LiteLLM

Coralogix's AI Observability integrations are designed to provide deep insight into applications leveraging large language models. Through a dedicated integration with the LiteLLM SDK, Coralogix delivers a unified view of calls across various LLM providers, enabling teams to track performance, costs, and errors in a single place. This helps teams standardize monitoring, compare model performance, and optimize the entire system for efficiency and accuracy.

Overview

This library offers customized OpenTelemetry instrumentation for LiteLLM, optimized to support large language model (LLM) application development with streamlined integration, detailed production tracing, and effective debugging capabilities.

Requirements

• Python 3.10–3.13.
• Coralogix API keys.

Installation

Run the following command.

pip install "llm-tracekit-litellm"

Authentication

Authentication data is passed during OTel Span Exporter definition:

1. Choose the ingress.:443 endpoint that corresponds to your Coralogix domain using the domain selector at the top of the page.
2. Use your customized API key in the authorization request header.
3. Provide the application and subsystem names.

from llm_tracekit.litellm import setup_export_to_coralogix

setup_export_to_coralogix(
    coralogix_token=<your_coralogix_token>,
    coralogix_endpoint="ingress.:443",
    service_name="ai-service",
    application_name="ai-application",
    subsystem_name="ai-subsystem",
    capture_content=True,
)

Usage

This section describes how to set up instrumentation for LiteLLM.

Set up tracing

Automatic

Use the setup_export_to_coralogix function to set up tracing and export traces to Coralogix. See the code snippet in the Authentication section.

Manual

Alternatively, you can set up tracing manually.

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

tracer_provider = TracerProvider(
    resource=Resource.create({SERVICE_NAME: "ai-service"}),
)
exporter = OTLPSpanExporter()
span_processor = SimpleSpanProcessor(exporter)
tracer_provider.add_span_processor(span_processor)
trace.set_tracer_provider(tracer_provider)

Instrument

To instrument all clients, call the instrument method.

from llm_tracekit.litellm import LiteLLMInstrumentor

LiteLLMInstrumentor().instrument()

Uninstrument

To uninstrument clients, call the uninstrument method.

LiteLLMInstrumentor().uninstrument()

Full example

import litellm
from llm_tracekit.litellm import LiteLLMInstrumentor, setup_export_to_coralogix

# Optional: Configure sending spans to Coralogix
# Reads Coralogix connection details from the following environment variables:
# - CX_TOKEN
# - CX_ENDPOINT
setup_export_to_coralogix(
    service_name="ai-service",
    application_name="ai-application",
    subsystem_name="ai-subsystem",
    capture_content=True,
)

# Activate instrumentation
LiteLLMInstrumentor().instrument()

# LiteLLM usage example
response = litellm.completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Write a short poem on open telemetry."}],
    user="[email protected]",
)

Enable message content capture

By default, message content — prompt contents, completions, function arguments, and return values — is not captured. To capture message content as span attributes:

• Pass capture_content=True when calling setup_export_to_coralogix.
• Set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to true.

Most Coralogix AI evaluations require message contents to function properly, so enabling message capture is strongly recommended.

Semantic conventions

Next steps

Once your integration is set up, explore the AI Center Overview to monitor performance, costs, quality issues, and security across all your AI applications — and to set up Guardrails for real-time policy enforcement.