Multi-Agent Conversation
For the Multi-Agent Conversation workflow, engage in dynamic discussions with multiple AI agents simultaneously. Configure unique settings for each agent, including their names and behaviors, to tailor interactions. Users can initiate conversations using @mentions to direct specific queries, or if no mentions are present, all agents will respond in random order. This workflow enables seamless multi-agent collaboration, enhancing user experience and providing diverse perspectives in real-time conversations.
View Large Image
Workflow Overview
For the Multi-Agent Conversation workflow, engage in dynamic discussions with multiple AI agents simultaneously. Configure unique settings for each agent, including their names and behaviors, to tailor interactions. Users can initiate conversations using @mentions to direct specific queries, or if no mentions are present, all agents will respond in random order. This workflow enables seamless multi-agent collaboration, enhancing user experience and providing diverse perspectives in real-time conversations.
Target Audience
- Developers: Those looking to implement multi-agent systems in their applications.
- Product Managers: Individuals seeking to enhance user engagement through conversational AI.
- AI Enthusiasts: Users interested in experimenting with advanced AI interactions and workflows.
- Researchers: Academics studying the dynamics of multi-agent conversations.
- Businesses: Companies aiming to automate customer service or support through AI agents.
Problem Solved
This workflow enables seamless interactions among multiple AI agents, allowing users to engage with various assistants simultaneously. It addresses the challenge of coordinating responses from different AI models, ensuring that users receive diverse perspectives and insights in a single conversation. By utilizing @mentions, users can direct questions to specific agents, enhancing the overall interaction experience.
Workflow Steps
- Trigger on Chat Message: The workflow starts when a chat message is received via a webhook.
- Define Global and Agent Settings: Global user settings and agent-specific configurations are defined, including names, models, and system messages.
- Extract Mentions: The workflow analyzes the chat message to identify any @mentions of agents, determining which assistants should respond.
- Loop Over Items: For each identified agent, the workflow processes their responses in a loop, ensuring each agent's unique input is captured.
- Set User and Last Assistant Messages: The messages from the user and the last assistant are set as inputs for the current agent.
- AI Agent Processing: Each agent processes the input and generates a response based on its unique settings.
- Combine Responses: Once all agents have responded, their messages are combined and formatted for clarity.
- Output Final Response: The final consolidated response is sent back to the user, showcasing inputs from all relevant agents.