How to split MCP into short-term and long-term memory layers?

 

Step 1: Understand the Structure of MCP

 

To effectively split MCP into short-term and long-term memory layers, you need to comprehend the components of MPC:

• System Instructions: Define the fixed, broad-purpose role of the model.
• User Profile: Stores user-specific data such as preferences and objectives.
• Document Context: Contains a repository of knowledge relevant to the conversation.
• Active Tasks/Goals: Specifies current objectives the model is working towards.
• Tool Access: Permission for the model to access various external tools.
• Rules/Constraints: Specific boundaries, like restricting medical advice.

 

Step 2: Identify Long-Term and Short-Term Components

 

• Long-Term Memory: This consists of components that persist across sessions and are foundational for the model's operation.

• User Profile: As it retains data over time.

• System Instructions: As these generally remain constant.

• Rules/Constraints: These form the boundaries and guidelines for operation.

• Short-Term Memory: This includes elements that are more transient and change session-to-session or task-to-task.

• Document Context: May change based on new inputs.

• Active Tasks/Goals: They change as tasks are completed or updated.

• Tool Access: Might be adjusted depending on specific task requirements.

 

Step 3: Code the Memory Layer Structure

 

Implementing the structure requires separating out the components into two distinct sections. This can be represented in code to reflect both long-term and short-term storage.

Pseudocode: Structure for MCP Memory layers

class MCPMemory:
    def init(self):
        # Long-term memories initialized here
        self.longtermmemory = {
            "userprofile": loaduser_profile(),
            "systeminstructions": loadsystem_instructions(),
            "rulesconstraints": loadrules_constraints()
        }

        # Short-term memories initialized here
        self.shorttermmemory = {
            "document_context": [],
            "active_tasks": [],
            "tool_access": []
        }

    def updateshorttermmemory(self, documentcontext, activetasks, toolaccess):
        self.shorttermmemory["documentcontext"] = documentcontext
        self.shorttermmemory["activetasks"] = activetasks
        self.shorttermmemory["toolaccess"] = toolaccess

    def resetshortterm_memory(self):
        self.shorttermmemory = {
            "document_context": [],
            "active_tasks": [],
            "tool_access": []
        }

 

Step 4: Implementing Context Loading and Management

 

Ensure each session loads long-term components and adjusts short-term components dynamically.

Simulate loading and resetting context

mcp_memory = MCPMemory()

Simulate a session
sessiondocumentcontext = ["recentupload1", "recentupload2"]
sessionactivetasks = ["task1", "task2"]
sessiontoolaccess = ["toolA"]

Update short-term memory for current session
mcpmemory.updateshorttermmemory(sessiondocumentcontext, sessionactivetasks, sessiontoolaccess)

View updated memory
print(mcpmemory.shortterm_memory)

Resetting after the session ends
mcpmemory.resetshorttermmemory()

 

Step 5: Establishing Usage Across Systems

 

Deploy MCP in the wider system architecture where these memory distinctions facilitate more predictable and reliable model behavior. Ensure seamless integration with platforms that utilize memory and context in AI/LLM frameworks.

 

Implement these layers within autonomous agent frameworks or chatbot platforms, enabling structured context flow, and aiding in modular design for complex AI tasks.