AgentOS AWS Template

Deploy a multi-agent system on AWS with ECS Fargate, RDS PostgreSQL, and an application load balancer.

What's Included

Agent	Pattern	Description
Knowledge Agent	Agentic RAG	Answers questions from a knowledge base.
MCP Agent	MCP Tool Use	Connects to external services via MCP.

Get Started

# Clone the repo
git clone https://github.com/agno-agi/agentos-aws-template.git agentos-aws
cd agentos-aws

# Add OPENAI_API_KEY
cp example.env .env
# Edit .env and add your key

# Start the application
ag infra up --env dev

# Load documents for the knowledge agent
docker exec -it agentos-aws-template-api python -m agents.knowledge_agent

Confirm AgentOS is running at http://localhost:8000/docs.

Connect to the Web UI

1. Open os.agno.com and login
2. Add OS → Local → http://localhost:8000
3. Click "Connect"

Stop the application

ag infra down --env dev

Deploy to AWS

Requires:

• AWS CLI installed and configured
• OPENAI_API_KEY set in your environment

ag infra up --env prd

See the full AWS deployment guide for configuring subnets, HTTPS, and connecting to the control plane.

Manage deployment

ag infra patch --env prd     # Update after changes
ag infra down --env prd      # Tear down all resources

The Agents

Knowledge Agent

Answers questions using hybrid search over a vector database (Agentic RAG).

Load documents:

# Local
docker exec -it agentos-aws-template-api python -m agents.knowledge_agent

# AWS
ECS_CLUSTER=agentos-aws-template-prd
TASK_ARN=$(aws ecs list-tasks --cluster $ECS_CLUSTER --query "taskArns[0]" --output text)

aws ecs execute-command --cluster $ECS_CLUSTER \
    --task $TASK_ARN \
    --container agentos-aws-template \
    --interactive \
    --command "python -m agents.knowledge_agent"

Try it:

What is Agno?
How do I create my first agent?
What documents are in your knowledge base?

MCP Agent

Connects to external tools via the Model Context Protocol.

Try it:

What tools do you have access to?
Search the docs for how to use LearningMachine
Find examples of agents with memory

Project Structure

├── agents/                  # Agents
│   ├── knowledge_agent.py   # Agentic RAG
│   └── mcp_agent.py         # MCP Tool Use
├── app/
│   ├── main.py              # AgentOS entry point
│   └── config.yaml          # Quick prompts config
├── db/
│   ├── session.py           # PostgreSQL database helpers
│   └── url.py               # Connection URL builder
├── infra/                   # AWS infrastructure config
│   ├── settings.py          # Region, subnets, image settings
│   ├── dev_resources.py     # Docker resources (local)
│   └── prd_resources.py     # AWS resources (ECS, RDS, ALB)
├── scripts/                 # Helper scripts
├── Dockerfile
├── example.env
└── pyproject.toml           # Dependencies

Common Tasks

Add your own agent

1. Create agents/my_agent.py:

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from db import get_postgres_db

my_agent = Agent(
    id="my-agent",
    name="My Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    db=get_postgres_db(),
    instructions="You are a helpful assistant.",
)

2. Register in app/main.py:

from agents.my_agent import my_agent

agent_os = AgentOS(
    name="AgentOS",
    agents=[knowledge_agent, mcp_agent, my_agent],
    ...
)

3. Restart: ag infra up --env dev

Add tools to an agent

Agno includes 100+ tool integrations. See the full list.

from agno.tools.slack import SlackTools
from agno.tools.google_calendar import GoogleCalendarTools

my_agent = Agent(
    ...
    tools=[
        SlackTools(),
        GoogleCalendarTools(),
    ],
)

Add dependencies

1. Edit pyproject.toml
2. Regenerate requirements: ./scripts/generate_requirements.sh
3. Rebuild: ag infra up --env dev

Use a different model provider

1. Add your API key to .env (e.g., ANTHROPIC_API_KEY)
2. Update agents to use the new provider:

from agno.models.anthropic import Claude

model=Claude(id="claude-sonnet-4-5")

3. Add dependency: anthropic in pyproject.toml

---

Local Development

For development without Docker:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Setup environment
./scripts/venv_setup.sh
source .venv/bin/activate

Environment Variables

Variable	Required	Default	Description
OPENAI_API_KEY	Yes	-	OpenAI API key
DB_HOST	No	localhost	Database host
DB_PORT	No	5432	Database port
DB_USER	No	ai	Database user
DB_PASS	No	ai	Database password
DB_DATABASE	No	ai	Database name
RUNTIME_ENV	No	prd	Set to dev for auto-reload

Learn More

• AWS Deployment Guide
• Agno Documentation
• AgentOS Documentation
• Agno Discord