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

# Gateway

> Gateway API reference

The central orchestrator for all LLM calls.

### Constructor

```python theme={null}
Gateway(
    limits: ExecutionLimits | None = None,
    loop_detection: LoopDetectionConfig | None = None,
    tool_access: ToolAccessController | None = None,
    checkpoint_enabled: bool = False,
    on_before_call: Callable | None = None,
    on_after_call: Callable | None = None,
)
```

| Parameter            | Type                   | Description             |
| -------------------- | ---------------------- | ----------------------- |
| `limits`             | `ExecutionLimits`      | Budget/step/time limits |
| `loop_detection`     | `LoopDetectionConfig`  | Loop detection config   |
| `tool_access`        | `ToolAccessController` | Tool access rules       |
| `checkpoint_enabled` | `bool`                 | Enable checkpointing    |
| `on_before_call`     | `Callable`             | Hook before each call   |
| `on_after_call`      | `Callable`             | Hook after each call    |

### Methods

#### configure\_provider

```python theme={null}
gateway.configure_provider(
    provider: str | LLMProvider,
    api_key: str | None = None,
    **kwargs
) -> Gateway
```

Configure an LLM provider.

#### call

```python theme={null}
await gateway.call(
    agent_id: str,
    provider: str | LLMProvider,
    model: str,
    messages: list[LLMMessage],
    tools: list[dict] | None = None,
    temperature: float | None = None,
    max_tokens: int | None = None,
    state: SharedState | dict | None = None,
    workflow_id: str | None = None,
    step: int | None = None,
) -> LLMResponse
```

Make an LLM call through the gateway.

#### call\_with\_tools

```python theme={null}
await gateway.call_with_tools(
    agent_id: str,
    provider: str | LLMProvider,
    model: str,
    messages: list[LLMMessage],
    tools: list[dict],
    tool_executor: Callable,
    max_iterations: int = 10,
    state: dict | None = None,
) -> LLMResponse
```

Make an LLM call with automatic tool execution loop.

#### get\_metrics

```python theme={null}
gateway.get_metrics() -> dict
```

Returns:

```python theme={null}
{
    "total_cost": 0.05,
    "total_steps": 10,
    "total_tokens": 5000,
    "input_tokens": 3000,
    "output_tokens": 2000,
    "elapsed_seconds": 30.5,
    "remaining": {...},
    "call_count": 10,
}
```

#### get\_call\_history

```python theme={null}
gateway.get_call_history(
    agent_id: str | None = None,
    limit: int | None = None,
) -> list[CallRecord]
```

#### reset

```python theme={null}
gateway.reset() -> None
```

Reset all state, metrics, and history.

### Properties

#### metrics

```python theme={null}
gateway.metrics -> ExecutionMetrics
```

Current execution metrics.

## CallRecord

Record of a single LLM call.

### Properties

| Property     | Type          | Description              |                         |
| ------------ | ------------- | ------------------------ | ----------------------- |
| `agent_id`   | `str`         | Agent that made the call |                         |
| `provider`   | `LLMProvider` | Provider used            |                         |
| `model`      | `str`         | Model used               |                         |
| `request`    | `LLMRequest`  | The request              |                         |
| `response`   | `LLMResponse` | The response             |                         |
| `error`      | \`str         | None\`                   | Error message if failed |
| `timestamp`  | `datetime`    | When the call was made   |                         |
| `success`    | `bool`        | Whether call succeeded   |                         |
| `cost`       | `float`       | Cost of the call         |                         |
| `latency_ms` | `float`       | Latency in milliseconds  |                         |