# Use ScaleMax in n8n

> Run Claude, Codex, and DeepSeek models in n8n's AI Agent through one OpenAI-compatible credential — plus a starter workflow you can import.

> **Attach the model as the AI Agent's “OpenAI Chat Model” sub-node.** Do not use the standalone “OpenAI” node with a ScaleMax key: its current version has a known custom-base-URL bug (n8n issue #21651) where the credential test passes and every real request then fails with a 404 at runtime.

## The recipe

Works on n8n Cloud and any self-hosted n8n 1.x with the AI nodes available.

1. Add an **AI Agent** node. n8n inserts the **Chat Trigger** (“When chat message received”) automatically; keep that wiring.
2. Click the agent's **Chat Model** socket and choose **OpenAI Chat Model**.
3. In the model node, open **Credential to connect with → Create new credential**, name it `ScaleMax API`, and fill exactly two fields:

   - **API Key:** your full ScaleMax key
   - **Base URL:** `https://api.scalemax.pro/v1` for an API-credit key, or `https://api.scalemax.pro/token/v1` for a token-pack key — the two are not interchangeable. Leave **Organization ID** empty.
4. Switch the **Model** selector from **From list** to **By ID** and enter one exact model id, for example `gpt-5.6-luna`.
5. Open the chat panel and send a message.

## Three setup mistakes to avoid

### 1\. The wrong OpenAI node

n8n has two ways to call an OpenAI-compatible API, and only one of them respects a custom base URL reliably. The **OpenAI Chat Model** sub-node under the AI Agent (or under a Basic LLM Chain) sends standard Chat Completions and works with ScaleMax. The standalone **OpenAI** node (“Message a model”) is the one affected by the n8n issue #21651 regression: it 404s at runtime against custom gateways even after its credential check succeeds. If a workflow passes the credential test but every execution fails, check which node you used before anything else.

### 2\. The wrong base URL

The credential's Base URL must end at the version segment — n8n appends the request path itself:

- **Correct (API credit):** `https://api.scalemax.pro/v1`
- **Correct (token packs):** `https://api.scalemax.pro/token/v1`
- **Wrong:** `…/v1/chat/completions` — the path gets doubled on every call
- **Wrong:** `…/v1/v1` — a doubled version segment from pasting `/v1` onto a URL that already has it

A valid key used on the *wrong surface* — a token-pack key against the credit `/v1`, or a credit key against `/token/v1` — comes back as a generic `401 Invalid API key`. The key is not dead; it is on the wrong base URL. Match the URL to your key type — check which you have on [Authentication & key type](https://docs.scalemax.pro/docs/authentication).

### 3\. The empty model dropdown

The **From list** dropdown reflects whatever your key can call. Current n8n lists every model a custom gateway returns; older versions filtered the list client-side and could hide ScaleMax ids. Either way **By ID** is the reliable choice — type an exact id:

- `gpt-5.6-luna`
- `gpt-5.6-sol`
- `claude-opus-5`
- `claude-sonnet-5`
- `claude-haiku-4-5-20251001`

Use only ids returned by your key's authenticated catalog — see [Models](https://docs.scalemax.pro/docs/models) and [Authentication & key type](https://docs.scalemax.pro/docs/authentication).

## Claude in the same node

There is no Anthropic node to configure. Enter a `claude-*` id (for example `claude-opus-5`) in the same **OpenAI Chat Model** sub-node: ScaleMax translates OpenAI-compatible chat calls to the Anthropic upstream on the gateway side. This is also how a Claude-only key works in n8n — same credential form, same base URL rule, Claude model ids only.

## DeepSeek V4 Flash

`deepseek-v4-flash` and `glm-5.2` work through the same credential, but each needs a key scoped to that exact model — on any other key the request is rejected with a `403`. Both support tool calling in the AI Agent node.

Two things worth setting explicitly on these models, because both are reasoning models and n8n leaves them unset by default:

- **Maximum Number of Tokens.** n8n sends no limit unless you set one. Reasoning tokens are spent from the same completion budget as the answer, so a long structured response can run out of room and come back truncated — n8n reports that as *“Model output doesn’t fit required format”*. Set it to a few thousand for agent workflows.
- **Structured Output Parser.** If you attach one, keep the schema small and the prompt explicit. A reasoning model that thinks at length before answering has less budget left for the JSON itself.

## Starter workflow: import and run

Download [scalemax-n8n-starter.json](https://docs.scalemax.pro/scalemax-n8n-starter.json) or copy the block below. It is a minimal Chat Trigger → AI Agent → OpenAI Chat Model workflow with the model set to `gpt-5.6-luna`.

1. In n8n, open **Workflow → Import from File…** — or copy the JSON and paste it straight onto the canvas with `Ctrl+V` / `Cmd+V`.
2. Open the **OpenAI Chat Model** node. It references a credential named `ScaleMax API` that does not exist on your instance yet — create it there with your key and base URL, exactly as in the recipe above.
3. Open the chat panel and send `Reply with exactly OK`. A short answer confirms the key, URL, and model id in one round trip.

scalemax-n8n-starter.json:

```json
{
  "name": "ScaleMax chat starter",
  "nodes": [
    {
      "parameters": {
        "options": {}
      },
      "id": "81a4277f-068f-4e25-95d6-0c35e9fe2202",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "typeVersion": 1.1,
      "position": [
        0,
        0
      ],
      "webhookId": "83a13d6a-e9e6-4955-8d15-72f36a6b6a37"
    },
    {
      "parameters": {
        "options": {}
      },
      "id": "25811587-54ec-466b-824d-ff9ffb216116",
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "typeVersion": 1.7,
      "position": [
        220,
        0
      ]
    },
    {
      "parameters": {
        "model": {
          "__rl": true,
          "value": "gpt-5.6-luna",
          "mode": "id"
        },
        "options": {}
      },
      "id": "f097ebef-1c9d-4480-86bf-ca536d991f14",
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "typeVersion": 1.2,
      "position": [
        200,
        220
      ],
      "credentials": {
        "openAiApi": {
          "id": "scalemax-api-credential",
          "name": "ScaleMax API"
        }
      }
    }
  ],
  "connections": {
    "When chat message received": {
      "main": [
        [
          {
            "node": "AI Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    }
  },
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "meta": {
    "templateCredsSetupCompleted": false
  }
}
```

## Fallback: the HTTP Request node

If you want raw API responses, are running DeepSeek without an agent, or the AI nodes are unavailable on your instance, call the endpoint directly:

- **Method:** `POST`
- **URL:** `https://api.scalemax.pro/v1/chat/completions` (token-pack keys: `https://api.scalemax.pro/token/v1/chat/completions`)
- **Authentication:** Generic Credential Type → **Header Auth**, with Name `Authorization` and Value `Bearer sm_live_...` — the word `Bearer`, one space, then the key
- **Send Body:** on, as JSON:

request-body.json:

```json
{
  "model": "gpt-5.6-luna",
  "messages": [{ "role": "user", "content": "Reply with exactly OK" }],
  "max_tokens": 16
}
```

A Header Auth credential keeps the key inside n8n's credential store, so it never appears in a shared or exported workflow.

## Verify it works

Run the same request from a terminal to separate key problems from n8n problems:

**macOS / Linux**

verify.sh:

```bash
export SCALEMAX_API_KEY="sm_live_..."

curl "https://api.scalemax.pro/v1/chat/completions" \
  -H "Authorization: Bearer $SCALEMAX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.6-luna",
    "messages": [{"role": "user", "content": "Reply with exactly OK"}],
    "max_tokens": 16
  }'
```

**Windows PowerShell**

verify.ps1:

```powershell
$env:SCALEMAX_API_KEY = "sm_live_..."

@'
{
  "model": "gpt-5.6-luna",
  "messages": [{"role": "user", "content": "Reply with exactly OK"}],
  "max_tokens": 16
}
'@ | Set-Content -Path body.json

curl.exe "https://api.scalemax.pro/v1/chat/completions" `
  -H "Authorization: Bearer $env:SCALEMAX_API_KEY" `
  -H "Content-Type: application/json" `
  --data-binary "@body.json"
```

PowerShell changes quotes inside a JSON argument, so the request body goes into body.json first and curl.exe sends that file. Type curl.exe, not curl: in Windows PowerShell 5.1, curl is a different command.

A JSON response whose message content is `OK` proves the key, URL, and model. If this curl succeeds but n8n fails, the fault is in the node choice or credential fields above — not the key. If the curl itself fails, start with [Error Codes](https://docs.scalemax.pro/docs/error-codes).

> Workflow exports never contain the key: n8n stores credentials separately, and the JSON references them by name and id — never the secret. Keep it that way — put the key in the OpenAI or Header Auth credential, never inline in a node field, and never reuse one key across the credit and token-pack URLs.