AI Chatbot Orchestration with Langchain and Gemini
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Automate sophisticated AI chatbot interactions by integrating Google's Gemini with Langchain's powerful agent framework. This workflow enables intelligent responses and persistent conversational memory, all while tracking interactions with Langfuse.
About This Workflow
This n8n workflow empowers you to build advanced AI-powered chatbots that leverage the cutting-edge capabilities of Google's Gemini models. It seamlessly integrates Langchain, a popular framework for developing applications powered by language models, to create intelligent agents. The workflow begins by triggering on incoming chat messages, feeding the conversation to an AI Agent. This agent utilizes Gemini's advanced reasoning and generation abilities, enhanced by a sliding window memory to maintain context over longer conversations. Crucially, it incorporates Langfuse for robust LLM observability, providing insights into model performance and conversation flow. This allows for sophisticated conversational AI with built-in memory and detailed analytics.
Key Features
- Real-time Chat Trigger: Instantly respond to incoming chat messages.
- Google Gemini-2.5 Integration: Harness the power of Google's advanced AI language models.
- Conversational Memory: Maintain context and coherence with a buffer window memory.
- Langchain Agent Framework: Build sophisticated and adaptable AI agents.
- Langfuse LLM Observability: Track and analyze AI interactions for performance and debugging.
How To Use
- Configure the Chat Trigger: Set up the 'When chat message received' node to listen for incoming messages from your desired chat platform (e.g., via webhook).
- Connect Gemini Model: In the 'gemini-2.5' node, configure your Google API credentials and select the desired Gemini model (e.g.,
models/gemini-2.5-flash-preview-05-20). - Set Up Conversation Memory: Configure the 'mem' node to define your conversational memory buffer window size (e.g., 100 messages).
- Initialize Langfuse Integration: Use the 'Langfuse LLM' code node to initialize the Langfuse callback handler. Ensure you have your Langfuse project details configured in n8n credentials. This node connects the Gemini model with Langfuse.
- Build Your AI Agent: Connect the output of the 'Langfuse LLM' node and the 'mem' node to the 'AI Agent' node. Configure the agent's prompt and available tools within this node.
Apps Used
Workflow JSON
{
"id": "f8d572ce-9469-461a-860e-525fddf96920",
"name": "AI Chatbot Orchestration with Langchain and Gemini",
"nodes": 8,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: f8d572ce-9469...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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