How To Build Agentic Applications With Streamlit And LangChain 🔧

Tutorials & Methods
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How To Build Agentic Applications with Streamlit and LangChain

Discover how to build powerful agentic applications by combining the simplicity of Streamlit with the advanced capabilities of LangChain. This method allows developers to create interactive, intelligent apps with minimal effort while leveraging cutting-edge language model tools.

:rocket: Introduction

Creating AI-driven applications no longer requires complex setups. By merging Streamlit for rapid front-end deployment and LangChain for advanced language model orchestration, developers can quickly design agentic systems that interact with users and external tools in real time.

:wrench: Core Setup

To start, install the required libraries:

pip install streamlit langchain openai

Streamlit manages the user interface, while LangChain integrates with LLMs and coordinates multiple components.

:puzzle_piece: Key Components

  1. Agents in LangChain
    LangChain provides different agent types, such as:

    • Zero-Shot ReAct Agent – uses reasoning + actions for queries.

    • Conversational Agent – maintains context across sessions.

    • Custom Tools – integrate APIs, databases, or logic directly.

  2. Streamlit UI
    With streamlit.chat_input and streamlit.chat_message, creating a chat-like experience is seamless.

  3. Memory Management
    LangChain supports short-term and long-term memory, ensuring conversations remain contextual.

:high_voltage: Implementation Example

import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, load_tools

# Setup
llm = ChatOpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")

# UI
st.title("Agentic App with Streamlit + LangChain")
user_input = st.chat_input("Ask me anything")

if user_input:
    with st.chat_message("user"):
        st.markdown(user_input)
    response = agent.run(user_input)
    with st.chat_message("assistant"):
        st.markdown(response)

This script combines Streamlit’s frontend simplicity with LangChain’s agent orchestration, producing a functional AI chatbot.

:chart_increasing: Advanced Enhancements

  • Custom APIs: Connect with services like Google Search API, Wolfram Alpha, or internal databases.

  • Chained Agents: Build multi-step workflows where one agent calls another.

  • Deployment: Use streamlit run app.py to launch instantly.

:globe_showing_europe_africa: Real-World Use Cases

  • Customer Support Bots with contextual memory.

  • Data Analysis Assistants that query structured datasets.

  • Knowledge Base Explorers that connect to document search.

:white_check_mark: Conclusion

By integrating Streamlit + LangChain, developers gain a scalable, customizable framework for crafting agentic applications that feel natural, intelligent, and highly interactive.

For more details, check the official docs:

Enjoy!

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