How to Run a Long-Running AI Agent with nanobot

nanobot can keep agent work alive across turns through sustained goals, persistent sessions, scheduled automations, local triggers, and a gateway process that stays running.

What you will build

• a working local agent
• a persistent chat session
• a long-running goal or automation
• a gateway process for background delivery

When to use this

Use this when the task is not a one-shot answer: project work, recurring checks, scheduled summaries, file maintenance, multi-step research, or local triggers from scripts and build jobs.

Install

python -m pip install nanobot-ai
nanobot onboard --wizard
nanobot agent -m "Hello!"

Minimal working example

Start a gateway:

nanobot gateway

From the WebUI or a chat session, start a sustained goal:

/goal Review this workspace, identify missing tests, and propose the smallest next fix.

For scheduled or trigger-based runs, create the automation from the target chat so nanobot can link it to the correct session and workspace.

Production notes

• Keep the gateway running for chat apps, WebUI sessions, automations, and local triggers.
• Use stable session keys or chat sessions for work that should preserve context.
• Keep goals bounded and explicit about done-ness.
• Review Automations in the WebUI before relying on a schedule.

Security notes

• Treat long-running goals as delegated work with real tool access.
• Restrict workspaces and shell execution before scheduling unattended tasks.
• Keep chat access narrow so unknown users cannot create goals or automations.

Troubleshooting

• If a goal appears stuck, inspect the active session and gateway logs.
• If an automation does not run, check that it is linked to a chat/session and that the gateway is still running.
• If a local trigger fails, check the command copied from the WebUI Automations view.

Related nanobot docs

• Automations
• WebUI Automations
• Chat Commands
• Memory
• Deployment