This article provides a complete, practical walkthrough of the Stickynote Splitinbatches Automation Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

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:
   Building Scalable Multi-Agent Conversations in n8n with OpenRouter-Powered AI Personas
   
   Meta Description:
   Discover how to create intelligent, multi-agent conversational workflows using n8n and OpenRouter-powered AI agents. This tutorial explores a no-code solution to simulate collaborative assistant dialogues with dynamic agent selection, memory, and personality customization.
   
   Keywords:
   n8n multi-agent, OpenRouter AI, conversational agents, AI personas, multi-assistant chat, LLM orchestration, n8n workflow tutorial, chatbot automation, LangChain n8n, OpenRouter credentials, GPT multi-agent, Claude, Gemma, Chad AI
   
   Third-party APIs Used:
   - OpenRouter API (for invoking language models like GPT-4o, Claude, and Gemini via OpenRouter)
   
   Article:
   
   Building Scalable Multi-Agent Conversations in n8n with OpenRouter-Powered AI Personas
   
   As natural language interfaces evolve, the ability to interact with multiple intelligent agents—each with distinct goals, personalities, and conversational styles—presents exciting new possibilities. This article explores a scalable solution for orchestrating multi-agent AI conversations inside n8n, the powerful open-source workflow automation platform.
   
   Through a clever combination of modular nodes, dynamic prompt construction, and OpenRouter-connected language models, this no-code workflow enables users to engage in rich, parallelized dialogs with multiple AI assistants in a single message flow. Whether you're simulating team meetings, advisory panels, or creative brainstorming sessions, the Multi-Agent Conversation workflow makes it possible in just a few steps.
   
   Let’s unpack how this system works, its benefits, and how it can be customized for your use.
   
   What the Workflow Achieves
   
   This n8n automation initiates an AI-powered conversation with one or more configurable "digital assistants" every time a user sends a chat message. Users can:
   
   - Mention specific agents in their message using @mentions (e.g., “@Chad, what do you think?”).
   - Let all agents respond randomly if no mentions are used.
   - Customize each agent’s name, system prompt (personality), and model.
   - Maintain short-term conversation memory (context) using a shared memory buffer.
   - Combine and format responses from each agent into a unified chat output.
   
   The result is fluid and context-aware dialogue that feels like speaking to a chat room full of unique virtual personas.
   
   Core Features
   
   🧠 Custom Personality, System Prompts, and LLMs per Agent
   
   Within the Define Agent Settings node, users can configure any number of AI agents like:
   
   - Chad – an eccentric, creative assistant powered by OpenAI’s GPT-4o.
   - Claude – a logical, practical responder using Anthropic’s Claude 3.7.
   - Gemma – a friendly, debate-loving assistant running Google's Gemini.
   
   Each agent has its system message, which influences tone and behavior, along with a designated OpenRouter-accessible model.
   
   📍 Smart Targeting with @Mentions
   
   The Extract Mentions node uses JavaScript to intelligently parse the user’s message and identify any @mentions referencing agents (e.g., @Chad, @Claude). Mentions are used to determine which agents should respond and in what order.
   
   If no mentions are present, the workflow randomly calls on all configured agents to contribute, ensuring dynamic engagement by default.
   
   🔁 Dynamic Agent Loop
   
   A SplitInBatches node (Loop Over Items) iterates through the chosen agents one at a time. On each loop:
   
   - A dynamic system message is built per agent using provided metadata about the user (name, location, preferences).
   - Conversation memory from earlier inputs and assistant replies is preserved using a Buffer Window Memory node.
   - Responses are aggregated and formatted together in Combine and Format Responses.
   
   The agents never "see" each other's responses but contribute singularly to a shared conversation thread.
   
   💾 Context Preservation via Shared Memory
   
   To support coherent multi-round interactions, a Simple Memory node buffers the last 99 messages per session—based on a custom key tied to sessionId. This enables follow-up questions and references to prior answers, enhancing believability and usefulness in dialogue.
   
   No-code Friendly, Infinite Customization
   
   Although the underlying mechanics use advanced node chaining and code snippets, the workflow is entirely no-code-friendly for everyday users. Agents and global settings are maintained in early Code nodes (Define Global Settings, Define Agent Settings), allowing easy customization of:
   
   - Agent count, models, and prompt style
   - User details and preferences
   - Core conversation style (e.g., don’t be too agreeable)
   
   Developers can extend this with routing logic, saving responses to databases, or layering in feedback scoring, agent voting mechanisms, or stream-based chat UIs.
   
   Third-Party API Integration – OpenRouter
   
   The magic behind this multi-agent system lies in the OpenRouter API, which allows n8n to access cutting-edge models from various providers like OpenAI, Anthropic, and Google through a unified interface. Credentials are managed simply within n8n, and model selection is dynamic via variables—meaning one node serves all agents flexibly.
   
   Strengths and Limitations
   
   ✔ Pros:
   
   - Highly modular and scalable—add/remove agents easily
   - Reuses a single AI node dynamically for multiple personas
   - Supports memory across interactions, enriching dialogue relevance
   - Fully customizable in system message and model choice
   
   ✖ Limitations:
   
   - Agents respond one at a time; responses are not truly parallel
   - User sees outputs after all agents finish (not streaming)
   - No inter-agent conversation (yet)—each agent speaks to the user only
   
   Real-World Applications
   
   Use cases for such multi-agent workflows include:
   
   - Business scenario role plays (e.g., COO, CTO, Advisor perspectives)
   - Educational debates simulated by AI experts
   - Therapy bots with different emotional tones
   - Product planning / brainstorming across diverse disciplines
   - Gamified experiences with distinct character personas
   
   Final Thoughts
   
   This n8n workflow demonstrates the growing power of orchestrated AI. By using OpenRouter’s multi-provider LLM access and n8n’s automation architecture, it’s now easy to create rich, collaborative AI experiences in minutes.
   
   If you're someone who appreciates working with AI tools that are modular, cross-compatible, and creatively empowering—this workflow is worth exploring, adapting, and evolving.
   
   You can grab the full workflow and instructions directly from n8n, or deploy a customized version to simulate your AI dream team today.
   
   — End —
   
   Let me know if you'd like an exportable Markdown version of this article or visuals to accompany the explanation!

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.