---
title: 'Tutorial: Hello World'
path: tutorials/hello-world
status: published
---

# Tutorial: Hello World

Build, deploy, and call the simplest possible ScaiFlow flow — an API trigger that echoes its input back via a flexible LLM step.

## What you'll end up with

A two-node flow:

```mermaid
flowchart LR
  A["API Entry"] -->|sequential| B["Flexible Prompt"]
```

Deployed as a Core; callable via `POST /v1/modules/scaicore/cores/{id}/invoke`.

## Steps

### 1. New flow

In the canvas toolbar, click **New**. Name it `Echo Agent` in the input.

### 2. Drag the entry

From the left palette **Entry points** section, drag **API Entry** onto the canvas. Click it. In the property panel, leave the defaults: `method: POST`, `path: /`.

### 3. Drag the LLM block

From **LLM blocks**, drag **Flexible Prompt** to the right of the entry node.

### 4. Wire them

Drag from the entry's `out_request` port (right edge) to the Flexible Prompt's `in_message` port (left edge). The canvas creates a sequential edge.

### 5. Configure the prompt

Click the Flexible Prompt node. In the property panel:

- **Label**: `Echo` (optional, makes the node easier to read).
- **Goal**: `Echo the user's message back verbatim.`
- **Model role**: `primary` (default).
- **Output schema**: leave `{}`.

### 6. Configure the model registry

If you haven't already, click empty canvas to deselect, then in the Flow properties panel under **Models**, the registry has one row (`primary`). The model picker reads from your tenant's ScaiGrid catalog. Pick a chat-capable model (e.g. `openai/gpt-4o`) or accept the default.

### 7. Save

Click **Save** in the toolbar. The flow is persisted to your tenant; it now appears in the My Flows sidebar.

### 8. Deploy

Click **Deploy**. A toast confirms `Deployed core_id=core_<id>`. Copy that ID.

### 9. Invoke

```bash
CORE_ID="core_<your-id>"
SCAIGRID_TOKEN="..."

curl -X POST "https://scaigrid.scailabs.ai/v1/modules/scaicore/cores/${CORE_ID}/invoke" \
  -H "Authorization: Bearer ${SCAIGRID_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{"input": {"message": "hello there"}}'
```

Response:

```json
{
  "output": { "response": "hello there" },
  "request_id": "req_...",
  "duration_ms": 412
}
```

## What you have now

A deployed Core named `echo-agent` under your tenant slug. Its compiled YAML is shown in the canvas's Live Preview pane (right sidebar, bottom) — you can see the `api` trigger declaration and the single `llm_turn` step.

In the canvas's Live Runs panel you should see one `invocation.completed` event corresponding to your `curl`.

## Variations

### Make it a chat model

In the Flow properties panel, check **Publish as chat model**. Save + Deploy. The Core is now reachable via `/v1/chat/completions`:

```bash
curl -X POST "https://scaigrid.scailabs.ai/v1/chat/completions" \
  -H "Authorization: Bearer ${SCAIGRID_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "scaicore/'"$TENANT"'/echo-agent",
    "messages": [{"role": "user", "content": "hello there"}]
  }'
```

### Make it deterministic

Replace the Flexible Prompt with a **Strict Prompt** (Rigid). Set its template to `Echo: {{message}}`. Same shape; no LLM call. Faster, cheaper, deterministic.

## Next

- **[Customer support flow](./customer-support-flow)** — adds branching, plugins, and HITL.
- **[Multi-model flow](./multi-model-flow)** — different roles per LLM block.