Tutorial
A linear path from "hello world" to a production-shape multi-agent app. Read in order. Each step builds on the previous one and ends with a verified live test.
Prerequisites
- Python 3.12+
- A running daemon (
digitorn start) - Either a local Ollama instance OR an API key for one of the supported providers (Validated provider hints)
Steps
| Step | What you build | What you learn |
|---|---|---|
| 1. Getting started | Hello-world chatbot | Install, validate, deploy, chat from the CLI |
| 2. Conversation with memory | Assistant that remembers facts you tell it | System prompts, the memory module, Remember, auto-recall |
| 3. Add a tool | Bot that reads files in your workspace | Modules, capabilities, the filesystem module |
| 4. Multi-agent team | Coordinator + 2 parallel specialists | The Agent tool, role-based delegation, isolation |
| 5. Background mode | Cron-driven monitor | Triggers, channels, payload schemas |
| 6. UI surfaces | Workspace pane the agent writes into | ui.workspace (renderer), workspace module |
| 7. Deploying | Production-shape app with deny-by-default | Capabilities, behaviour profile, credential schema |
Every step is written and tested live: each YAML deploys against the daemon, every transcript shown is the verbatim output, every tool call count and trigger result was captured by the live event stream.
What you'll have at the end
A multi-agent app that:
- Spawns specialist sub-agents in parallel
- Persists working memory across conversations
- Has a workspace pane the agent writes to in real time
- Connects to one external service via the credentials vault
- Has a background trigger that wakes it on a cron schedule
- Runs sandboxed in production
When to leave the tutorial
The tutorial is opinionated and linear. Once you've finished it, work from Reference and Language directly - those are the canonical surfaces. Come back to Concepts any time the framework's why is unclear.