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LangChain

LangChain is a framework for developing applications powered by large language models (LLMs). By instrumenting LangChain with Agenta, you can monitor and debug your applications more effectively, gaining insights into each step of your workflows.

This guide shows you how to instrument LangChain applications using Agenta's observability features.

Installation​

Install the required packages:

pip install -U agenta openai opentelemetry-instrumentation-langchain langchain langchain_community

Configure Environment Variables​

import os

os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY"
os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai"

Code Example​

import agenta as ag
from opentelemetry.instrumentation.langchain import LangchainInstrumentor
from langchain.schema import SystemMessage, HumanMessage
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_community.chat_models import ChatOpenAI
from langchain.chains import LLMChain, SequentialChain, TransformChain

ag.init()
LangchainInstrumentor().instrument()


def langchain_app():
    # Initialize the chat model
    chat = ChatOpenAI(temperature=0)

    # Define a transformation chain to create the prompt
    transform = TransformChain(
        input_variables=["subject"],
        output_variables=["prompt"],
        transform=lambda inputs: {"prompt": f"Tell me a joke about {inputs['subject']}."},
    )

    # Define the first LLM chain to generate a joke
    first_prompt_messages = [
        SystemMessage(content="You are a funny sarcastic nerd."),
        HumanMessage(content="{prompt}"),
    ]
    first_prompt_template = ChatPromptTemplate.from_messages(first_prompt_messages)
    first_chain = LLMChain(llm=chat, prompt=first_prompt_template, output_key="joke")

    # Define the second LLM chain to translate the joke
    second_prompt_messages = [
        SystemMessage(content="You are an Elf."),
        HumanMessagePromptTemplate.from_template(
            "Translate the joke below into Sindarin language:\n{joke}"
        ),
    ]
    second_prompt_template = ChatPromptTemplate.from_messages(second_prompt_messages)
    second_chain = LLMChain(llm=chat, prompt=second_prompt_template)

    # Chain everything together in a sequential workflow
    workflow = SequentialChain(
        chains=[transform, first_chain, second_chain],
        input_variables=["subject"],
    )

    # Execute the workflow and print the result
    result = workflow({"subject": "OpenTelemetry"})
    print(result)

# Run the LangChain application
langchain_app()

Explanation​

  • Initialize Agenta: ag.init() sets up the Agenta SDK.
  • Instrument LangChain: LangchainInstrumentor().instrument() instruments LangChain for tracing. This must be called before running your application to ensure all components are traced.

Using Workflows​

You can optionally use the @ag.instrument(spankind="WORKFLOW") decorator to create a parent span for your workflow. This is optional, but it's a good practice to instrument the main function of your application.