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