Cloud

Cloud - An Advanced AI Memory Server


Cloud acts as the central memory hub for all my AI agents. This is a customized mem0 server that uses Neo4j for graph_store and Qdrant for vector_store.

TO-DO

• Create mailbox system for shared communication through all my AI apps

The mem0 package included in this repo is a modified version of the mem0 package from the [mem0ai](https://github.com/mem0ai/mem0) repo.

• `/add` allows you to add memories by passing a memory string, plus `agent_id`, `run_id`, `user_id`, and optional parameters:

  • metadata: Additional information about the memory (timestamps, etc.)
  • skip_extraction: (bool, default=False) If True, bypasses LLM fact-extraction and stores raw content directly
  • store_mode: (str, default="both") Where to store memories - options are "both", "vector", or "graph"
    • Note: When skip_extraction=True, store_mode must be "vector" as graph storage requires fact extraction (Skip extraction was created to store raw, un-edited memories in the vector store to keep FULL details, typically used for business or story knowledge for RAG.)

• `/query` allows you to search the stored memories with a query string, plus optional `agent_id`, `run_id`, `user_id`, and `limit`.

• `/get_all` allows you to retrieve all memories filtered by `agent_id`, `run_id`, and/or `user_id`.

How we treat mem0 functions:

• "agent_id" is the name of the agent that is making the memory.
• "run_id" is the category of memories. In my specific use case, my 3 main categories are "general_knowledge", "agent_specific", and "user_specific".
• "user_id" is the id of the user that is making the memory. optional, only invoked when called user_specific memories
• "metadata" is any additional information you want to store about the memory. timestamps, etc.

**Installation & Running:**

1. Install depende