LangChain

Coralogix's AI Observability integrations make it easy to monitor any LangChain-powered application. With a dedicated LangChain integration, Coralogix consolidates spans emitted by OpenAI, AWS Bedrock, and future LangChain chat providers so teams can understand performance, drift, and tool usage without stitching logs across services.

How LangChain works

This library ships production-ready OpenTelemetry instrumentation for LangChain. It automatically hooks into LangChain's callback manager, traces chat and tool-call workflows, and records usage metrics. The integration accelerates debugging, highlights token consumption, and standardizes observability across multi provider LangChain deployments.

Instrumentation currently supports OpenAI and AWS Bedrock providers (including tool binding) across synchronous, streaming, and multi turn LangChain runs.

What you need

• Python version 3.10 and above.
• Coralogix API keys.
• LangChain 1.0.0 or newer along with the provider-specific SDKs (for example langchain-openai or langchain-aws).

Ensure each underlying provider SDK (OpenAI, AWS Bedrock, etc.) is configured with valid credentials before enabling instrumentation.

Installation

Run the following command.

pip install llm-tracekit[langchain]

Authentication

Exporting spans to Coralogix requires configuring OTLP credentials before enabling instrumentation. Use setup_export_to_coralogix or the corresponding environment variables to supply authentication details.

Using setup_export_to_coralogix

from llm_tracekit import setup_export_to_coralogix

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

Using environment variables

If arguments are not passed to setup_export_to_coralogix, the helper reads the following environment variables:

• CX_TOKEN: Your Coralogix API key.
• CX_ENDPOINT: Select the ingress.:443 endpoint that corresponds to your Coralogix domain using the domain selector at the top of the page.
• CX_APPLICATION_NAME: Your application's name.
• CX_SUBSYSTEM_NAME: Your subsystem's name.

Usage

Instrument for LangChain.

Set up tracing

Instrument

LangChain instrumentation is installed globally by wrapping BaseCallbackManager. Create an instance of LangChainInstrumentor and call instrument before instantiating your chains.

From llm_tracekit import LangChainInstrumentor

instrumentor = LangChainInstrumentor()
instrumentor.instrument()

Uninstrument

To remove instrumentation, call the uninstrument method.

instrumentor.uninstrument()

Full example

from langchain_openai import ChatOpenAI

from llm_tracekit import setup_export_to_coralogix
from llm_tracekit import LangChainInstrumentor

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

instrumentor = LangChainInstrumentor()
instrumentor.instrument()

llm = ChatOpenAI(model="gpt-4o-mini")

response = llm.invoke("Draft a release note for LangChain instrumentation")

print(response)

Enable message content capture

By default, message content - such as prompts, completions, tool call arguments, and tool responses - is not captured.

To capture message content as span attributes, set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to true.

Many Coralogix AI evaluations rely on message content; enabling capture is a best practice.

Key differences from OpenTelemetry

Prompts, tool invocations, and model responses are stored as span attributes instead of log events, preserving a single correlated timeline for each LangChain run.

Semantic conventions