LangChain with SUTRA
This guide walks you through using SUTRA models (V2 or R0) within the LangChain ecosystem. With LangChain, you can build context-aware chains, agents, and tools that integrate SUTRA’s multilingual and reasoning capabilities.
📦 Step 1: Install Dependencies
# SUTRA models are OpenAI API compatible
!pip install -qU openai langchain langchain-openai langchain_community🔐 Step 2: Authenticate with Your API Key
from openai import OpenAI
from langchain_openai import ChatOpenAI
api_key = "YOUR_SUTRA_API_KEY"⚙️ Step 3: Initialize LangChain Client with SUTRA
You can use either sutra-v2 for multilingual chat or sutra-r0 for advanced reasoning.
chat = ChatOpenAI(
model="sutra-v2", # or "sutra-r0"
api_key=api_key,
base_url="https://api.two.ai/v2"
)💬 Step 4: Run a Basic Chat Prompt
from langchain.schema import HumanMessage, SystemMessage
messages = [
SystemMessage(content="You are a helpful AI that answers concisely."),
HumanMessage(content="What are the benefits of multilingual education?")
]
response = chat.invoke(messages)
print(response.content)🧠 Use SUTRA-R0 for Structured Reasoning
r0_chat = ChatOpenAI(
model="sutra-r0",
api_key=api_key,
base_url="https://api.two.ai/v2"
)
prompt = [
SystemMessage(content="You are a legal advisor."),
HumanMessage(content="If a contract states 'Party A is responsible unless Party B provides notice,' who is liable?")
]
response = r0_chat.invoke(prompt)
print(response.content)📎 Resources
Use LangChain + SUTRA to create production-ready AI flows with multilingual reasoning, chain logic, and smart prompting.