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

# Configuration API

> Reference for managing runtime resources and memory settings

The `client.config` namespace provides programmatic access to the system's underlying resources and memory behavior. Use this to manage model providers, vector stores, and the specific toggles for Episodic and Semantic subsystems.

## System Configuration

### `get_full_config`

Retrieves the entire current configuration of the MemMachine server, including active resources and default settings.

```python theme={null}
config = client.config.get_full_config()
print(config["episodic_memory"]["enabled"])
```

***

## Memory Subsystem Updates

These methods allow you to modify how memory behaves without restarting the server. Only the fields you provide will be updated.

### `update_episodic`

Configures the behavior of the episodic memory stream.

| **Parameter** | **Type** | **Description**                                                  |
| ------------- | -------- | ---------------------------------------------------------------- |
| `enabled`     | `bool`   | Master toggle for all episodic operations.                       |
| `long_term`   | `bool`   | Enable/disable vector-based retrieval for older memories.        |
| `short_term`  | `bool`   | Enable/disable the sliding-window buffer of recent interactions. |

### `update_long_term`

Updates the infrastructure used for long-term storage.

```python theme={null}
client.config.update_long_term(
    vector_store="chroma",
    embedder="openai-small",
    reranker="cohere-v3"
)
```

| **Parameter**  | **Type** | **Description**                                              |
| -------------- | -------- | ------------------------------------------------------------ |
| `vector_store` | `str`    | The name of the configured vector store resource.            |
| `embedder`     | `str`    | The name of the configured embedder resource.                |
| `reranker`     | `str`    | (Optional) The name of a reranker for high-precision search. |

***

## Resource Management

### `add_embedder`

Registers a new embedding model provider.

```python theme={null}
client.config.add_embedder(
    name="openai-v3",
    provider="openai",
    config={"api_key": "sk-...", "model": "text-embedding-3-small"}
)
```

### `add_language_model`

Registers a new LLM provider for summarization and semantic extraction.

| **Parameter** | **Type** | **Description**                                |
| ------------- | -------- | ---------------------------------------------- |
| `name`        | `str`    | Unique identifier for this resource.           |
| `provider`    | `str`    | Provider type (e.g., `"openai"`, `"bedrock"`). |
| `config`      | `dict`   | Provider-specific credentials and settings.    |

***

## Resource Health & Recovery

### `retry_reranker`

Attempts to re-initialize a reranker resource that has moved into a `FAILED` state.

```python theme={null}
response = client.config.retry_reranker(name="cohere-v3")
if response.success:
    print("Reranker is now READY")
```

### `retry_language_model`

Attempts to re-initialize a failed LLM provider.

| **Parameter** | **Type** | **Description**                          |
| ------------- | -------- | ---------------------------------------- |
| `name`        | `str`    | The name of the language model to retry. |

***

<Tip>
  Before updating a memory subsystem to use a new resource (like a new Embedder), ensure the resource has been added via `add_embedder` and is in a `READY` state.
</Tip>