Use Dolphin LLM in n8n workflows
Updated

n8n can call any OpenAI-compatible API. With one credential you get Dolphin LLM in the AI Agent, Basic LLM Chain and other LangChain nodes, or in a plain HTTP Request node.
Key points
- Create an OpenAI credential and set its Base URL to the address below.
- In the OpenAI Chat Model node, set the model by ID to
uncensored. - Turn on Retry On Fail for 429 and 503 answers, which are never charged.
- Batch large loops: a trial key allows 2 parallel requests, a full key 8.
Create the credential
- In n8n open Credentials, click Add credential and choose OpenAI.
- Paste your key into API Key. Leave Organization ID empty.
- Set Base URL to
https://dolphinllm.com/v1. - Save. n8n tests the credential by asking for the model list; a success message means the key and URL work.
Create the key in your account. If you have none yet, sign up; $0.50 of trial credit is added when you first open the account.

Use it in an AI Agent or LLM chain
- Add an AI Agent or Basic LLM Chain node.
- Under Chat Model, add OpenAI Chat Model and pick the credential you created.
- Set Model to By ID and enter
uncensored(or pick it from the list if n8n shows it). - In Options, set Maximum Number of Tokens (for example 800), Sampling Temperature and a Timeout of 120 seconds or more for long answers.
The API supports tool calling, so the AI Agent node can use n8n tools with this model. JSON mode (response_format: json_object) is available when you need structured output for the next node.
Or call it with an HTTP Request node
For full control over the request body, use a plain HTTP Request node:
- Method POST, URL
https://dolphinllm.com/v1/chat/completions - Authentication: Generic Credential Type → Header Auth, name
Authorization, valueBearer YOUR_KEY - Body content type JSON:
{
"model": "uncensored",
"messages": [
{"role": "system", "content": "Summarize the text in three bullet points."},
{"role": "user", "content": "{{ $json.text }}"}
],
"max_tokens": 400,
"temperature": 0.3
}
The answer is in choices[0].message.content, and the token counts in usage.
Limits, retries and batches
- A trial key allows 2 parallel requests and 60 a minute; after the first top-up, 8 parallel and 300 a minute. Workflows that process many items at once can hit HTTP 429 (
concurrencyorrate_limited). - Use Loop Over Items with a batch size of 2 to 8, or the node's batching options, to stay under the limit.
- Turn on Retry On Fail in the node settings with a wait of a few seconds. 429 and 503 (
upstream_busy) answers are safe to retry and are never charged. - 402
no_creditmeans the balance is empty; retrying will not help. Add an error branch that notifies you.
Cost
A workflow that summarizes 1,000 articles a day at 2,500 tokens in and 300 out spends 1,000 × ($0.000375 + $0.00015) = $0.525 a day, about $16 a month. The pricing page has the calculator and the top-up bonuses.
Questions and answers
Which n8n credential do I use?
The built-in OpenAI credential, with Base URL set to https://dolphinllm.com/v1.
What model do I enter?
uncensored, the only model on this API.
Why do some items fail with 429?
Too many parallel requests for your key. Batch the items and turn on Retry On Fail; retried 429 answers cost nothing.
More guides
Use Dolphin LLM in Continue for VS Code and JetBrainsConfigure Continue (VS Code, JetBrains) to chat and edit code with the Dolphin LLM API: config.yaml model block, roles, context length, keeping the key out of the file, and costs.
Free LLM API for Developers: Trial vs Pay as You GoWhat a free LLM API tier gives you and where it stops. A $0.50 no-card trial, then cents per call: what it buys, how to make it last and what to check before you commit.
Dolphin LLM API: Local, Rented GPU or HostedThree ways to get an API for a Dolphin-style uncensored model: run it locally, serve it on a rented GPU, or call a hosted OpenAI-compatible endpoint. Effort and cost compared.