Create a Smart Multi-Tool AI Agent with Streamlit for Real-Time Interactive Assistance

Building a Feature-Rich AI Assistant with Streamlit and LangChain

This tutorial demonstrates how to develop an interactive Streamlit application that integrates LangChain, Google Gemini API, and various advanced tools to form a powerful AI assistant. The chat-based interface enables users to perform web searches, retrieve Wikipedia content, execute mathematical calculations, remember key information, and maintain conversation history — all in real time.

Setting Up Environment and Dependencies

We start by installing essential Python and Node.js packages including Streamlit for UI, LangChain for agent logic, Wikipedia and DuckDuckGo for search capabilities, and tunneling tools like ngrok or localtunnel. These components lay the foundation for the AI assistant's multi-tool functionality.

Configuring API Keys and Authentication

The Google Gemini API key and ngrok authentication tokens are configured to allow secure access to the Gemini model and public exposure of the local Streamlit app. This setup ensures the AI agent can interact with external services and users effectively.

Developing Advanced Agent Tools

The InnovativeAgentTools class equips the AI with specific capabilities:

• Calculator Tool: Safely evaluates mathematical expressions.
• Memory Tools: Enables saving and recalling information during conversations.
• DateTime Tool: Provides current date and time in various formats.

These tools empower the agent to perform context-aware reasoning and responses.

Constructing the Multi-Agent System

The MultiAgentSystem class integrates the Gemini Pro model with LangChain. It initializes all tools, maintains conversation memory, and employs a ReAct-style agent guided by a custom prompt. The chat method processes user inputs, utilizes tools as needed, and produces intelligent answers.

Designing the Streamlit Interface

The app features a polished and responsive UI with custom CSS styles. A sidebar allows users to enter API keys and view stored memory. The main chat interface supports real-time user-agent interactions with message history. Example query buttons facilitate easy exploration of functionalities like search, math, and memory.

Hosting and Sharing the Application

Helper functions configure ngrok authentication to expose the app publicly. Failover options like LocalTunnel and Serveo are provided for alternative hosting methods. The app can be seamlessly run locally or within Google Colab, with automatic environment detection.

Conclusion

This comprehensive approach delivers a robust AI assistant accessible via browser, combining multi-tool capabilities with a user-friendly Streamlit front-end. The modular design supports easy expansion and integration into sophisticated AI workflows.

Explore the full notebook for detailed implementation and code examples.