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

# Inference

> Select the model connection that powers the agent and configure its runtime parameters.

**Inference** selects which model connection the agent uses and lets you fine-tune its behavior with model parameters.

## Model connection

The **Model connection** dropdown lists all connections registered in [Settings → Model Connections](/platform/settings/models). Select the one this agent should use — credentials are inherited from the connection; you do not re-enter API keys here.

Each option shows the connection name, type, and model ID (e.g., **GPT-4o Production — OpenAI** with `gpt-4o` below).

Different agents in the same workspace can use different connections. Common patterns:

* Use a powerful model (e.g., Claude Sonnet, GPT-4o) for complex reasoning agents
* Use a fast, cheap model (e.g., Groq Llama, Claude Haiku) for simple classification or routing agents
* Use a custom endpoint for local development and testing

<Warning>
  If the dropdown is empty, no model connections have been configured yet. Go to [Settings → Model Connections](/platform/settings/models) and add at least one.
</Warning>

## Model parameters

Model parameters are optional key-value pairs passed to the LLM at inference time. They let you tune the model's behavior per-agent without changing the connection itself.

Common parameters:

| Parameter           | Type            | Description                                                                  |
| ------------------- | --------------- | ---------------------------------------------------------------------------- |
| `temperature`       | float (0.0–2.0) | Creativity vs. determinism. `0` = highly deterministic, `1+` = more creative |
| `max_tokens`        | integer         | Maximum number of tokens in the response                                     |
| `top_p`             | float (0.0–1.0) | Nucleus sampling. Alternative to temperature for controlling randomness      |
| `top_k`             | integer         | Limits sampling to the top K most likely tokens                              |
| `frequency_penalty` | float           | Reduces repetition of frequently used words                                  |
| `presence_penalty`  | float           | Encourages the model to introduce new topics                                 |

<Note>
  Supported parameters vary by connection type. Unsupported parameters are silently ignored by the runtime. Refer to your provider's API documentation for the full list.
</Note>

## Next steps

With a model connection selected, your agent is ready to run. From here you can optionally add:

<CardGroup cols={2}>
  <Card title="MCP Tools" icon="wrench" href="/platform/agent-creation/mcp-tools">
    Connect external tools and APIs.
  </Card>

  <Card title="Knowledge Base" icon="book" href="/platform/agent-creation/knowledge-base">
    Add document retrieval for RAG-based answers.
  </Card>
</CardGroup>