Agent Demo

1. Basic Agent

## Basic Agent

from agno.agent import Agent, RunResponse

from agno.models.ollama import Ollama

agent = Agent(

    model=Ollama(id="llama3.2"),

    markdown=True

)

# Print the response in the terminal

agent.print_response("What is Ministry of Corporate Affairs in India?

what it does?")

Response:

2. Web Search Agent

## Agents with Tools

# Pip install phidata, duckduckgo-search, arxiv

from agno.agent import Agent

from agno.models.ollama import Ollama

from agno.tools.duckduckgo import DuckDuckGoTools

from agno.tools.arxiv import ArxivTools

agent = Agent(

    model=Ollama(id="llama3.2"),

    # tools=[DuckDuckGoTools()],

    tools=[DuckDuckGoTools(), ArxivTools()],

    show_tool_calls=True,

    markdown=True

)

# Print the response in the terminal

# agent.print_response("What do you think of the Latest news on

US deporting migrants.")

#agent.print_response("Give me some latest news

in Indian Politics.")

# agent.print_response("What is 23+89.")

agent.print_response("Search arxiv for 'Reinforcement Learning")

Response:

3. Chat_Agent :

#pip install pydantic requests streamlit ollama

import streamlit as st

from pydantic import BaseModel

import ollama

# 🎭 Define AI Agent

class AIAgent(BaseModel):

    name: str = "OllamaBot"

    version: str = "1.0"

    description: str = "A chatbot powered by Ollama LLM."

agent = AIAgent()

# 🛠️ Streamlit UI

st.title("🤖 iMMSS LLM for Legal Assistance")

st.write("Ask anything! (Type 'exit' to stop)")

# 🎤 Initialize chat history

if "messages" not in st.session_state:

    st.session_state.messages = []

# 🚀 Display chat history

for msg in st.session_state.messages:

    st.write(msg)

# 🎤 User Input

user_query = st.text_input("You:", "")

# 🧠 Function to get AI response

def get_ai_response(question: str):

    response = ollama.chat(model="llama3.2", messages=[{"role": "user", "content": question}])

    return response["message"]["content"]

# 🚀 Process User Query

if user_query:

    if user_query.lower() == "exit":

        st.write("👋 Chatbot: Goodbye! Closing chat...")

        st.stop()

    # Get AI response

    ai_answer = get_ai_response(user_query)

    # Append user and bot messages to session state

    st.session_state.messages.append(f"**You:** {user_query}")

    st.session_state.messages.append(f"**{agent.name}:** {ai_answer}")

    # Display AI response

    st.write(f"**{agent.name}:** {ai_answer}")

Response: