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

# Agent Configuration

> Configure LLM model, behavior prompt, tools, and execution settings.

## Basic settings

| Field           | Description                                    | Default    |
| --------------- | ---------------------------------------------- | ---------- |
| **Name**        | Display name for the agent                     | Required   |
| **Description** | Short description of what the agent does       | Optional   |
| **Icon**        | Bootstrap icon class for visual identification | `bi-robot` |
| **Status**      | Active, Inactive, or Archived                  | Active     |

## LLM Model

Choose which language model powers your agent. Available models vary by provider:

* **Azure OpenAI** — GPT-4o, GPT-4o Mini
* **OpenAI** — GPT-4o, GPT-4 Turbo, o1, o3
* **Anthropic** — Claude Sonnet, Claude Haiku, Claude Opus

Each model has different capabilities, context windows, and credit costs. See [Billing & Credits](/billing/credits) for pricing details.

## Behavior Prompt

The behavior prompt is the **system message** sent to the LLM. It defines the agent's personality, instructions, and constraints. Write it as if you're briefing a human assistant.

```text theme={null}
You are an expert data analyst. When given a dataset description:
1. Identify key patterns and anomalies
2. Suggest relevant visualizations
3. Provide actionable insights

Always cite specific data points in your analysis.
Be concise — limit responses to 500 words.
```

<Tip>
  Be specific about output format, constraints, and edge cases. The more precise your prompt, the more consistent the agent's behavior.
</Tip>

## Execution settings

| Setting                 | Description                                            | Default  |
| ----------------------- | ------------------------------------------------------ | -------- |
| **Max Steps**           | Maximum tool-call iterations per run (1–12)            | 8        |
| **Conversation Memory** | Include previous messages from the same conversation   | Enabled  |
| **Auto Tool Selection** | Let a ToolSelector system agent dynamically pick tools | Disabled |

### Max Steps

Each "step" is one LLM call that results in a tool invocation. If the agent reaches the step limit without finishing, it returns whatever output it has so far. Higher limits allow more complex multi-step reasoning but consume more credits.

### Conversation Memory

When enabled, the agent receives the full conversation history with each new message. This enables multi-turn interactions where the agent remembers context. Disable it for stateless, one-shot tasks to save tokens.

## Knowledge Base

Attach a **Knowledge Base** to give the agent access to your documents. The agent automatically gets a `SearchKnowledge` tool that performs semantic search over uploaded documents.

## Attached Tools

Add tools that the agent can call during execution. These can be:

* **Built-in functions** — platform-provided utilities
* **Custom tools** — workflow automations you've built
* **Integration tools** — functions from connected services
* **Other agents** — invoke another agent as a sub-task