GenerativeAgentMemory

class langchain_experimental.generative_agents.memory.GenerativeAgentMemory

Bases: BaseMemory

Memory for the generative agent.

param add_memory_key: str = 'add_memory'

param aggregate_importance: float = 0.0

Track the sum of the ‘importance’ of recent memories.

Triggers reflection when it reaches reflection_threshold.

param current_plan: List[str] = []

The current plan of the agent.

param importance_weight: float = 0.15

How much weight to assign the memory importance.

param llm: BaseLanguageModel [Required]

The core language model.

param max_tokens_limit: int = 1200

param memory_retriever: TimeWeightedVectorStoreRetriever [Required]

The retriever to fetch related memories.

param most_recent_memories_key: str = 'most_recent_memories'

param most_recent_memories_token_key: str = 'recent_memories_token'

param now_key: str = 'now'

param queries_key: str = 'queries'

param reflecting: bool = False

param reflection_threshold: float | None = None

When aggregate_importance exceeds reflection_threshold, stop to reflect.

param relevant_memories_key: str = 'relevant_memories'

param relevant_memories_simple_key: str = 'relevant_memories_simple'

param verbose: bool = False

async aclear() → None

Async clear memory contents.

Return type:

None

add_memories(memory_content: str, now: datetime | None = None) → List[str]

Add an observations or memories to the agent’s memory.

Parameters:

• memory_content (str) –

• now (datetime | None) –

Return type:

List[str]

add_memory(memory_content: str, now: datetime | None = None) → List[str]

Add an observation or memory to the agent’s memory.

Parameters:

• memory_content (str) –

• now (datetime | None) –

Return type:

List[str]

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

Async return key-value pairs given the text input to the chain.

Parameters:

inputs (Dict[str, Any]) – The inputs to the chain.

Returns:

A dictionary of key-value pairs.

Return type:

Dict[str, Any]

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

Async save the context of this chain run to memory.

Parameters:

• inputs (Dict[str, Any]) – The inputs to the chain.

• outputs (Dict[str, str]) – The outputs of the chain.

Return type:

None

chain(prompt: PromptTemplate) → LLMChain

Parameters:

prompt (PromptTemplate) –

Return type:

LLMChain

clear() → None

Clear memory contents.

Return type:

None

fetch_memories(observation: str, now: datetime | None = None) → List[Document]

Fetch related memories.

Parameters:

• observation (str) –

• now (datetime | None) –

Return type:

List[Document]

format_memories_detail(relevant_memories: List[Document]) → str

Parameters:

relevant_memories (List[Document]) –

Return type:

str

format_memories_simple(relevant_memories: List[Document]) → str

Parameters:

relevant_memories (List[Document]) –

Return type:

str

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

Return key-value pairs given the text input to the chain.

Parameters:

inputs (Dict[str, Any]) –

Return type:

Dict[str, str]

pause_to_reflect(now: datetime | None = None) → List[str]

Reflect on recent observations and generate ‘insights’.

Parameters:

now (datetime | None) –

Return type:

List[str]

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

Save the context of this model run to memory.

Parameters:

• inputs (Dict[str, Any]) –

• outputs (Dict[str, Any]) –

Return type:

None

property memory_variables: List[str]

Input keys this memory class will load dynamically.