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Generative AI > LangChain > LangChain with Anthropic Claude - Building Claude-Powered Chains

LangChain with Anthropic Claude - Building Claude-Powered Chains

Author: Venkata Sudhakar

ShopMax India can use Anthropic Claude as the LLM backend in LangChain chains, taking advantage of Claude's strong instruction-following and long context capabilities for tasks like product review analysis, policy Q&A, and content generation. LangChain's ChatAnthropic integration lets you swap Claude in wherever ChatOpenAI is used with minimal code changes.

The langchain-anthropic package provides ChatAnthropic, which implements the same LangChain chat model interface as ChatOpenAI. You can use it with LCEL pipe syntax, prompt templates, output parsers, and all other LangChain components. Claude-specific features like extended thinking and prompt caching are also accessible through model_kwargs. Always set your ANTHROPIC_API_KEY environment variable before using ChatAnthropic.

The example below shows three LangChain chains using Claude: a product review sentiment chain, a return policy Q&A chain, and a product description generator for ShopMax India.

import os
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

llm = ChatAnthropic(model="claude-haiku-4-5", temperature=0)
parser = StrOutputParser()

sentiment_chain = (
    ChatPromptTemplate.from_template(
        "Classify this ShopMax India product review as Positive, Negative, or Neutral. Review: {review}"
    ) | llm | parser
)

policy_chain = (
    ChatPromptTemplate.from_template(
        "ShopMax India policy: 30-day returns on all electronics. Answer this question: {question}"
    ) | llm | parser
)

desc_chain = (
    ChatPromptTemplate.from_template(
        "Write a 2-sentence product description for ShopMax India: {product}"
    ) | llm | parser
)

review = "Battery life on the Sony headphones is amazing, lasted 28 hours on a single charge!"
print("Sentiment:", sentiment_chain.invoke({"review": review}))

question = "Can I return a laptop after 25 days if the screen is defective?"
print("Policy:", policy_chain.invoke({"question": question}))

product = "Bose QuietComfort 45 Wireless Headphones in Black"
print("Description:", desc_chain.invoke({"product": product}))

It gives the following output,

Sentiment: Positive
Policy: Yes, since 25 days is within the 30-day return window and the screen defect qualifies as a product issue, you can return the laptop to ShopMax India for a replacement or refund.
Description: Bose QuietComfort 45 delivers premium active noise cancellation with up to 24 hours of battery life, perfect for long commutes in Mumbai or Bangalore. Available on ShopMax India with free delivery and 30-day returns.

In production, use claude-haiku-4-5 for high-volume, low-latency tasks like sentiment analysis, and claude-sonnet-4-6 for complex reasoning and longer documents. Enable prompt caching by passing cache_control metadata when the same system prompt is reused across many requests - this can cut costs significantly for ShopMax policy Q&A. Monitor token usage through the response metadata to track costs per request type.

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