splunk-otel-instrumentation-langchain 0.1.12

OpenTelemetry Official Langchain instrumentation

This package provides OpenTelemetry instrumentation for LangChain LLM/chat workflows. It leverages Splunk distribution of opentelemetry-util-genai for producing telemetry in semantic convention. Core concepts, high-level usage and configuration

Status: Alpha (APIs and produced telemetry are subject to change).

Installation

Install from source:

pip install -e splunk-otel-instrumentation-langchain

This will pull in required OpenTelemetry core + opentelemetry-util-genai.

Quick Start

Manual Instrumentation (development/debugging)

from opentelemetry.instrumentation.langchain import LangChainInstrumentor
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage

# manual instrumentation, easy to debug in your IDE
LangChainInstrumentor().instrument()

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.0)
messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is the capital of France?"),
]
response = llm.invoke(messages)
print(response.content)

Zero-code instrumentation

in zero-code instrumentation mode, ensure you install opentelemetry-distribution and run you app with the OpenTelemetry LangChain Instrumentor enabled:

.. code:: bash

opentelemetry-instrument python your_langchain_app.py

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.0)
messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is the capital of France?"),
]
response = llm.invoke(messages)
print(response.content)

Embedding Support

The instrumentation wraps embed_documents() and embed_query() methods for embedding classes (e.g., OpenAIEmbeddings, AzureOpenAIEmbeddings).

Token Counting: For token usage metrics, the instrumentation uses tiktoken to count input tokens client-side. This is necessary because:

• LangChain’s embedding methods only return embedding vectors (list[list[float]])

• The API’s usage response is discarded internally by LangChain

• LangChain has no embedding callbacks (unlike LLM callbacks)

tiktoken is already a required dependency of langchain-openai, so no additional installation is needed when using OpenAI embeddings. If tiktoken is unavailable (e.g., for non-OpenAI providers), token metrics are gracefully omitted.

Interrupt/Resume Support

The instrumentation automatically detects LangGraph interrupt/resume patterns:

• Error classification — GraphInterrupt, NodeInterrupt, and Interrupt exceptions are classified as interrupts (span status left as UNSET instead of ERROR). CancelledError and TaskCancelledError are classified as cancellations.

• Conversation ID — thread_id from LangGraph checkpoint metadata is extracted and set as gen_ai.conversation.id on root workflow/agent spans.

See the examples/multi_agent_travel_planner demo for interrupt/resume in action.

Testing

Run the package tests (from repository root or this directory):

pytest -k langchain instrumentation-genai/opentelemetry-instrumentation-langchain-alpha/tests

(Recorded cassettes or proper API keys may be required for full integration tests.)

Contributing

Issues / PRs welcome in the main otel-splunk-python-contrib repository. This module is alpha: feedback on attribute coverage, performance, and LangChain surface expansion is especially helpful.

License

Apache 2.0