CAMEL - Build Multi-Agent AI Systems

ScoreBasedContextCreator

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

def __init__(self, token_counter: BaseTokenCounter, token_limit: int):

token_counter

def token_counter(self):

token_limit

def token_limit(self):

set_cached_token_count

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

def clear_cache(self):

Clear the cached token count.

_estimate_message_tokens

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

def create_context(self, records: List[ContextRecord]):

Returns messages sorted by timestamp and their total token count.

​ScoreBasedContextCreator

​init

​token_counter

​token_limit

​set_cached_token_count

​clear_cache

​_estimate_message_tokens

​create_context