].message.content

def generate_content(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-5.2",
        messages=[
            {"role": "system", "content": "You are a creative AI assistant."},
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content

@Tracer.observe(span_type="function")  
def main():
    text = "The future of artificial intelligence is bright and full of possibilities."

    analysis = analyze_text(text)
    summary = summarize_text(text)
    story = generate_content(f"Create a story about: {text}")

    print(f"Analysis: {analysis}")
    print(f"Summary: {summary}")
    print(f"Story: {story}")

if __name__ == "__main__":
    main()