OpenInference LangChain Instrumentation

Python auto-instrumentation library for LangChain.

These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as Arize Phoenix or Arize AX.

Compatibility

This instrumentation works with:

LangChain 1.x (langchain>=1.0.0): Modern agent framework built on LangGraph
LangChain Classic (langchain-classic>=1.0.0): Legacy chains and tools (formerly langchain 0.x)
All LangChain partner packages (langchain-openai, langchain-anthropic, langchain-google-vertexai, etc.)

The instrumentation hooks into langchain-core, which is the shared foundation used by all LangChain packages.

Installation

For LangChain 1.x (Recommended for New Projects)

pip install openinference-instrumentation-langchain langchain langchain-openai

For LangChain Classic (Legacy Applications)

pip install openinference-instrumentation-langchain langchain-classic langchain-openai

For Both (Migration Scenarios)

pip install openinference-instrumentation-langchain langchain langchain-classic langchain-openai

Quickstart

Example with LangChain 1.x (New Agent Framework)

Install packages needed for this demonstration.

pip install openinference-instrumentation-langchain langchain langchain-openai arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

Start the Phoenix app in the background as a collector. By default, it listens on http://localhost:6006. You can visit the app via a browser at the same address.

The Phoenix app does not send data over the internet. It only operates locally on your machine.

python -m phoenix.server.main serve

The following Python code sets up the LangChainInstrumentor to trace langchain and send the traces to Phoenix at the endpoint shown below.

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from openinference.instrumentation.langchain import LangChainInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
trace_api.set_tracer_provider(tracer_provider)
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

LangChainInstrumentor().instrument()

To demonstrate tracing, we'll create a simple agent. First, configure your OpenAI credentials.

import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"

Now we can create an agent and run it.

def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"The weather in {city} is sunny!"

model = ChatOpenAI(model="gpt-4")
agent = create_agent(model, tools=[get_weather])
result = agent.invoke({"messages": [{"role": "user", "content": "What's the weather in Paris?"}]})
print(result)

Example with LangChain Classic (Legacy Chains)

For legacy applications using LangChain Classic:

from langchain_classic.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

# ... (same instrumentation setup as above)

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
llm = LLMChain(llm=OpenAI(), prompt=prompt, metadata={"category": "jokes"})
completion = llm.predict(adjective="funny", metadata={"variant": "funny"})
print(completion)