N8N Tools - Agno Team Agent icon

N8N Tools - Agno Team Agent

Level 4 AI Agent with team collaboration - Multi-agent coordination, task delegation, and shared memory powered by Agno framework
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Overview

The Agno Team Agent node is a Level 4 AI agent designed for multi-agent collaboration within n8n workflows. It enables coordination among multiple specialized AI agents working as a team to tackle complex tasks by delegating subtasks, combining insights, and leveraging shared memory. This node is particularly useful when you want to simulate or orchestrate teamwork among AI agents with different expertise areas, improving the quality and depth of results.

Common scenarios include:

  • Collaborative content creation where researchers gather information and writers produce text.
  • Data analysis projects involving data analysts and strategists working together.
  • Complex problem solving requiring multiple perspectives such as developers, mathematicians, and designers.
  • Automated workflows that require consensus or hierarchical decision-making among AI agents.

Practical example: You provide a task like "Develop a marketing strategy for a new product launch." The node coordinates a team of agents (e.g., researcher, strategist, content writer) who work sequentially or in parallel, share knowledge via memory, and produce a combined output.

Properties

Name Meaning
Team Agent Recommendation Informational notice explaining that Reasoning Tools and Memory are automatically included for multi-agent collaboration.
Instructions System instructions guiding the team agent's behavior and collaboration approach. Example: coordinating, delegating tasks, and combining insights.
Message Input message or task description for the team to collaborate on.
Team Configuration Collection of options configuring the team setup:
- Team Size: Number of agents (2–10).
- Collaboration Strategy: How agents work together (Sequential, Parallel, Hierarchical, Democratic).
- Specialist Roles: Roles assigned to team members (e.g., Researcher, Content Writer, Developer).
- Coordination Mode: Task distribution method (Auto, Manual, Hybrid).
- Quality Control: Peer review, consensus threshold, conflict resolution.
- Communication Protocol: Format of inter-agent communication (Structured, Natural Language, Minimal).
Enable Memory Boolean to enable shared team memory and collaboration history.
Memory Configuration Options for memory management:
- Session ID: Unique identifier for the collaboration session.
- Shared Memory: Whether memory is shared among team members.
Enable Tools Boolean to enable native Agno tools (Reasoning Tools are always included automatically).
Agno Native Tools Selection of native tools to enable for ultra-fast performance (~3μs execution), including Reasoning, Knowledge, Web Search, Data Analysis, Finance, Email, HTTP Requests, File System, Database, Shell.
Advanced Options Additional settings:
- Temperature: Controls randomness/balance in team coordination (0–2).
- Max Tokens: Token limit for collaboration.
- Output Format: Output style (Text, Markdown, JSON).
Report Issue Notice with links to support form and GitHub issues page for reporting bugs or getting help.

Output

The node outputs a JSON object containing:

  • response: The main response text or result from the team collaboration.
  • usage: Information about token usage or resource consumption.
  • model: The language model used for the operation.
  • agentType: Fixed value "teamAgent".
  • agentLevel: Fixed value 4 indicating the agent sophistication level.
  • executionTime: Time taken for the operation in milliseconds.
  • teamResults: Array of detailed results or outputs from individual team members or sub-agents.
  • collaborationStrategy: The collaboration strategy used (e.g., sequential, parallel).
  • teamMembers: List of team members involved in the collaboration.
  • agnoFramework: Boolean flag indicating use of the Agno framework.
  • performance: String indicating performance metric (e.g., "~3μs").

If an error occurs and "Continue On Fail" is enabled, the output will contain an error field with the error message and timestamp.

No binary data output is produced by this node.

Dependencies

  • Requires connection to an AI language model node input (e.g., OpenAI GPT models).
  • Requires an API key credential for the external N8N Tools API service.
  • Makes HTTP POST requests to the Agno backend API endpoint at https://n8ntools-agno-production.up.railway.app/api/v1/agent.
  • Uses native Agno tools optionally for enhanced capabilities and ultra-fast execution.
  • The node expects proper configuration of the AI model input and valid API credentials in n8n.

Troubleshooting

  • Missing AI Model Input:
    Error: "AI Language Model input is required. Please connect a model node."
    Resolution: Connect a compatible AI language model node to the first input of this node.

  • Missing API Credentials:
    Error: "N8N Tools API credentials are required."
    Resolution: Configure and provide a valid API key credential for the external N8N Tools API.

  • LLM Invocation Errors:
    If the underlying language model invocation fails, the error message will be prefixed with "LLM Error:" followed by details.
    Resolution: Check your AI model node configuration, network connectivity, and API limits.

  • Timeouts or Slow Responses:
    The node sets a timeout of 180 seconds for the API call. Long-running tasks may hit this limit.
    Resolution: Adjust task complexity, reduce max tokens, or check network stability.

  • Incorrect Team Configuration:
    Invalid values for team size, roles, or strategies might cause unexpected behavior.
    Resolution: Ensure team size is between 2 and 10, roles are valid, and collaboration strategy matches supported options.

Links and References

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