Concept: Memory

For an agent to have a coherent conversation, it must remember what has been said previously. The Memory system in CogniCoreAI is responsible for storing, retrieving, and managing the conversation history.

The BaseMemory Contract

Like the LLM system, the memory system is built on an abstraction: the cognicoreai.memory.BaseMemory class. This ensures that the Agent can interact with any type of memory store in a consistent way. The BaseMemory contract requires all memory classes to implement:

• add_message(message): Adds a new message to the history.

• get_history(): Retrieves the full list of messages.

• clear(): Wipes the entire history.

The Message Structure

All messages stored in memory follow a consistent structure, defined by the cognicoreai.memory.Message typed dictionary. A message has two keys:

• role: Who is speaking. This can be "system", "user", "assistant", or "tool".

• content: The text of the message.

Volatile vs. Persistent Memory

CogniCoreAI comes with a simple, default memory implementation:

• ``VolatileMemory``: Stores the conversation history in a list in your computer’s RAM. This is fast and simple, but the history is lost as soon as your script finishes. It’s perfect for development and testing.

from cognicoreai import VolatileMemory

# History is stored in RAM and will be cleared on exit.
memory = VolatileMemory()
agent = Agent(memory=memory, ...)

The BaseMemory abstraction makes it straightforward to implement your own persistent memory backends. For example, you could create a FileMemory class that saves the history to a JSON file or a DatabaseMemory class that connects to a SQL database, allowing conversations to be resumed across multiple sessions.