Quickstart

Prerequisites

Install the CLI

npm install -g @ao-ai/cli

ao login

This opens your browser to authenticate. Once done, your API key is stored securely on your machine.

Initialize your project

Inside your agent’s project directory:

ao init

This creates an ao.toml file:

name = "my-agent"
framework = "langgraph"
entrypoint = "./graph.py:graph"

python = "3.11"

[runtime]
max_retries = 3
timeout = 300 # 5 minutes

Edit entrypoint to point to your agent’s main file.

Deploy

ao deploy

AO zips your project, builds a Docker image, and deploys it. You’ll see a link to your deployment in the dashboard when it’s done.

Deploying agent...
Packing project...
Deployment successful!
View your deployment at: https://aodeploy.com/dashboard/deployments

Run your agent

ao run --deployment <deployment-id> --input '{"messages": ["your input here"]}'

Find your deployment ID in the dashboard.

Once deployed, AO handles:

Retries - if your agent fails, it retries automatically with exponential backoff up to max_retries
Timeouts - if a run exceeds timeout seconds, it’s killed and retried
Queues - runs are queued durably so nothing is lost if the server restarts, processed concurrently across workers, and never duplicated
Scheduling - run your agent on a cron schedule, no infrastructure needed
Logging - every tool call, LLM call, and error is logged and visible in the dashboard
State - each retry knows what attempt it’s on and what the previous error was

Learn how to customize retries, timeouts, scheduling, and more.