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

# Mainly LLM

> Out of the box LLM models

<img src="https://mintcdn.com/mainlyai/FJUQ4hjdYyeHcxxE/images/core_concepts/mainlyllm/header.png?fit=max&auto=format&n=FJUQ4hjdYyeHcxxE&q=85&s=3ccff9d7e30634a1985423453f18ecd6" alt="Mainly LLM" width="3204" height="1250" data-path="images/core_concepts/mainlyllm/header.png" />

The `llm.generate_text` node will default to our LLM proxy, which bills usage directly from the Credits included in your subscription to the MainlyAI Platform. This also means that you don't have to manually set up API keys or other credentials. To use your own models, simply connect them to the `llm` reciever.

## Using in your own nodes

The Mainly LLM proxy can also be used directly in your own nodes without any additional setup.

### Simple Example

```py theme={null}
from mirmod import llm
...
@wob.execute()
async def execute(self):
  messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What's 9+10?"},
    {"role": "assistant", "content": "21"},
    {"role": "user", "content": "Are you sure???"},
  ]

  model = llm("openai/gpt-5.2")
    .raw_response() # whether to return raw dicts or classes

  response = await model.send(messages) # returns a list of new messages
```

### Configuring the model

```py theme={null}
from mirmod.miranda_llm import LLMReasoningEffort

model = llm("openai/gpt-5.2")
  .raw_response() # whether to return raw dicts or classes
  .model_parameters({
    "max_tokens": 100,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 40,
    "parallel_tool_calls": True,
    "reasoning_effort": LLMReasoningEffort.MIMIMAL
  }) # all of these are optional
```

Looking for more models? Let us know by emailing us at [contact@mainly.ai](mailto:contact@mainly.ai)!