RichmondAlake/memorizz
MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management.
Implements five distinct memory subsystems (episodic, semantic, procedural, short-term, shared) with pluggable backends including Oracle, MongoDB, and local FAISS, enabling persistent cross-session context and semantic retrieval via embeddings. Provides preset application modes (`assistant`, `workflow`, `deep_research`) that auto-configure memory stacks, plus optional integrations for internet search (Tavily), sandboxed code execution (E2B/Daytona), and scheduled automations with delivery hooks. Built on a builder pattern with automatic tool registration and multi-agent orchestration via shared blackboard memory.
692 stars. Actively maintained with 1 commit in the last 30 days.
Stars
692
Forks
76
Language
Python
License
—
Category
Last pushed
Mar 10, 2026
Commits (30d)
1
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