> ## Documentation Index
> Fetch the complete documentation index at: https://docs.terminaluse.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start

> Create, deploy, and run your first Terminal Use agent

This quickstart follows one path end to end:

* install the CLI
* scaffold an agent
* deploy it
* create a project and task
* pull the task output back to your machine

The point of Terminal Use is that you do not have to bolt together your own deploy flow, sandbox, task state, streaming, and filesystem transfer. You write the agent, deploy it, and get a persistent `/workspace` plus a task API on top.

<Steps>
  <Step title="Install and log in">
    ```bash theme={"dark"}
    uv tool install terminaluse
    ```

    <Note>
      If `tu` is not found, run `uv tool update-shell` and restart your shell once.
    </Note>

    ```bash theme={"dark"}
    tu login
    ```
  </Step>

  <Step title="Scaffold an agent">
    ```bash theme={"dark"}
    tu init
    ```

    `tu init` creates a Python agent project with `config.yaml`, `Dockerfile`, and `src/agent.py`.
  </Step>

  <Step title="Replace src/agent.py">
    ```python theme={"dark"}
    from typing import Any

    from terminaluse.lib import AgentServer, TaskContext
    from terminaluse.types import Event, TextPart

    server = AgentServer()


    @server.on_create
    async def handle_create(ctx: TaskContext, params: dict[str, Any]):
        await ctx.state.create({"turns": 0})
        await ctx.messages.send("Task created. Send me a message and I will write it to /workspace/output.txt.")


    @server.on_event
    async def handle_event(ctx: TaskContext, event: Event):
        if not isinstance(event.content, TextPart):
            await ctx.messages.send("Only text events are supported in this quickstart.")
            return

        state = await ctx.state.get() or {"turns": 0}
        turns = int(state.get("turns", 0)) + 1
        await ctx.state.update({"turns": turns})

        with open("/workspace/output.txt", "a") as f:
            f.write(f"turn {turns}: {event.content.text}\n")

        await ctx.messages.send(f"Wrote turn {turns} to /workspace/output.txt")


    @server.on_cancel
    async def handle_cancel(ctx: TaskContext):
        await ctx.messages.send("Task cancelled.")
    ```
  </Step>

  <Step title="Deploy the agent">
    ```bash theme={"dark"}
    tu deploy -y
    ```
  </Step>

  <Step title="Create a project">
    ```bash theme={"dark"}
    tu projects create --namespace <your-namespace> --name quickstart
    ```

    Copy the returned project ID.
  </Step>

  <Step title="Create a task and send the first message">
    ```bash theme={"dark"}
    tu tasks create \
      -a <your-namespace>/<your-agent> \
      -p <project-id> \
      -m "hello from the quickstart"
    ```

    Using `-p` auto-creates a filesystem for the task. If you already have a filesystem, use `-f <filesystem-id>` instead.
  </Step>

  <Step title="Continue the task">
    ```bash theme={"dark"}
    tu tasks send <task-id> -m "write one more line"
    ```
  </Step>

  <Step title="Pull the task output">
    ```bash theme={"dark"}
    tu tasks pull <task-id> --out ./quickstart-output
    ```

    Your task filesystem will be under `./quickstart-output/workspace`.
  </Step>
</Steps>

## What You Just Used

| Command              | Purpose                                           |
| -------------------- | ------------------------------------------------- |
| `tu init`            | Scaffold an agent project                         |
| `tu deploy`          | Build and deploy a new version                    |
| `tu projects create` | Create a permission boundary for filesystems      |
| `tu tasks create`    | Start a task                                      |
| `tu tasks send`      | Send another event to an existing task            |
| `tu tasks pull`      | Download the task's filesystem and system folders |

## Next Steps

* Runtime model: [Building Agents](/introduction/building-agents)
* App integration: [Using Agents](/introduction/using-agents)
* Deployment model: [Deploying Agents](/introduction/deploying)

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