Monitor messages, trigger designated AI agents with @mentions, collect responses, and return a unified, formatted output, while enabling scalable configuration via a single JSON file.
The AI agent orchestrates several specialized AI agents in a single chat interface, each with its own model and instructions. It routes messages to the appropriate agents via @mentions, collects their responses, and returns a single, formatted output. Conversation history is maintained across turns, enabling coherent multi-agent collaboration without complex setup.
Manages diverse AI personalities in one conversation
Define multiple unique agents with names, models, and system instructions.
Trigger specific agents via @AgentName mentions in messages.
Configure a dedicated OpenRouter model for each agent.
Fallback to all agents when no mentions are used, in a randomized order.
Remember conversation history within the session to maintain context.
Return a single, formatted output that aggregates all agents' responses.
Two sentences explaining why this setup helps. It summarizes the practical shift after adoption.
A simple 3-step flow anyone can follow
Load global settings and per-agent configurations from a JSON file to define identities, models, and prompts.
Parse the chat for @AgentName mentions; if present, build an ordered list of target agents; if none, randomly select all defined agents.
Iterate through the selected agents, send each appropriate input, collect responses, and format them into a single output.
A realistic scenario with concrete task and outcome
Scenario: A 15-minute product launch brainstorming session with three agents — Gemma (market analysis), Claude (risk assessment), and Chad (technical feasibility). Task: Generate a multi-perspective launch plan. Outcome: A consolidated report with insights from each agent, ready for executive review.
Roles that gain from multi-agent collaboration
Need rapid, multi-perspective analysis in a single chat window.
Seek diverse insights on messaging and positioning from distinct personas.
Obtain feasibility and risk viewpoints in one place.
Draft consistent responses across scenarios with different tones.
Collaborate with personas to align tone and style.
Compare hypotheses and gather evidence from varied sources.
Tools that enable the AI agent to operate
Provides per-agent models and dynamic system prompts.
Maintains conversation history to preserve context for each response.
Centralizes agent definitions and global behavior in a single JSON file.
Common scenarios where this AI agent shines
Common questions about using an AI agent setup
The system supports multiple agents defined in the configuration. The exact number depends on resources and the complexity of prompts. Each agent adds its own model call and memory footprint, so consider performance implications for very large setups. You can scale gradually by adding agents to the JSON configuration and testing progressively. If latency becomes an issue, reduce the number of active agents per turn or group agents by task.
Responses are collected in sequence based on either specified mentions or a randomized order when no mentions exist. This ensures deterministic aggregation when needed and varied perspectives when not. Parallel responses could complicate input handling and output formatting, so the current flow processes agents one after another. You still receive a single, unified output after all responses are gathered. This keeps the final result coherent and easy to review.
OpenRouter credentials are configured in the AI agent interface by selecting or creating credentials and linking them to the agent’s model. Each agent can point to a different model, enabling task-specific capabilities. The credentials stay in the configuration and don’t require code changes for new agents. After setup, agents automatically use their designated model during conversations. This makes model management straightforward and scalable.
Yes. If you include @mentions in the message, agents respond in the order specified by those mentions. If no mentions are used, the system shuffles the agent list to determine response order. You can adjust the initial agent by changing the order in the JSON configuration or by crafting your prompt to prioritize a particular agent. The final output reflects the chosen order, maintaining transparency for review.
If an agent times out, the system continues with the remaining agents and includes placeholders or notes for the missing input in the final aggregation. Errors generate a structured fallback message indicating the agent and issue, so the user still receives a complete multi-view report. The memory context is preserved, so subsequent turns don’t lose prior context. You can reattempt or adjust the affected agent’s configuration from the JSON file.
Memory in this setup is scoped to the current chat session, allowing continuity across turns. It does not automatically persist between separate conversations unless you export and re-import the session data. If you need persistent memory, you can implement an external store and reference it in the memory layer. Within a session, agents remember prior interactions to maintain context and coherence.
Yes. Agent definitions and global settings are stored in a single JSON configuration that can be exported and imported into other chats. This makes rerunning scenarios or cloning setups quick and reliable. You can version-control configurations to track changes over time. Reuse ensures consistency across multiple teams and use cases.
Monitor messages, trigger designated AI agents with @mentions, collect responses, and return a unified, formatted output, while enabling scalable configuration via a single JSON file.
