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, Anthropic, AWS Bedrock, and other LangChain chat providers so teams can understand performance, drift, and tool usage without stitching logs across services.

Overview

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

Supported providers

The following providers are supported with full prompt/completion attributes:

Provider	Chat model class	System value
OpenAI	ChatOpenAI	openai
Anthropic	ChatAnthropic	anthropic
AWS Bedrock	ChatBedrock, ChatBedrockConverse, BedrockChat	aws.bedrock

Other chat model classes are still instrumented with system value langchain and a span is always created (model name uses metadata or provider class name when not in standard keys).

Requirements

• Python 3.10–3.13.
• Coralogix API keys.

Installation

Run the following command.

pip install "llm-tracekit-langchain"

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.langchain 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 LangChain.

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.langchain import LangChainInstrumentor

LangChainInstrumentor().instrument()

Uninstrument

To uninstrument clients, call the uninstrument method.

LangChainInstrumentor().uninstrument()

Full example

from llm_tracekit.langchain import LangChainInstrumentor, setup_export_to_coralogix
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# 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
LangChainInstrumentor().instrument()

# LangChain usage example
llm = ChatOpenAI(model="gpt-4o-mini")
messages = [HumanMessage(content="Write a short poem on open telemetry.")]
response = llm.invoke(messages)

# Pass user via config metadata
response = llm.invoke(
    messages,
    config={"metadata": {"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

LangChain-specific attributes

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.