feat: Adjust ephemeral memory agent to become persisted sleeptime agent (#1943)
This commit is contained in:
Matthew Zhou
399
letta/agents/voice_sleeptime_agent.py
Normal file
399
letta/agents/voice_sleeptime_agent.py
Normal file
@@ -0,0 +1,399 @@
|
||||
import json
|
||||
import xml.etree.ElementTree as ET
|
||||
from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import openai
|
||||
|
||||
from letta.agents.base_agent import BaseAgent
|
||||
from letta.schemas.agent import AgentState
|
||||
from letta.schemas.block import BlockUpdate
|
||||
from letta.schemas.enums import MessageStreamStatus
|
||||
from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage
|
||||
from letta.schemas.letta_message_content import TextContent
|
||||
from letta.schemas.letta_response import LettaResponse
|
||||
from letta.schemas.message import Message, MessageCreate, ToolReturn
|
||||
from letta.schemas.openai.chat_completion_request import ChatCompletionRequest, Tool, UserMessage
|
||||
from letta.schemas.usage import LettaUsageStatistics
|
||||
from letta.schemas.user import User
|
||||
from letta.server.rest_api.utils import convert_in_context_letta_messages_to_openai, create_input_messages
|
||||
from letta.services.agent_manager import AgentManager
|
||||
from letta.services.block_manager import BlockManager
|
||||
from letta.services.message_manager import MessageManager
|
||||
from letta.system import package_function_response
|
||||
|
||||
|
||||
# TODO: Move this to the new Letta Agent loop
|
||||
class VoiceSleeptimeAgent(BaseAgent):
|
||||
"""
|
||||
A stateless agent that helps with offline memory computations.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
agent_id: str,
|
||||
convo_agent_state: AgentState,
|
||||
openai_client: openai.AsyncClient,
|
||||
message_manager: MessageManager,
|
||||
agent_manager: AgentManager,
|
||||
block_manager: BlockManager,
|
||||
target_block_label: str,
|
||||
message_transcripts: List[str],
|
||||
actor: User,
|
||||
):
|
||||
super().__init__(
|
||||
agent_id=agent_id,
|
||||
openai_client=openai_client,
|
||||
message_manager=message_manager,
|
||||
agent_manager=agent_manager,
|
||||
actor=actor,
|
||||
)
|
||||
|
||||
self.convo_agent_state = convo_agent_state
|
||||
self.block_manager = block_manager
|
||||
self.target_block_label = target_block_label
|
||||
self.message_transcripts = message_transcripts
|
||||
|
||||
def update_message_transcript(self, message_transcripts: List[str]):
|
||||
self.message_transcripts = message_transcripts
|
||||
|
||||
async def step(self, input_messages: List[MessageCreate], max_steps: int = 10) -> LettaResponse:
|
||||
"""
|
||||
Process the user's input message, allowing the model to call memory-related tools
|
||||
until it decides to stop and provide a final response.
|
||||
"""
|
||||
agent_state = self.agent_manager.get_agent_by_id(agent_id=self.agent_id, actor=self.actor)
|
||||
in_context_messages = create_input_messages(input_messages=input_messages, agent_id=self.agent_id, actor=self.actor)
|
||||
openai_messages = convert_in_context_letta_messages_to_openai(in_context_messages, exclude_system_messages=True)
|
||||
|
||||
# 1. Store memories
|
||||
request = self._build_openai_request(openai_messages, agent_state, tools=self._build_store_memory_tool_schemas())
|
||||
|
||||
chat_completion = await self.openai_client.chat.completions.create(**request.model_dump(exclude_unset=True))
|
||||
assistant_message = chat_completion.choices[0].message
|
||||
|
||||
# Process tool calls
|
||||
tool_call = assistant_message.tool_calls[0]
|
||||
function_name = tool_call.function.name
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
|
||||
if function_name == "store_memories":
|
||||
print("Called store_memories")
|
||||
print(function_args)
|
||||
chunks = function_args.get("chunks", [])
|
||||
results = [self.store_memory(agent_state=self.convo_agent_state, **chunk_args) for chunk_args in chunks]
|
||||
|
||||
aggregated_result = next((res for res, _ in results if res is not None), None)
|
||||
aggregated_success = all(success for _, success in results)
|
||||
|
||||
else:
|
||||
raise ValueError("Error: Unknown tool function '{function_name}'")
|
||||
|
||||
assistant_message = {
|
||||
"role": "assistant",
|
||||
"content": assistant_message.content,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": tool_call.id,
|
||||
"type": "function",
|
||||
"function": {"name": function_name, "arguments": tool_call.function.arguments},
|
||||
}
|
||||
],
|
||||
}
|
||||
openai_messages.append(assistant_message)
|
||||
in_context_messages.append(
|
||||
Message.dict_to_message(
|
||||
agent_id=self.agent_id,
|
||||
openai_message_dict=assistant_message,
|
||||
model=agent_state.llm_config.model,
|
||||
name=function_name,
|
||||
)
|
||||
)
|
||||
tool_call_message = {
|
||||
"role": "tool",
|
||||
"tool_call_id": tool_call.id,
|
||||
"content": package_function_response(was_success=aggregated_success, response_string=str(aggregated_result)),
|
||||
}
|
||||
openai_messages.append(tool_call_message)
|
||||
in_context_messages.append(
|
||||
Message.dict_to_message(
|
||||
agent_id=self.agent_id,
|
||||
openai_message_dict=tool_call_message,
|
||||
model=agent_state.llm_config.model,
|
||||
name=function_name,
|
||||
tool_returns=[ToolReturn(status="success" if aggregated_success else "error")],
|
||||
)
|
||||
)
|
||||
|
||||
# 2. Execute rethink block memory loop
|
||||
human_block_content = self.agent_manager.get_block_with_label(
|
||||
agent_id=self.agent_id, block_label=self.target_block_label, actor=self.actor
|
||||
)
|
||||
rethink_command = f"""
|
||||
Here is the current memory block created earlier:
|
||||
|
||||
### CURRENT MEMORY
|
||||
{human_block_content}
|
||||
### END CURRENT MEMORY
|
||||
|
||||
Please refine this block:
|
||||
|
||||
- Merge in any new facts and remove outdated or contradictory details.
|
||||
- Organize related information together (e.g., preferences, background, ongoing goals).
|
||||
- Add any light, supportable inferences that deepen understanding—but do not invent unsupported details.
|
||||
|
||||
Use `rethink_user_memory(new_memory)` as many times as you need to iteratively improve the text. When it’s fully polished and complete, call `finish_rethinking_memory()`.
|
||||
"""
|
||||
rethink_command = UserMessage(content=rethink_command)
|
||||
openai_messages.append(rethink_command.model_dump())
|
||||
|
||||
for _ in range(max_steps):
|
||||
request = self._build_openai_request(openai_messages, agent_state, tools=self._build_sleeptime_tools())
|
||||
chat_completion = await self.openai_client.chat.completions.create(**request.model_dump(exclude_unset=True))
|
||||
assistant_message = chat_completion.choices[0].message
|
||||
|
||||
# Process tool calls
|
||||
tool_call = assistant_message.tool_calls[0]
|
||||
function_name = tool_call.function.name
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
|
||||
if function_name == "rethink_user_memory":
|
||||
print("Called rethink_user_memory")
|
||||
print(function_args)
|
||||
result, success = self.rethink_user_memory(agent_state=agent_state, **function_args)
|
||||
elif function_name == "finish_rethinking_memory":
|
||||
print("Called finish_rethinking_memory")
|
||||
result, success = None, True
|
||||
break
|
||||
else:
|
||||
print(f"Error: Unknown tool function '{function_name}'")
|
||||
raise ValueError(f"Error: Unknown tool function '{function_name}'", False)
|
||||
assistant_message = {
|
||||
"role": "assistant",
|
||||
"content": assistant_message.content,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": tool_call.id,
|
||||
"type": "function",
|
||||
"function": {"name": function_name, "arguments": tool_call.function.arguments},
|
||||
}
|
||||
],
|
||||
}
|
||||
openai_messages.append(assistant_message)
|
||||
in_context_messages.append(
|
||||
Message.dict_to_message(
|
||||
agent_id=self.agent_id,
|
||||
openai_message_dict=assistant_message,
|
||||
model=agent_state.llm_config.model,
|
||||
name=function_name,
|
||||
)
|
||||
)
|
||||
tool_call_message = {
|
||||
"role": "tool",
|
||||
"tool_call_id": tool_call.id,
|
||||
"content": package_function_response(was_success=success, response_string=str(result)),
|
||||
}
|
||||
openai_messages.append(tool_call_message)
|
||||
in_context_messages.append(
|
||||
Message.dict_to_message(
|
||||
agent_id=self.agent_id,
|
||||
openai_message_dict=tool_call_message,
|
||||
model=agent_state.llm_config.model,
|
||||
name=function_name,
|
||||
tool_returns=[ToolReturn(status="success" if success else "error")],
|
||||
)
|
||||
)
|
||||
|
||||
# Actually save the memory:
|
||||
target_block = agent_state.memory.get_block(self.target_block_label)
|
||||
self.block_manager.update_block(block_id=target_block.id, block_update=BlockUpdate(value=target_block.value), actor=self.actor)
|
||||
|
||||
self.message_manager.create_many_messages(pydantic_msgs=in_context_messages, actor=self.actor)
|
||||
return LettaResponse(messages=[msg for m in in_context_messages for msg in m.to_letta_messages()], usage=LettaUsageStatistics())
|
||||
|
||||
def _format_messages_llm_friendly(self):
|
||||
messages = self.message_manager.list_messages_for_agent(agent_id=self.agent_id, actor=self.actor)
|
||||
|
||||
llm_friendly_messages = [f"{m.role}: {m.content[0].text}" for m in messages if m.content and isinstance(m.content[0], TextContent)]
|
||||
return "\n".join(llm_friendly_messages)
|
||||
|
||||
def _build_openai_request(self, openai_messages: List[Dict], agent_state: AgentState, tools: List[Tool]) -> ChatCompletionRequest:
|
||||
openai_request = ChatCompletionRequest(
|
||||
model=agent_state.llm_config.model, # TODO: Separate config for summarizer?
|
||||
messages=openai_messages,
|
||||
tools=tools,
|
||||
tool_choice="required",
|
||||
user=self.actor.id,
|
||||
max_completion_tokens=agent_state.llm_config.max_tokens,
|
||||
temperature=agent_state.llm_config.temperature,
|
||||
stream=False,
|
||||
)
|
||||
return openai_request
|
||||
|
||||
def _build_store_memory_tool_schemas(self) -> List[Tool]:
|
||||
"""
|
||||
Build the schemas for the three memory-related tools.
|
||||
"""
|
||||
tools = [
|
||||
Tool(
|
||||
type="function",
|
||||
function={
|
||||
"name": "store_memories",
|
||||
"description": "Archive coherent chunks of dialogue that will be evicted, preserving raw lines and a brief contextual description.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"chunks": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"start_index": {"type": "integer", "description": "Index of first line in original history."},
|
||||
"end_index": {"type": "integer", "description": "Index of last line in original history."},
|
||||
"context": {
|
||||
"type": "string",
|
||||
"description": "A high-level description providing context for why this chunk matters.",
|
||||
},
|
||||
},
|
||||
"required": ["start_index", "end_index", "context"],
|
||||
},
|
||||
}
|
||||
},
|
||||
"required": ["chunks"],
|
||||
"additionalProperties": False,
|
||||
},
|
||||
},
|
||||
),
|
||||
]
|
||||
|
||||
return tools
|
||||
|
||||
def _build_sleeptime_tools(self) -> List[Tool]:
|
||||
tools = [
|
||||
Tool(
|
||||
type="function",
|
||||
function={
|
||||
"name": "rethink_user_memory",
|
||||
"description": (
|
||||
"Rewrite memory block for the main agent, new_memory should contain all current "
|
||||
"information from the block that is not outdated or inconsistent, integrating any "
|
||||
"new information, resulting in a new memory block that is organized, readable, and "
|
||||
"comprehensive."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"new_memory": {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"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."
|
||||
),
|
||||
},
|
||||
},
|
||||
"required": ["new_memory"],
|
||||
"additionalProperties": False,
|
||||
},
|
||||
},
|
||||
),
|
||||
Tool(
|
||||
type="function",
|
||||
function={
|
||||
"name": "finish_rethinking_memory",
|
||||
"description": ("This function is called when the agent is done rethinking the memory."),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
"additionalProperties": False,
|
||||
},
|
||||
},
|
||||
),
|
||||
]
|
||||
|
||||
return tools
|
||||
|
||||
def rethink_user_memory(self, new_memory: str, agent_state: AgentState) -> Tuple[Optional[str], bool]:
|
||||
if agent_state.memory.get_block(self.target_block_label) is None:
|
||||
agent_state.memory.create_block(label=self.target_block_label, value=new_memory)
|
||||
|
||||
agent_state.memory.update_block_value(label=self.target_block_label, value=new_memory)
|
||||
return None, True
|
||||
|
||||
def store_memory(self, start_index: int, end_index: int, context: str, agent_state: AgentState) -> Tuple[Optional[str], bool]:
|
||||
"""
|
||||
Store a memory.
|
||||
"""
|
||||
try:
|
||||
messages = self.message_transcripts[start_index : end_index + 1]
|
||||
memory = self.serialize(messages, context)
|
||||
self.agent_manager.passage_manager.insert_passage(
|
||||
agent_state=agent_state,
|
||||
agent_id=agent_state.id,
|
||||
text=memory,
|
||||
actor=self.actor,
|
||||
)
|
||||
self.agent_manager.rebuild_system_prompt(agent_id=agent_state.id, actor=self.actor, force=True)
|
||||
|
||||
return None, True
|
||||
except Exception as e:
|
||||
return f"Failed to store memory given start_index {start_index} and end_index {end_index}: {e}", False
|
||||
|
||||
def serialize(self, messages: List[str], context: str) -> str:
|
||||
"""
|
||||
Produce an XML document like:
|
||||
|
||||
<memory>
|
||||
<messages>
|
||||
<message>…</message>
|
||||
<message>…</message>
|
||||
…
|
||||
</messages>
|
||||
<context>…</context>
|
||||
</memory>
|
||||
"""
|
||||
root = ET.Element("memory")
|
||||
|
||||
msgs_el = ET.SubElement(root, "messages")
|
||||
for msg in messages:
|
||||
m = ET.SubElement(msgs_el, "message")
|
||||
m.text = msg
|
||||
|
||||
sum_el = ET.SubElement(root, "context")
|
||||
sum_el.text = context
|
||||
|
||||
# ET.tostring will escape reserved chars for you
|
||||
return ET.tostring(root, encoding="unicode")
|
||||
|
||||
def deserialize(self, xml_str: str) -> Tuple[List[str], str]:
|
||||
"""
|
||||
Parse the XML back into (messages, context). Raises ValueError if tags are missing.
|
||||
"""
|
||||
try:
|
||||
root = ET.fromstring(xml_str)
|
||||
except ET.ParseError as e:
|
||||
raise ValueError(f"Invalid XML: {e}")
|
||||
|
||||
msgs_el = root.find("messages")
|
||||
if msgs_el is None:
|
||||
raise ValueError("Missing <messages> section")
|
||||
|
||||
messages = []
|
||||
for m in msgs_el.findall("message"):
|
||||
# .text may be None if empty, so coerce to empty string
|
||||
messages.append(m.text or "")
|
||||
|
||||
sum_el = root.find("context")
|
||||
if sum_el is None:
|
||||
raise ValueError("Missing <context> section")
|
||||
context = sum_el.text or ""
|
||||
|
||||
return messages, context
|
||||
|
||||
async def step_stream(
|
||||
self, input_messages: List[MessageCreate], max_steps: int = 10
|
||||
) -> AsyncGenerator[Union[LettaMessage, LegacyLettaMessage, MessageStreamStatus], None]:
|
||||
"""
|
||||
This agent is synchronous-only. If called in an async context, raise an error.
|
||||
"""
|
||||
raise NotImplementedError("VoiceSleeptimeAgent does not support async step.")
|
||||
Reference in New Issue
Block a user