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.
Run this with your team's AIWhat This Recipe Does
The Multi-Agent Conversation automation transforms your standard AI interactions into a collaborative team environment. Instead of relying on a single generic AI response, this system orchestrates multiple specialized agents that work together to solve complex problems. By breaking down requests and routing them through specific logic paths, the application ensures that every part of a query is handled by the most qualified virtual expert. This approach significantly increases the accuracy and depth of the output, making it ideal for high-stakes business decisions, technical troubleshooting, or creative brainstorming. Business users can deploy this to handle sophisticated workflows that require different perspectives, such as a legal review followed by a marketing summary. The result is a more professional, nuanced, and reliable AI interaction that moves beyond simple chat and into true automated problem-solving.
What your team gets
Forms and dashboards, so it is not a script only one person understands
Runs on your schedule in the cloud, so it does not stop when a laptop closes
Endpoints, so the rest of your stack can trigger the same work
Langchain.chatTrigger, Langchain.agent, SplitInBatches, Code, Langchain.memoryBufferWindow connected for the team, not per person
How It Works
- 1
Open the recipe and connect your accounts
Connect Langchain.chatTrigger and Langchain.agent once, in your team cloud, and nobody has to do it again on their own machine
- 2
Tell your own agent what is different about your process
Claude, ChatGPT, Cursor, whichever your team already uses. It adapts the recipe to how you actually work
- 3
Run it, then leave it running
It lives in your team cloud, so it keeps going after you close the laptop and every teammate's AI can use it
Who Uses This
- Product managers use this to analyze customer feedback by deploying one agent to identify technical bugs and another to summarize feature requests.
- Marketing teams use this to create content where one agent focuses on SEO optimization while a second agent ensures the brand voice remains consistent.
- Customer success departments use this to handle complex support tickets by having one agent pull technical documentation and another draft a personalized, empathetic response.
Frequently Asked Questions
How do multiple agents improve the quality of the chat?
By assigning specific roles to different agents, the system prevents the AI from becoming overwhelmed by complex tasks, leading to more precise and high-quality results.
Can I define the specific expertise of each agent?
Yes, you can customize the instructions for each agent within the workflow to ensure they focus on the specific business rules or domain knowledge you require.
Does this automation support different AI models for different steps?
The workflow is designed to be flexible, allowing you to use the most cost-effective or powerful models for different parts of the conversation logic.
What kind of user interface does this provide?
When converted via Runwork, this workflow becomes a polished chat application that your team or customers can interact with directly through any web browser.
Coming from n8n?
This recipe uses nodes like Langchain.chatTrigger, Langchain.agent, SplitInBatches, Code and 5 more. On Runwork, you don't need to learn n8n's workflow syntax. Describe what you want to your own AI agent in plain English.
Based on n8n community workflow. View original
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Run this with the AI your team already uses
Your agent adapts it, your team cloud keeps it running, and everyone's AI can find it.
Open this recipe in Runwork