Short answer: You can meta llama chat completion in Meta Llama API by hand from its own interface, but it won’t repeat itself. On TinyCommand, add the Meta Llama API Meta Llama Chat Completion action to a workflow, map its 6 inputs from any upstream app, and it runs automatically every time the trigger fires. No code, and a free tier to start.
Every field can be mapped from an upstream trigger, AI step, table row, or hard-coded literal.
| Field | Type | Required | Description |
|---|---|---|---|
Model model | options | Required | Which model to use |
User Message message | string | Required | User message to send to the model |
System Prompt system_prompt | string | Optional | Optional system instructions that shape the model's behavior |
Temperature temperature | string | Optional | Sampling temperature (0–2). Higher = more random. |
Max Tokens max_tokens | string | Optional | Maximum tokens to generate in the response |
Top P top_p | string | Optional | Nucleus sampling threshold (0–1) |
{"model": "{{trigger.model}}","message": "e.g. Summarize this article in 3 bullets","system_prompt": "e.g. You are a helpful assistant.","temperature": "0.7","max_tokens": "1024"}
{"id": "chatcmpl-abc123","model": "Llama-4-Maverick-17B-128E-Instruct-FP8","usage": {"total_tokens": 60,"prompt_tokens": 10,"completion_tokens": 50},"choices": [{"message": {"role": "assistant","content": "Sample response"},"finish_reason": "stop"}]}
Use these fields in downstream nodes for routing, logging, or error handling.
Any of these apps can fire this action as part of a workflow.