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:  
   Integrating Any LLM with n8n Using OpenRouter: A Flexible Workflow for AI-Powered Chatbots
   
   Meta Description:  
   Discover how to use any large language model (LLM) with n8n through OpenRouter. Explore this configurationally dynamic, no-code/low-code workflow that powers intelligent, session-aware AI chatbots using customizable models.
   
   Keywords:  
   n8n, OpenRouter, LLM, chatbot, AI agent, workflow automation, deepseek, langchain, memory buffer, OpenAI, model selection, prompt customization, GPT alternative, open source automation, AI workflows
   
   Third-Party APIs Used:  
   
   - OpenRouter API (via OpenAI-compatible API key)  
   - LangChain (used internally via n8n-supported LangChain nodes)
   
   —
   
   Article:
   
   Power Your AI Workflow with Any LLM Using OpenRouter and n8n
   
   As the demand for AI-driven applications—especially those powered by large language models (LLMs)—continues to surge, developers and automation enthusiasts are turning to tools like n8n to build scalable, flexible, and no-code/low-code workflows. One standout integration is between n8n and OpenRouter, a universal API interface that allows users to tap into a wide selection of LLMs beyond just OpenAI.
   
   In this article, we’ll break down a ready-to-use n8n workflow entitled “Use any LLM-Model via OpenRouter” that enables developers to trigger intelligent conversations, dynamically select AI models, manage prompt context, and maintain session history—all while customizing the AI agent to suit virtually any use case.
   
   Let’s dive into how this powerful automation works.
   
   Triggering AI on Chat Message Reception
   
   The workflow begins with a listener node titled “When chat message received.” This node acts as the entry point to the workflow whenever a new chat message is generated. It captures user input along with associated metadata such as a session ID to identify conversation context.
   
   This webhook-style trigger enables the workflow to be highly responsive, whether you’re feeding input from a chatbot interface, an app, or an external system that supports webhook events.
   
   Dynamic Settings Configuration
   
   Next comes the “Settings” node, a simple yet powerful step where the model name, input prompt, and session ID are assigned dynamically. Instead of hardcoding these values, the workflow references variables like $json.chatInput and $json.sessionId, ensuring flexibility.
   
   The key element here is the model selection, set by default to deepseek/deepseek-r1-distill-llama-8b. While this is an excellent LLM on its own, the real beauty lies in your ability to configure this with nearly any model hosted on OpenRouter, from Google’s Gemini Flash to Mistral and Meta’s LLaMa derivatives.
   
   Model Selection Made Easy
   
   To aid users in choosing the best model for their needs, a helpful sticky note in the workflow lists examples like:
   
   - openai/o3-mini  
   - google/gemini-2.0-flash-001  
   - deepseek/deepseek-r1-distill-llama-8b  
   - mistralai/mistral-small-24b-instruct-2501:free  
   - qwen/qwen-turbo
   
   Even more options are available through the OpenRouter model directory at https://openrouter.ai/models, making this workflow not just adaptable, but scalable based on performance, pricing, or licensing preferences.
   
   AI Agent with Memory and Intelligence
   
   The workflow takes things up a notch with its internal AI agent, implemented using LangChain nodes within n8n. Several different components come together here:
   
   - AI Agent: Uses the formatted input and model to generate intelligent output.
   - LLM Model: Connects to OpenRouter through a compatible OpenAI-style API and processes the prompt using the selected LLM.
   - Chat Memory: Maintains conversation history using LangChain’s sliding window memory buffer keyed by session. This means your AI can remember previous context, making it suitable for contextual chatbots, customer support assistants, or tutors.
   
   Memory buffer support is crucial for user experience. Instead of generating one-off replies like a basic form-filler bot, your AI agent can follow a thread across multi-turn conversations.
   
   Why Use OpenRouter?
   
   Traditionally, working with LLMs meant choosing a specific vendor—such as OpenAI—and being tied to their infrastructure, pricing, and constraints. OpenRouter acts as an intelligent router layer that gives users access to dozens of supported models through a unified interface that mimics the OpenAI API schema.
   
   With OpenRouter, you're no longer limited to GPT-3.5 or GPT-4. You can select cutting-edge alternatives like DeepSeek, Gemini, Qwen, or Mistral—sometimes free or cheaper depending on load and use case.
   
   In this workflow, OpenRouter’s API key is managed securely under the credentials labeled as "OpenRouter," abstracting away API-specific intricacies and letting users focus on inputs and outputs.
   
   Use Cases and Applications
   
   This plug-and-play AI automation serves as a foundation for several applications:
   
   - Custom AI Chatbots with dynamic memory and multi-turn dialogue
   - Virtual assistants that change behavior based on selected LLM characteristics
   - AI tutors for specific fields (law, medical, programming) with specialized models
   - Prototyping new AI features fast without complex backend dependencies
   - Comparative model benchmarking for cost and accuracy tradeoffs
   
   Conclusion
   
   This n8n workflow exemplifies the legibility and power of integrating LLMs into modern automation. By leveraging OpenRouter, users are no longer confined to a single provider and can experiment freely with emerging models. Combined with LangChain agents and conversation memory, the workflow represents a modular, maintainable, and production-ready solution for anyone building AI-enabled processes.
   
   Start with this template, customize it according to your deployment environment or use case, and you’ll be unlocking a future where AI works exactly how—and with whatever model—you need.
   
   —
   
   Get Started:  
   👉 Explore this workflow via n8n.io or community templates   
   🔑 Don't forget to register for an OpenRouter API key  
   🛠️ Learn more about supported models: https://openrouter.ai/models  
   
   With n8n and OpenRouter, your AI automations just got a whole lot more intelligent.

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.