This article provides a complete, practical walkthrough of the Stickynote Automation Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Simple setup in 5-15 minutes. One‑time purchase: €9.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title: Create a Configurable LLM Chatbot in n8n Using OpenRouter
   
   Meta Description: Discover how to build a flexible chatbot workflow in n8n that leverages OpenRouter to dynamically access a variety of LLM models like DeepSeek, GPT, and Gemini. Learn how to configure AI agents, memory, and prompts based on real-time inputs.
   
   Keywords: n8n, OpenRouter, LLM, chatbot, AI agent, workflow automation, Langchain, GPT, Gemini, DeepSeek, API integration, dynamic model selection, chat memory, no-code automation
   
   Third-Party APIs Used:
   
   - OpenRouter API (via OpenAI-compatible endpoint)
   - LangChain (n8n Langchain nodes used for memory, agent, and model components)
   
   Article:
   Build a Configurable LLM Chatbot With OpenRouter and n8n
   
   As the demand for personalized AI assistants and intelligent chatbots grows, developers are exploring new, low-code solutions to build robust natural language processing systems with minimal overhead. n8n, a powerful workflow automation platform, combined with OpenRouter and Langchain, opens the door to a flexible, customizable chatbot setup using a variety of large language models (LLMs).
   
   In this article, we’ll explore an n8n workflow titled “Use any LLM-Model via OpenRouter.” This workflow is designed to let you create a conversational AI that can dynamically select different LLMs—including models from OpenAI, Google, DeepSeek, Mistral, and more—based on request inputs.
   
   Overview: A Flexible, LLM-Powered Chat Workflow
   
   The core architecture of the workflow combines three key components:
   
   - A trigger that activates on incoming chat messages
   - A set of configuration nodes to define which model and inputs to use
   - Langchain-based AI Agent, memory, and language model nodes to handle context, conversation history, and generation
   
   Let’s break down the components of this workflow and see how they function collaboratively.
   
   1. Chat Trigger Node: Listening for Incoming Messages
   
   At the root of the workflow is a “When chat message received” trigger node. This webhook listens for new chat messages, likely from a UI or API integration, and initiates the chain reaction for LLM-based interactions. The trigger extracts useful inputs like:
   
   - chatInput: The message from the user
   - sessionId: A unique ID to track session-specific conversation memory
   
   2. Settings Node: Dynamic Model and Prompt Assignment
   
   The “Settings” node is an essential part of the workflow. It acts as a pre-processor, assigning values to dynamic variables:
   
   - model: The identifier for the LLM to be used (e.g., deepseek/deepseek-r1-distill-llama-8b)
   - prompt: The user’s chat input
   - sessionId: A reference to manage memory across conversational sessions
   
   This level of configurability makes it easy to switch between different AI models without changing the underlying architecture.
   
   3. Sticky Notes for Developer Usability
   
   To improve workflow clarity, sticky notes are added to provide documentation directly within the n8n canvas. For example:
   
   - One note reminds users to specify the model in the Settings node
   - Another offers several LLM examples from OpenRouter such as:
     - openai/o3-mini
     - google/gemini-2.0-flash-001
     - mistralai/mistral-small-24b-instruct-2501:free
   
   A link to the full list of available models (https://openrouter.ai/models) makes exploration easier for developers.
   
   4. LLM Model Node: Plug Into OpenRouter
   
   The most critical integration is the “LLM Model” node, which employs OpenRouter credentials to call an OpenAI-compatible endpoint. This node creates a bridge to high-performing LLMs like:
   
   - DeepSeek-R1 Distill (LLaMA-based)
   - OpenAI GPT series
   - Google's Gemini models
   - Qwen Turbo and others
   
   Because OpenRouter supports prompt routing and billing across multiple leading LLM providers, it drastically simplifies the process of experimenting with or deploying different models on demand.
   
   5. Memory Buffer: Handling Conversational Context
   
   To maintain fluency across a multi-turn conversation, a Langchain “Memory Buffer Window” node is used to store interaction history. It uses the sessionId as a key to keep messages tied to specific users or threads.
   
   This enables use cases like:
   
   - Follow-up questions
   - Session-based knowledge sharing
   - Personalized recommendations
   
   6. AI Agent Node: Bringing It All Together
   
   The final processing logic happens in the Langchain “AI Agent” node. It:
   
   - Accepts the dynamic prompt (user message)
   - Links up with the memory and model nodes
   - Configures the prompt type to “define,” ensuring predictable behavior
   
   Effectively, this node acts as the smart entity that receives the user input, references context from memory, invokes the specified LLM, and then generates a response.
   
   Why Use OpenRouter in n8n?
   
   OpenRouter acts as a model aggregator and gateway between LLM providers. Instead of keeping track of unrelated APIs, token limits, and credential systems, developers can use OpenRouter to work with multiple models from one unified interface. When integrated into an automation tool like n8n, this approach offers:
   
   - Full LLM model flexibility (try new models in seconds)
   - Centralized API key management
   - Cost-conscious implementation (choose free or optimized models)
   - Scalable, no-code or low-code deployment
   
   Use Cases
   
   This setup is particularly suited for the following:
   
   - Multi-model chatbot demos or prototypes
   - AI-powered customer support agents
   - Language generation tools embedded in no-code apps
   - Teaching or experimenting with prompt engineering
   
   Conclusion
   
   This n8n workflow showcases the power of composable automation and modular AI integration. By leveraging OpenRouter as a LLM backend, and Langchain for orchestration, developers and AI enthusiasts can build, test, and scale intelligent assistants easily. Whether you’re switching LLMs for cost-saving or experimenting with performance, this setup ensures flexibility without friction—right from your n8n dashboard.
   
   Try modifying the model in the Settings node and see how different LLMs respond to the same query. With n8n, you’re in control of the conversation.
   
   Explore more models here: https://openrouter.ai/models
   
   Want to take it a step further? Connect this workflow with Slack, Telegram, or a frontend chat app to power your own interactive AI assistant in production.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.