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

> Learn to implement a multi-agent team using Agno by composing skill agents with SequentialAgent or CoordinatorAgent. This guide shows how to create a root agent for request routing.

- Repository: [Shubham Saboo/awesome-llm-apps](https://github.com/shubhamsaboo/awesome-llm-apps)
- Tags: how-to-guide
- Published: 2026-02-16

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**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](https://github.com/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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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.

```python
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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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.

```python
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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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`.

```python
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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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.

```python
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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/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.