Build Custom AI Agent with LangChain & Gemini (Self-Hosted)

Build Custom AI Agent with LangChain & Gemini enables users to create a personalized AI chatbot that responds to chat messages with tailored interactions. This self-hosted solution integrates seamlessly with LangChain and Google Gemini, allowing for dynamic conversation management and memory storage. Users can customize the agent's personality and conversation structure, enhancing user engagement and satisfaction. Ideal for those seeking to automate interactions while maintaining a unique conversational style.

7/4/2025
9 nodes
Medium
7m5zpgl3owuorkplmanualmediumlangchainsticky note
Categories:
Manual TriggeredMedium Workflow
Integrations:
LangChainSticky Note

Target Audience

This workflow is ideal for:
- Developers looking to integrate AI chat capabilities into their applications.
- Businesses that want to automate customer interactions and improve user engagement through personalized chat experiences.
- Researchers exploring AI language models and their applications in real-time communication.
- Hobbyists interested in building custom AI agents for personal projects or experimentation.

Problem Solved

This workflow addresses the challenge of creating an interactive AI chat agent that can respond to user messages in a personalized manner. It leverages the power of the Google Gemini model to deliver coherent and contextually relevant responses, enhancing user experience and engagement. By integrating memory management, it ensures that conversations are contextually aware, allowing for more meaningful interactions.

Workflow Steps

  • Triggering the Chat: The workflow begins with the When chat message received node, which activates upon receiving a chat message.
    2. Storing Conversation History: The Store conversation history node captures and maintains previous interactions, ensuring the AI can reference past messages for context.
    3. Processing with Google Gemini: The Google Gemini Chat Model node utilizes the Google Gemini model to generate responses based on the input message and conversation history.
    4. Constructing the Prompt: The Construct & Execute LLM Prompt node formats the input and context into a structured prompt that guides the AI in generating appropriate responses.
    5. Outputting the Response: Finally, the workflow returns the AI's response back to the user, completing the interaction.

    • Customization Guide

    To customize this workflow:
    - Adjust AI Parameters: Modify the temperature and safetySettings in the Google Gemini Chat Model node to change the response style and safety measures.
    - Edit the Prompt Template: Update the Construct & Execute LLM Prompt node's code to alter the AI's persona, tone, or response style by changing the template string.
    - Manage Memory Settings: Configure the Store conversation history node to adjust how much past conversation history is retained, affecting the context available for responses.
    - Change the Chat Interface: Modify settings in the When chat message received node to customize the chat UI elements, making it more suitable for your application's design.