> ## 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.memories.context creators.score based
## ScoreBasedContextCreator
```python theme={"system"}
class ScoreBasedContextCreator(BaseContextCreator):
```
A context creation strategy that orders records chronologically.
This class supports token count estimation to reduce expensive repeated
token counting. When a cached token count is available, it estimates
new message tokens using character-based approximation instead of
calling the token counter for every message.
**Parameters:**
* **token\_counter** (BaseTokenCounter): Token counter instance used to compute the combined token count of the returned messages.
* **token\_limit** (int): Retained for API compatibility. No longer used to filter records.
### **init**
```python theme={"system"}
def __init__(self, token_counter: BaseTokenCounter, token_limit: int):
```
### token\_counter
```python theme={"system"}
def token_counter(self):
```
### token\_limit
```python theme={"system"}
def token_limit(self):
```
### set\_cached\_token\_count
```python theme={"system"}
def set_cached_token_count(self, token_count: int, message_count: int):
```
Set the cached token count from LLM response usage.
**Parameters:**
* **token\_count** (int): The total token count (prompt + completion) from LLM response usage.
* **message\_count** (int): The number of messages including the assistant response that will be added to memory.
### clear\_cache
```python theme={"system"}
def clear_cache(self):
```
Clear the cached token count.
### \_estimate\_message\_tokens
```python theme={"system"}
def _estimate_message_tokens(self, message: OpenAIMessage):
```
Estimate token count for a single message.
Uses \~2 chars/token as a conservative approximation to handle both
ASCII (\~4 chars/token) and CJK text (\~1-2 chars/token).
**Parameters:**
* **message**: The OpenAI message to estimate.
**Returns:**
Estimated token count (intentionally conservative).
### create\_context
```python theme={"system"}
def create_context(self, records: List[ContextRecord]):
```
Returns messages sorted by timestamp and their total token count.