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from typing import Dict, List, Any, Optional, Tuple
from copy import deepcopy
from pydantic import BaseModel, ConfigDict
from phi.memory.classifier import MemoryClassifier
from phi.memory.db import MemoryDb
from phi.memory.manager import MemoryManager
from phi.memory.memory import Memory
from phi.memory.summary import SessionSummary
from phi.memory.summarizer import MemorySummarizer
from phi.model.message import Message
from phi.run.response import RunResponse
from phi.utils.log import logger
class AgentRun(BaseModel):
message: Optional[Message] = None
messages: Optional[List[Message]] = None
response: Optional[RunResponse] = None
model_config = ConfigDict(arbitrary_types_allowed=True)
class MemoryRetrieval(str, Enum):
last_n = "last_n"
first_n = "first_n"
semantic = "semantic"
class AgentMemory(BaseModel):
# Runs between the user and agent
runs: List[AgentRun] = []
# List of messages sent to the model
messages: List[Message] = []
update_system_message_on_change: bool = False
# Create and store session summaries
create_session_summary: bool = False
# Update session summaries after each run
update_session_summary_after_run: bool = True
# Summary of the session
summary: Optional[SessionSummary] = None
# Summarizer to generate session summaries
summarizer: Optional[MemorySummarizer] = None
# Create and store personalized memories for this user
create_user_memories: bool = False
# Update memories for the user after each run
update_user_memories_after_run: bool = True
# MemoryDb to store personalized memories
db: Optional[MemoryDb] = None
# User ID for the personalized memories
user_id: Optional[str] = None
retrieval: MemoryRetrieval = MemoryRetrieval.last_n
memories: Optional[List[Memory]] = None
num_memories: Optional[int] = None
classifier: Optional[MemoryClassifier] = None
manager: Optional[MemoryManager] = None
# True when memory is being updated
updating_memory: bool = False
model_config = ConfigDict(arbitrary_types_allowed=True)
def to_dict(self) -> Dict[str, Any]:
_memory_dict = self.model_dump(
exclude_none=True,
exclude={
"summary",
"summarizer",
"db",
"updating_memory",
"memories",
"classifier",
"manager",
"retrieval",
},
)
if self.summary:
_memory_dict["summary"] = self.summary.to_dict()
if self.memories:
_memory_dict["memories"] = [memory.to_dict() for memory in self.memories]
return _memory_dict
def add_run(self, agent_run: AgentRun) -> None:
"""Adds an AgentRun to the runs list."""
self.runs.append(agent_run)
logger.debug("Added AgentRun to AgentMemory")
def add_system_message(self, message: Message, system_message_role: str = "system") -> None:
"""Add the system messages to the messages list"""
# If this is the first run in the session, add the system message to the messages list
if len(self.messages) == 0:
if message is not None:
self.messages.append(message)
# If there are messages in the memory, check if the system message is already in the memory
# If it is not, add the system message to the messages list
# If it is, update the system message if content has changed and update_system_message_on_change is True
else:
system_message_index = next((i for i, m in enumerate(self.messages) if m.role == system_message_role), None)
# Update the system message in memory if content has changed
if system_message_index is not None:
if (
self.messages[system_message_index].content != message.content
and self.update_system_message_on_change
):
logger.info("Updating system message in memory with new content")
self.messages[system_message_index] = message
else:
# Add the system message to the messages list
self.messages.insert(0, message)
def add_message(self, message: Message) -> None:
"""Add a Message to the messages list."""
self.messages.append(message)
logger.debug("Added Message to AgentMemory")
def add_messages(self, messages: List[Message]) -> None:
"""Add a list of messages to the messages list."""
self.messages.extend(messages)
logger.debug(f"Added {len(messages)} Messages to AgentMemory")
def get_messages(self) -> List[Dict[str, Any]]:
"""Returns the messages list as a list of dictionaries."""
return [message.model_dump(exclude_none=True) for message in self.messages]
def get_messages_from_last_n_runs(
self, last_n: Optional[int] = None, skip_role: Optional[str] = None
) -> List[Message]:
"""Returns the messages from the last_n runs
Args:
last_n: The number of runs to return from the end of the conversation.
skip_role: Skip messages with this role.
Returns:
A list of Messages in the last_n runs.
"""
if last_n is None:
logger.debug("Getting messages from all previous runs")
messages_from_all_history = []
for prev_run in self.runs:
if prev_run.response and prev_run.response.messages:
if skip_role:
prev_run_messages = [m for m in prev_run.response.messages if m.role != skip_role]
else:
prev_run_messages = prev_run.response.messages
messages_from_all_history.extend(prev_run_messages)
logger.debug(f"Messages from previous runs: {len(messages_from_all_history)}")
return messages_from_all_history
logger.debug(f"Getting messages from last {last_n} runs")
messages_from_last_n_history = []
for prev_run in self.runs[-last_n:]:
if prev_run.response and prev_run.response.messages:
if skip_role:
prev_run_messages = [m for m in prev_run.response.messages if m.role != skip_role]
else:
prev_run_messages = prev_run.response.messages
messages_from_last_n_history.extend(prev_run_messages)
logger.debug(f"Messages from last {last_n} runs: {len(messages_from_last_n_history)}")
return messages_from_last_n_history
def get_message_pairs(
self, user_role: str = "user", assistant_role: Optional[List[str]] = None
) -> List[Tuple[Message, Message]]:
"""Returns a list of tuples of (user message, assistant response)."""
if assistant_role is None:
assistant_role = ["assistant", "model", "CHATBOT"]
runs_as_message_pairs: List[Tuple[Message, Message]] = []
for run in self.runs:
if run.response and run.response.messages:
user_messages_from_run = None
assistant_messages_from_run = None
# Start from the beginning to look for the user message
for message in run.response.messages:
if message.role == user_role:
user_messages_from_run = message
break
# Start from the end to look for the assistant response
for message in run.response.messages[::-1]:
if message.role in assistant_role:
assistant_messages_from_run = message
break
if user_messages_from_run and assistant_messages_from_run:
runs_as_message_pairs.append((user_messages_from_run, assistant_messages_from_run))
return runs_as_message_pairs
def get_tool_calls(self, num_calls: Optional[int] = None) -> List[Dict[str, Any]]:
"""Returns a list of tool calls from the messages"""
tool_calls = []
for message in self.messages[::-1]:
if message.tool_calls:
for tool_call in message.tool_calls:
tool_calls.append(tool_call)
if num_calls and len(tool_calls) >= num_calls:
return tool_calls
return tool_calls
def load_user_memories(self) -> None:
"""Load memories from memory db for this user."""
if self.db is None:
return
try:
if self.retrieval in (MemoryRetrieval.last_n, MemoryRetrieval.first_n):
memory_rows = self.db.read_memories(
user_id=self.user_id,
limit=self.num_memories,
sort="asc" if self.retrieval == MemoryRetrieval.first_n else "desc",
)
else:
raise NotImplementedError("Semantic retrieval not yet supported.")
except Exception as e:
logger.debug(f"Error reading memory: {e}")
return
# Clear the existing memories
self.memories = []
# No memories to load
if memory_rows is None or len(memory_rows) == 0:
return
for row in memory_rows:
try:
self.memories.append(Memory.model_validate(row.memory))
except Exception as e:
logger.warning(f"Error loading memory: {e}")
continue
def should_update_memory(self, input: str) -> bool:
"""Determines if a message should be added to the memory db."""
if self.classifier is None:
self.classifier = MemoryClassifier()
self.classifier.existing_memories = self.memories
classifier_response = self.classifier.run(input)
if classifier_response == "yes":
return True
return False
async def ashould_update_memory(self, input: str) -> bool:
"""Determines if a message should be added to the memory db."""
if self.classifier is None:
self.classifier = MemoryClassifier()
self.classifier.existing_memories = self.memories
classifier_response = await self.classifier.arun(input)
if classifier_response == "yes":
return True
return False
def update_memory(self, input: str, force: bool = False) -> Optional[str]:
"""Creates a memory from a message and adds it to the memory db."""
if input is None or not isinstance(input, str):
return "Invalid message content"
if self.db is None:
logger.warning("MemoryDb not provided.")
return "Please provide a db to store memories"
self.updating_memory = True
# Check if this user message should be added to long term memory
should_update_memory = force or self.should_update_memory(input=input)
logger.debug(f"Update memory: {should_update_memory}")
if not should_update_memory:
logger.debug("Memory update not required")
return "Memory update not required"
if self.manager is None:
self.manager = MemoryManager(user_id=self.user_id, db=self.db)
else:
self.manager.db = self.db
self.manager.user_id = self.user_id
response = self.manager.run(input)
self.load_user_memories()
self.updating_memory = False
return response
async def aupdate_memory(self, input: str, force: bool = False) -> Optional[str]:
"""Creates a memory from a message and adds it to the memory db."""
if input is None or not isinstance(input, str):
return "Invalid message content"
if self.db is None:
logger.warning("MemoryDb not provided.")
return "Please provide a db to store memories"
self.updating_memory = True
# Check if this user message should be added to long term memory
should_update_memory = force or await self.ashould_update_memory(input=input)
logger.debug(f"Async update memory: {should_update_memory}")
if not should_update_memory:
logger.debug("Memory update not required")
return "Memory update not required"
if self.manager is None:
self.manager = MemoryManager(user_id=self.user_id, db=self.db)
else:
self.manager.db = self.db
self.manager.user_id = self.user_id
response = await self.manager.arun(input)
self.load_user_memories()
self.updating_memory = False
return response
def update_summary(self) -> Optional[SessionSummary]:
"""Creates a summary of the session"""
self.updating_memory = True
if self.summarizer is None:
self.summarizer = MemorySummarizer()
self.summary = self.summarizer.run(self.get_message_pairs())
self.updating_memory = False
return self.summary
async def aupdate_summary(self) -> Optional[SessionSummary]:
"""Creates a summary of the session"""
self.updating_memory = True
if self.summarizer is None:
self.summarizer = MemorySummarizer()
self.summary = await self.summarizer.arun(self.get_message_pairs())
self.updating_memory = False
return self.summary
def clear(self) -> None:
"""Clear the AgentMemory"""
self.runs = []
self.messages = []
self.summary = None
self.memories = None
def deep_copy(self):
# Create a shallow copy of the object
copied_obj = self.__class__(**self.model_dump())
# Manually deepcopy fields that are known to be safe
for field_name, field_value in self.__dict__.items():
if field_name not in ["db", "classifier", "manager", "summarizer"]:
try:
setattr(copied_obj, field_name, deepcopy(field_value))
except Exception as e:
logger.warning(f"Failed to deepcopy field: {field_name} - {e}")
setattr(copied_obj, field_name, field_value)
copied_obj.db = self.db
copied_obj.classifier = self.classifier
copied_obj.manager = self.manager
copied_obj.summarizer = self.summarizer
return copied_obj
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