MemoryModel

The MemoryModel class stores conversation history and context.

Inheritance

MemoryModel extends DirtyAwareBaseModel (which itself combines BaseModel with dirty-mark tracking), enabling automatic mutation tracking on all fields.

Properties

• messages (list): List of messages in the conversation
• time (float): Timestamp
• abstract (str): Summary

Dirty Tracking Methods

Inherited from DirtyAwareBaseModel, these methods allow checking whether fields have been modified:

• is_dirty(name: str | None = None) -> bool: Check whether a specific attribute (or any attribute) has been modified
• get_dirty_vars() -> set[str]: Return the set of all dirty attribute names
• clean(): Reset the dirty state, clearing all tracked changes

Example

from amrita_core.types import MemoryModel, Message

memory = MemoryModel()
memory.messages.append(Message(content="Hello", role="user"))
memory.messages.append(Message(content="Hi there", role="assistant"))

# Check dirty state
assert memory.is_dirty("messages")  # True — messages was modified
print("Dirty vars:", memory.get_dirty_vars())  # {'messages'}

memory.clean()  # Reset tracking
assert not memory.is_dirty()  # True — no pending changes

Description

The MemoryModel class inherits from DirtyAwareBaseModel and is used to store conversation history, timestamps, and summary information. It is an important component for managing conversation context. The dirty-mark mechanism allows backends to efficiently detect which fields have changed and only persist the modified portions.