ConversationStringBufferMemory

class langchain.memory.buffer.ConversationStringBufferMemory

Bases: BaseMemory

Deprecated since version 0.3.1: Please see the migration guide at: https://python.langchain.com/docs/versions/migrating_memory/ It will be removed in None==1.0.0.

A basic memory implementation that simply stores the conversation history.

This stores the entire conversation history in memory without any additional processing.

Equivalent to ConversationBufferMemory but tailored more specifically for string-based conversations rather than chat models.

Note that additional processing may be required in some situations when the conversation history is too large to fit in the context window of the model.

param ai_prefix: str = 'AI'

Prefix to use for AI generated responses.

param buffer: str = ''

param human_prefix: str = 'Human'

param input_key: str | None = None

param output_key: str | None = None

async aclear() → None

Async clear memory contents.

Return type:

None

async aload_memory_variables(inputs: Dict[str, Any]) → Dict[str, str]

Return history buffer.

Parameters:

inputs (Dict[str, Any])

Return type:

Dict[str, str]

async asave_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None

Save context from this conversation to buffer.

Parameters:

• inputs (Dict[str, Any])

• outputs (Dict[str, str])

Return type:

None

clear() → None

Clear memory contents.

Return type:

None

load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str]

Return history buffer.

Parameters:

inputs (Dict[str, Any])

Return type:

Dict[str, str]

save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None

Save context from this conversation to buffer.

Parameters:

• inputs (Dict[str, Any])

• outputs (Dict[str, str])

Return type:

None

classmethod validate_chains(values: Dict) → Dict

Validate that return messages is not True.

Parameters:

values (Dict)

Return type:

Dict

property memory_variables: List[str]

Will always return list of memory variables. :meta private: