MemGPT/letta/offline_memory_agent.py

174 lines
7.5 KiB
Python

from typing import List, Optional, Union
from letta.agent import Agent, AgentState, save_agent
from letta.interface import AgentInterface
from letta.orm import User
from letta.schemas.message import Message
from letta.schemas.openai.chat_completion_response import UsageStatistics
from letta.schemas.usage import LettaUsageStatistics
def trigger_rethink_memory(agent_state: "AgentState", message: Optional[str]) -> Optional[str]: # type: ignore
"""
Called if and only when user says the word trigger_rethink_memory". It will trigger the re-evaluation of the memory.
Args:
message (Optional[str]): Description of what aspect of the memory should be re-evaluated.
"""
from letta import create_client
client = create_client()
agents = client.list_agents()
for agent in agents:
if agent.agent_type == "offline_memory_agent":
client.user_message(agent_id=agent.id, message=message)
def trigger_rethink_memory_convo(agent_state: "AgentState", message: Optional[str]) -> Optional[str]: # type: ignore
"""
Called if and only when user says the word "trigger_rethink_memory". It will trigger the re-evaluation of the memory.
Args:
message (Optional[str]): Description of what aspect of the memory should be re-evaluated.
"""
from letta import create_client
client = create_client()
recent_convo = "".join([str(message) for message in agent_state.messages])[
-2000:
] # TODO: make a better representation of the convo history
agent_state.memory.update_block_value(label="conversation_block", value=recent_convo)
client = create_client()
agents = client.list_agents()
for agent in agents:
if agent.agent_type == "offline_memory_agent":
client.user_message(agent_id=agent.id, message=message)
def rethink_memory_convo(agent_state: "AgentState", new_memory: str, target_block_label: Optional[str], source_block_label: Optional[str]) -> Optional[str]: # type: ignore
"""
Re-evaluate the memory in block_name, integrating new and updated facts. Replace outdated information with the most likely truths, avoiding redundancy with original memories. Ensure consistency with other memory blocks.
Args:
new_memory (str): The new memory with information integrated from the memory block. If there is no new information, then this should be the same as the content in the source block.
source_block_label (str): The name of the block to integrate information from. None if all the information has been integrated to terminate the loop. This can by any block.
target_block_label (str): The name of the block to write to. This should be chat_agent_human_new or chat_agent_persona_new.
Returns:
Optional[str]: None is always returned as this function does not produce a response.
"""
if target_block_label is not None:
if agent_state.memory.get_block(target_block_label) is None:
agent_state.memory.create_block(label=target_block_label, value=new_memory)
agent_state.memory.update_block_value(label=target_block_label, value=new_memory)
return None
def rethink_memory(agent_state: "AgentState", new_memory: str, target_block_label: Optional[str], source_block_label: Optional[str]) -> Optional[str]: # type: ignore
"""
Re-evaluate the memory in block_name, integrating new and updated facts.
Replace outdated information with the most likely truths, avoiding redundancy with original memories.
Ensure consistency with other memory blocks.
Args:
new_memory (str): The new memory with information integrated from the memory block. If there is no new information, then this should be the same as the content in the source block.
source_block_label (str): The name of the block to integrate information from. None if all the information has been integrated to terminate the loop.
target_block_label (str): The name of the block to write to.
Returns:
Optional[str]: None is always returned as this function does not produce a response.
"""
if target_block_label is not None:
if agent_state.memory.get_block(target_block_label) is None:
agent_state.memory.create_block(label=target_block_label, value=new_memory)
agent_state.memory.update_block_value(label=target_block_label, value=new_memory)
return None
def finish_rethinking_memory(agent_state: "AgentState") -> Optional[str]: # type: ignore
"""
This function is called when the agent is done rethinking the memory.
Returns:
Optional[str]: None is always returned as this function does not produce a response.
"""
return None
def finish_rethinking_memory_convo(agent_state: "AgentState") -> Optional[str]: # type: ignore
"""
This function is called when the agent is done rethinking the memory.
Returns:
Optional[str]: None is always returned as this function does not produce a response.
"""
from letta import create_client
client = create_client()
agents = client.list_agents()
agent_state.memory.update_block_value("chat_agent_human", agent_state.memory.get_block("chat_agent_human_new").value)
agent_state.memory.update_block_value("chat_agent_persona", agent_state.memory.get_block("chat_agent_persona_new").value)
for agent in agents:
if agent.name == "conversation_agent":
agent.memory.update_block_value(label="chat_agent_human", value=agent_state.memory.get_block("chat_agent_human_new").value)
agent.memory.update_block_value(label="chat_agent_persona", value=agent_state.memory.get_block("chat_agent_persona_new").value)
return None
class OfflineMemoryAgent(Agent):
def __init__(
self,
interface: AgentInterface,
agent_state: AgentState,
user: User = None,
# extras
first_message_verify_mono: bool = False,
max_memory_rethinks: int = 10,
):
super().__init__(interface, agent_state, user)
self.first_message_verify_mono = first_message_verify_mono
self.max_memory_rethinks = max_memory_rethinks
def step(
self,
messages: Union[Message, List[Message]],
chaining: bool = True,
max_chaining_steps: Optional[int] = None,
**kwargs,
) -> LettaUsageStatistics:
"""Go through what is currently in memory core memory and integrate information."""
next_input_message = messages if isinstance(messages, list) else [messages]
counter = 0
total_usage = UsageStatistics()
step_count = 0
while counter < self.max_memory_rethinks:
if counter > 0:
next_input_message = []
kwargs["first_message"] = False
step_response = self.inner_step(
messages=next_input_message,
**kwargs,
)
for message in step_response.messages:
if message.tool_calls:
for tool_call in message.tool_calls:
# check if the function name is "finish_rethinking_memory"
if tool_call.function.name == "finish_rethinking_memory":
counter = self.max_memory_rethinks
break
usage = step_response.usage
step_count += 1
total_usage += usage
counter += 1
self.interface.step_complete()
save_agent(self)
return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count)