SUTRA, a family of large multilingual language models (LMLMs), excels in handling over 50 languages, making it ideal for applications requiring cultural and linguistic diversity. Agno, a lightweight and model-agnostic library, complements SUTRA by enabling the creation of intelligent agents with memory, reasoning, and tool integration.
This guide will walk you through setting up a multilingual AI agent, running examples, and leveraging tools like DuckDuckGo and Yahoo Finance for real-time data.
Prerequisites
Before diving in, ensure you have:
- A SUTRA API key from TWO AI's SUTRA API page
- Basic familiarity with Python and Jupyter notebooks
- Access to Google Colab
Setting Up the Environment
Step 1: Install Required Packages
!pip install openai agno duckduckgo-search yfinance
These packages include:
- openai: For interacting with SUTRA's API.
- agno: The Agno library for building AI agents.
- duckduckgo-search: For real-time web searches.
- yfinance: For accessing stock market data.
Step 2: Configure API Keys
import os
from google.colab import userdata
# Set the API keys from Colab secrets
os.environ["OPENAI_API_KEY"] = userdata.get("SUTRA_API_KEY")
os.environ["TAVILY_API_KEY"] = userdata.get("TAVILY_API_KEY")
🔐 Replace SUTRA_API_KEY with your actual key from TWO AI.
Using SUTRA with OpenAI Client
from openai import OpenAI
client = OpenAI(
base_url='https://api.two.ai/v2',
api_key=os.environ["SUTRA_API_KEY"]
)
response = client.chat.completions.create(
model="sutra-v2",
messages=[
{"role": "system", "content": "You are a helpful assistant that specializes in Indian languages and culture."},
{"role": "user", "content": "Tell me about the importance of the Ganga river in Indian culture."}
]
)
print(response.choices[0].message.content)
Building Multilingual AI Agents with Agno
Example 1: Cultural Q&A Agent
from agno.agent import Agent
from agno.models.openai.like import OpenAILike
sutra_agent = Agent(
model=OpenAILike(
id="sutra-v2",
api_key=os.getenv("SUTRA_API_KEY"),
base_url="https://api.two.ai/v2"
),
description="You are a helpful assistant specializing in Indian culture and history.",
markdown=True
)
sutra_agent.print_response("Tell me about the history of yoga in India.", stream=True)
Example 3: Storytelling Agent
story_agent = Agent(
model=OpenAILike(
id="sutra-v2",
api_key=os.getenv("SUTRA_API_KEY"),
base_url="https://api.two.ai/v2"
),
description="You are a creative storyteller specializing in Indian folklore.",
markdown=True
)
story_agent.print_response("Write a short story about a magical tree in an Indian village.", stream=True)
Example 2: Code Explanation Agent
sample_code = '''
def diwali_date(year):
return f'Diwali in {year} is likely in October or November.'
print(diwali_date(2025))
'''
code_agent = Agent(
model=OpenAILike(
id="sutra-v2",
api_key=os.getenv("SUTRA_API_KEY"),
base_url="https://api.two.ai/v2"
),
description="You are an expert in explaining Python code, especially related to Indian culture.",
markdown=True
)
code_agent.print_response(f"Explain this Python code:\n{sample_code}", stream=True)
Example 3: Stock Market Agent
from agno.tools.yfinance import YFinanceTools
sutra_agent = Agent(
model=OpenAILike(
id="sutra-v2",
api_key=os.getenv("SUTRA_API_KEY"),
base_url="https://api.two.ai/v2"
),
description="You are an expert in analyzing stock market data using YFinanceTools.",
markdown=True,
tools=[YFinanceTools()]
)
sutra_agent.print_response("How is TSLA stock doing right now in Hindi?", stream=True)
Example 4: Multilingual Agent with Reasoning
from agno.tools.duckduckgo import DuckDuckGoTools
sutra_agent_with_tools = Agent(
model=OpenAILike(
id="sutra-v2",
api_key=os.getenv("SUTRA_API_KEY"),
base_url="https://api.two.ai/v2"
),
description="You are a helpful assistant specializing in Indian languages, culture, and current events. Provide accurate and detailed responses in Hindi when requested, using DuckDuckGoTools for up-to-date information.",
tools=[DuckDuckGoTools()],
show_tool_calls=True,
markdown=True
)
query = '''
भारत के अंतरिक्ष कार्यक्रम में हाल के विकास क्या हैं? DuckDuckGoTools का उपयोग करके नवीनतम जानकारी प्राप्त करें और निम्नलिखित शामिल करें:
1. हाल की मिशन सफलताएँ (उदाहरण: SpaDeX, Gaganyaan)।
2. भविष्य की योजनाएँ (उदाहरण: चंद्रयान-4, भारतीय अंतरिक्ष स्टेशन)।
3. निजी क्षेत्र की भागीदारी।
हिंदी में विस्तृत और संरचित उत्तर प्रदान करें, प्रत्येक अनुभाग को स्पष्ट रूप से लेबल करें।
'''
sutra_agent_with_tools.print_response(query, stream=True)
Next Steps
- 🔍 Experiment with Prompts: Customize prompts and agent descriptions.
- 🧠 Combine Tasks: Build agents that merge tasks like translation + storytelling.
- 📘 Explore the API: Visit the Sutra API Docs for more options.
- 🌍 Share Your Work: Publish your creations and tag the community!
🌟 Share Your Work
Contribute your chatbot to the open-source community:
- ✨ Submit to sutra-cookbook GitHub repo
- Share your notebook with your team or Audience
💡 Tips & Tricks
- ✅ Multilingual Power: Use sutra-v2 to support 50+ languages
- 📚 Optimal Chunks: Stick with chunk_size = 1000 and chunk_overlap = 100
🌍Community First: Star the repo and share feedback
📘️ Conclusion
Combining RAG with SUTRA empowers you to build intelligent, multilingual, document-aware chatbots—perfect for education, community discussions, and global learning.
🔗 Resources & Community
- 🌐 Website: two.ai
- 💻 GitHub: sutra-cookbook
- 💬 Discord: Join the community
- 🤞 Twitter: @sutra_dev
- 💼 LinkedIn: TWO Platforms
