Scalable multi-agent chat using @mentions
Orchestrate a collaborative team of AI personalities in a single chat interface using OpenRouter models. By leveraging simple @mentions, you can direct specific tasks to specialized agents or let them all brainstorm together in a randomized sequence. This flexible setup makes it easy to scale your AI workforce just by tweaking a JSON configuration file.
Start BuildingWhat This Recipe Does
Managing complex business tasks often requires more than a single AI prompt. The Multi-Agent Conversation automation transforms your n8n workflows into a collaborative digital workspace where specialized AI agents work together to solve problems. Instead of receiving a generic response, this system coordinates multiple AI personas—each with a specific role—to analyze data, generate content, and refine outputs. This collaborative approach ensures higher accuracy and more nuanced results for sophisticated projects like strategic planning, content production, or customer support triage. By automating the hand-off between different AI specialists, your business reduces manual oversight and accelerates the transition from initial idea to final deliverable. This tool is essential for teams looking to scale their operations without sacrificing the quality of expert-level human logic. It turns a simple chat interface into a powerful coordination hub for your entire organization, allowing you to automate multi-step reasoning processes that previously required constant human intervention.
What You'll Get
Forms, dashboards, and UI components ready to use
Background automations that run on your schedule
REST APIs for external integrations
Langchain.chatTrigger, Langchain.agent, SplitInBatches, Code, Langchain.memoryBufferWindow configured and ready
How It Works
- 1
Click "Start Building" and connect your accounts
Runwork will guide you through connecting Langchain.chatTrigger and Langchain.agent
- 2
Describe any customizations you need
The AI will adapt the recipe to your specific requirements
- 3
Preview, test, and deploy
Your app is ready to use in minutes, not weeks
Who Uses This
- Content Marketing Teams use this to coordinate between specialized researcher, writer, and editor agents to produce publish-ready articles.
- Customer Support Managers use this to analyze complex tickets where one agent identifies sentiment while another suggests technical solutions based on internal documentation.
- Product Strategy Teams use this to simulate focus groups where different AI agents represent various customer personas to provide feedback on new feature concepts.
Frequently Asked Questions
Do I need technical skills to manage these agents?
No. Runwork converts the underlying logic into a user-friendly application interface, allowing you to focus on defining the roles and goals of your agents without writing code.
Can I customize the roles of the agents for different projects?
Yes. You can easily adjust the instructions and parameters for each agent within the workflow to suit specific business needs, such as switching from legal review to creative brainstorming.
How many agents can participate in a single conversation?
The system is designed to handle multiple agents. You can scale the complexity of the conversation by adding more specialized roles to ensure every aspect of your project is covered.
What is the final result of a multi-agent conversation?
You receive a comprehensive, refined output that has been vetted through multiple perspectives, resulting in a more polished and professional final product than a single AI model could provide.
Importing from n8n?
This recipe uses nodes like Langchain.chatTrigger, Langchain.agent, SplitInBatches, Code and 5 more. With Runwork, you don't need to learn n8n's workflow syntax—just describe what you want in plain English.
Based on n8n community workflow. View original
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