# LM Studio (Local)

LM Studio provides local model execution with an OpenAI-compatible API and a GUI model manager.

## Requirements

* LM Studio installed.
* A model downloaded and loaded.

## Setup

1. Install LM Studio from [lmstudio.ai](https://lmstudio.ai).
2. Load a model.
3. Start local server (`Developer -> Start Server`).
4. Configure in **AI Backend** settings tab.

## Configuration

| Setting                      | Default                 | Description                         |
| ---------------------------- | ----------------------- | ----------------------------------- |
| **Preferred Backend**        | `LM Studio`             | Select backend.                     |
| **LM Studio URL**            | `http://127.0.0.1:1234` | Server API base URL.                |
| **LM Studio Model**          | `lmstudio`              | Model identifier.                   |
| **LM Studio API Key**        | *(empty)*               | Optional bearer token.              |
| **LM Studio Headers**        | *(empty)*               | Extra headers (`Header: value`).    |
| **LM Studio Auto-Start**     | On                      | Start server command automatically. |
| **LM Studio Server Command** | `lms server start`      | Auto-start command.                 |
| **LM Studio Timeout**        | `120`                   | Request timeout in seconds.         |

## Notes

Use Auto-Start only if your LM Studio server command is stable in the same runtime environment as Burp.

## Output Token Limits

The extension sets `max_tokens` automatically per request type to ensure complete responses:

| Request Type                 | `max_tokens` |
| ---------------------------- | ------------ |
| **Chat**                     | 4096         |
| **Scanner (single request)** | 2048         |
| **Scanner (batch analysis)** | 4096         |
| **Payload generation**       | 1024         |

## Troubleshooting

{% hint style="info" %}

* Connection refused: verify server is running and URL/port match.
* Model not found: confirm model ID from LM Studio server logs.
* Timeouts: increase timeout or use smaller model.
* Slow responses: local hardware constraints are expected on CPU-only setups.
  {% endhint %}

## Retry Behavior

If a request fails due to a transient network error, the extension retries automatically up to 6 attempts using a bounded stepped backoff (500/1000/1500/2000/3000/4000 ms). A circuit breaker opens after 5 consecutive failures and resets after 30 s. Each retry is logged in the [AI Request Logger](/privacy-and-logging/ai-request-logger.md). See [Backends Overview → Retry Behavior](/backends/overview.md#retry-behavior).

## Related Pages

* [Backends Overview](/backends/overview.md)
* [Troubleshooting](/reference/troubleshooting.md)


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