How to Implement a Multi-Agent Team Using Agno: A Complete Guide

Agno enables you to build multi-agent teams by composing specialized skill agents with SequentialAgent or CoordinatorAgent orchestrators, then exposing a root agent that routes user requests to the appropriate pipeline.

The Shubhamsaboo/awesome-llm-apps repository demonstrates production-ready patterns for implementing a multi-agent team using Agno. This guide breaks down the architecture, implementation steps, and real-world code from the VC Due-Diligence Agent Team example.

Understanding the Agno Multi-Agent Architecture

Agno organizes multi-agent systems into three distinct layers. Each layer uses specific classes from the agno.agent and agno.agents modules to separate concerns between specialized reasoning and workflow orchestration.

Skill Agents (Specialized LLMs)

Skill agents are atomic units that perform a single domain-specific task. In advanced_ai_agents/multi_agent_apps/agent_teams/ai_vc_due_diligence_agent_team/agent.py, the company_research_agent is defined at lines 23-31 as an Agent (or LlmAgent) instance configured with a specific model, instructions, and optional tools.

These agents accept templated inputs—such as {company_info} or {market_analysis}—and return structured outputs that feed downstream stages.

The Orchestrator Layer

The orchestrator chains skill agents into executable pipelines. Agno provides SequentialAgent for linear workflows and CoordinatorAgent for conditional routing.

In the VC Due-Diligence implementation (lines 29-41 of the same file), a SequentialAgent named DueDiligencePipeline lists sub-agents in execution order. The orchestrator automatically passes the previous agent’s output to the next agent’s input context using placeholder variables.

The Root Agent (Entry Point)

The root agent serves as the public-facing interface. It interprets user intent and decides whether to handle a request directly or transfer_to_agent (the orchestrator).

In agent.py lines 48-63, the root_agent is configured with instructions that trigger the pipeline when the user asks to analyze a startup. This pattern isolates the complexity of the multi-agent team from the end user, who interacts with a single conversational endpoint.

Building a Multi-Agent Team Step-by-Step

Follow these implementation steps to replicate the architecture found in the awesome-llm-apps repository.

Step 1: Define Specialized Skill Agents

Create focused agents for each task. Import Agent from agno.agent and configure model parameters, system instructions, and toolsets.

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.googlesearch import GoogleSearchTools

def create_company_research_agent(api_key: str) -> Agent:
    model = Gemini(id="gemini-2.0-flash-exp", api_key=api_key)
    return Agent(
        model=model,
        name="CompanyResearchAgent",
        description="Researches a startup via web search",
        instructions=[
            "Search for the company's website, Crunchbase, news, etc.",
            "Extract name, founding date, team size, product, funding, traction."
        ],
        tools=[GoogleSearchTools()],
        markdown=True,
    )

Source: advanced_ai_agents/multi_agent_apps/agent_teams/ai_vc_due_diligence_agent_team/agent.py (lines 23-31).

Step 2: Create the Sequential Orchestrator

Compose skill agents into a pipeline using SequentialAgent. The sub_agents list defines execution order, and the framework handles context passing between stages.

from agno.agents import SequentialAgent

# Assume previous agents are instantiated

company_agent = create_company_research_agent(key)
market_agent = create_market_analysis_agent(key)
finance_agent = create_financial_modeling_agent(key)

due_diligence_pipeline = SequentialAgent(
    name="DueDiligencePipeline",
    description="Runs research → market → financial modeling → …",
    sub_agents=[company_agent, market_agent, finance_agent],
)

Source: Orchestration block in ai_vc_due_diligence_agent_team/agent.py (lines 29-41).

Step 3: Configure the Root Agent for Request Routing

Expose a single entry point that delegates to the pipeline. Use the sub_agents parameter to register the orchestrator, and include instructions that trigger transfer_to_agent.

root_agent = Agent(
    name="DueDiligenceAnalyst",
    model=Gemini(id="gemini-3-flash-preview", api_key=key),
    description="Front-end LLM that hands off VC due-diligence queries",
    instructions=[
        "If the user asks to analyze a startup, forward to the DueDiligencePipeline",
        "Otherwise answer directly."
    ],
    sub_agents=[due_diligence_pipeline],
)

Source: Root definition in ai_vc_due_diligence_agent_team/agent.py (lines 48-63).

Step 4: Deploy with a User Interface

Integrate the root agent into a web interface. The repository uses Streamlit to handle file uploads and display multi-agent outputs.

import streamlit as st
from ai_vc_due_diligence_agent_team.agent import root_agent

st.title("🚀 VC Due-Diligence Bot")
query = st.text_input("Enter a startup name or URL")

if query:
    with st.spinner("Analyzing…"):
        result = root_agent.run(message=query)
        st.markdown(result.content)

Source: UI patterns adapted from multimodal_design_agent_team/design_agent_team.py (lines 97-105).

Real-World Example: VC Due-Diligence Agent Team

The ai_vc_due_diligence_agent_team implementation in the repository demonstrates a production-grade multi-agent team. It comprises eight specialized skill agents:

• company_research_agent – Gathers baseline data via web search
• market_analysis_agent – Generates market sizing and competitive landscape
• financial_modeling_agent – Produces projection charts using generate_financial_chart
• risk_assessment_agent – Performs deep risk analysis
• investor_memo_agent, report_generator_agent, infographic_generator_agent – Synthesize final deliverables

These agents are chained via SequentialAgent at lines 29-41 of agent.py, which passes context through templated placeholders like {company_info} and {market_analysis}. The root_agent defined at lines 48-63 acts as the gatekeeper, transferring control to the pipeline when it detects a due-diligence request.

Alternative Patterns: Parallel Execution and Multimodal Teams

Not all multi-agent teams require sequential processing. The multimodal_design_agent_team demonstrates parallel skill agents that operate simultaneously:

• vision_agent – Analyzes uploaded images or mockups
• ux_agent – Generates user experience recommendations
• market_agent – Researches market fit

These agents are instantiated independently and invoked based on UI selections in design_agent_team.py (lines 1-31). This pattern suits use cases where inputs can be processed in isolation and aggregated later, reducing total latency compared to sequential pipelines.

Summary

• Agno provides a lightweight framework to implement a multi-agent team using three layers: skill agents, an orchestrator, and a root agent.
• Skill agents are specialized Agent instances configured with specific models, instructions, and tools for single-domain tasks.
• Orchestrators like SequentialAgent chain skill agents together, automatically passing context via templated placeholders.
• Root agents serve as the user-facing entry point, using transfer_to_agent to delegate to the appropriate pipeline.
• The ai_vc_due_diligence_agent_team in the awesome-llm-apps repository demonstrates a production implementation with eight specialized agents processing startup analysis end-to-end.

Frequently Asked Questions

What is the difference between Agent and SequentialAgent in Agno?

Agent is the base class for autonomous LLM workers that perform specific tasks using tools and instructions. SequentialAgent is a higher-level orchestrator that accepts a list of sub_agents and executes them in order, passing the output of each agent to the next via context variables. Use Agent for atomic skills and SequentialAgent for multi-step workflows.

How does context pass between agents in a sequential pipeline?

Agno uses string templating to pass context. When you define a SequentialAgent, the framework injects the previous agent's output into the next agent's context using placeholder variables like {company_info} or {market_analysis}. These placeholders are referenced in the receiving agent's instructions or description, allowing each stage to build upon previous results without manual state management.

Can I run agents in parallel instead of sequentially?

Yes. While SequentialAgent is used for dependent, ordered execution, you can implement parallel execution by instantiating multiple Agent objects and invoking them independently within your application logic. The multimodal_design_agent_team example demonstrates this pattern, where vision, UX, and market agents run simultaneously based on UI inputs, and their results are aggregated after all complete.

What models and tools work with Agno multi-agent teams?

Agno is model-agnostic and supports Google Gemini, OpenAI GPT, Anthropic Claude, and other LLM providers through a unified interface. You configure the model when instantiating an Agent (e.g., Gemini(id="gemini-2.0-flash-exp")). For tools, Agno integrates with function calling, web search, image generation, and custom Python functions, which you attach via the tools parameter in your skill agent definitions.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}