> ## Documentation Index
> Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Camel.models.model manager
## ModelProcessingError
```python theme={"system"}
class ModelProcessingError(Exception):
```
Raised when an error occurs during model processing.
## ModelManager
```python theme={"system"}
class ModelManager:
```
ModelManager choosing a model from provided list.
Models are picked according to defined strategy.
**Parameters:**
* **models** (Union\[BaseModelBackend, List\[BaseModelBackend]]): model backend or list of model backends (e.g., model instances, APIs)
* **scheduling\_strategy** (str): name of function that defines how to select the next model. (default: :str:`round_robin`)
### **init**
```python theme={"system"}
def __init__(
self,
models: Union[BaseModelBackend, List[BaseModelBackend]],
scheduling_strategy: str = 'round_robin'
):
```
### model\_type
```python theme={"system"}
def model_type(self):
```
**Returns:**
Union\[ModelType, str]: Current model type.
### model\_config\_dict
```python theme={"system"}
def model_config_dict(self):
```
**Returns:**
Dict\[str, Any]: Config dictionary of the current model.
### model\_config\_dict
```python theme={"system"}
def model_config_dict(self, model_config_dict: Dict[str, Any]):
```
Set model\_config\_dict to the current model.
**Parameters:**
* **model\_config\_dict** (Dict\[str, Any]): Config dictionary to be set at current model.
### current\_model\_index
```python theme={"system"}
def current_model_index(self):
```
**Returns:**
int: index of current model in given list of models.
### num\_models
```python theme={"system"}
def num_models(self):
```
**Returns:**
int: The number of models available in the model manager.
### token\_limit
```python theme={"system"}
def token_limit(self):
```
**Returns:**
int: The maximum token limit for the given model.
### token\_counter
```python theme={"system"}
def token_counter(self):
```
**Returns:**
BaseTokenCounter: The token counter following the model's
tokenization style.
### add\_strategy
```python theme={"system"}
def add_strategy(self, name: str, strategy_fn: Callable):
```
Add a scheduling strategy method provided by user in case when none
of existent strategies fits.
When custom strategy is provided, it will be set as
"self.scheduling\_strategy" attribute.
**Parameters:**
* **name** (str): The name of the strategy.
* **strategy\_fn** (Callable): The scheduling strategy function.
### round\_robin
```python theme={"system"}
def round_robin(self):
```
**Returns:**
BaseModelBackend for processing incoming messages.
### always\_first
```python theme={"system"}
def always_first(self):
```
**Returns:**
BaseModelBackend for processing incoming messages.
### random\_model
```python theme={"system"}
def random_model(self):
```
**Returns:**
BaseModelBackend for processing incoming messages.
### run
```python theme={"system"}
def run(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None
):
```
Process a list of messages by selecting a model based on
the scheduling strategy.
Sends the entire list of messages to the selected model,
and returns a single response.
**Parameters:**
* **messages** (List\[OpenAIMessage]): Message list with the chat history in OpenAI API format.
**Returns:**
Union\[ChatCompletion, Stream\[ChatCompletionChunk],
ChatCompletionStreamManager\[BaseModel]]:
`ChatCompletion` in the non-stream mode, or
`Stream[ChatCompletionChunk]` in the stream mode, or
`ChatCompletionStreamManager[BaseModel]` for
structured-output stream.