Best practices for LangChain Integration and handling Memory Conflicts/Updates

[JoesSattes]

Hi there, thanks for this project.

I’ve been testing OpenMemory, specifically focusing on the Server Mode for multi-user memory functions. My goal is to integrate OpenMemory into a LangChain application that handles concurrent users, separated by user_id.

After my initial tests, I have two main questions regarding architecture and logic:
1. Recommended Integration Pattern

For a LangChain setup with multiple concurrent users, what approach do you recommend?

• Option A: Connect via API or write an MCP (Model Context Protocol) wrapper to treat OpenMemory as an external service.
• Option B: Integrate directly with LangChain within the same codebase.

2. Handling Memory Evolution and Conflicts

I am curious about how the system handles conflicting or evolving information.

• Scenario A (Contextual Evolution):
  If the input sequence is: "I like high-tech stuff" $\to$ "Bought IoT device" $\to$ "Don't know how to use it" $\to$ "Stopped using IoT."
  Instead of just returning a chat log, can OpenMemory summarize this narrative? (e.g., "The user likes tech but stopped using IoT devices because they found them too difficult.")

• Scenario B (Direct Update/Conflict):
  If a user says "I like Adidas" and later says "I like Nike," does the system update the state to reflect that the user currently prefers Nike?

Any advice on how to achieve these behaviors would be appreciated!