Hello World

Create a file called hello.keel:

agent Hello {
  role "A friendly greeter"
  model "claude-haiku"

  every 5.seconds {
    notify user "Hello from Keel!"
  }
}

run Hello

Run it:

KEEL_OLLAMA_MODEL=gemma4 keel run hello.keel

Output:

⚡ LLM provider: Ollama (http://localhost:11434)
▸ Starting agent Hello
  role: A friendly greeter
  model: gemma4 (ollama @ http://localhost:11434)

  ⏱ polling every 5 seconds
  ▸ Hello from Keel!

  ▸ Agent running. Press Ctrl+C to stop.
  ▸ Hello from Keel!
  ▸ Hello from Keel!

The agent prints “Hello from Keel!” every 5 seconds until you press Ctrl+C.

What just happened?

1. agent Hello — declares an agent named Hello
2. role "..." — describes what the agent does (used as system prompt context)
3. model "claude-haiku" — which LLM model to use (mapped to your local Ollama model)
4. every 5.seconds { ... } — runs the block every 5 seconds
5. notify user "..." — prints a message to the terminal
6. run Hello — starts the agent

Using AI

Let’s make the agent actually use AI:

type Mood = happy | neutral | sad

task analyze(text: str) -> Mood {
  classify text as Mood fallback neutral
}

agent MoodBot {
  role "Analyzes the mood of text"
  model "claude-haiku"

  every 10.seconds {
    mood = analyze("I love building programming languages!")
    notify user "Mood: {mood}"
  }
}

run MoodBot

The classify keyword sends the text to the LLM and maps the response to one of the enum variants. The fallback neutral ensures you always get a valid result.

Next: Your First Agent →