4 Repos
Mechanisms for isolating stored data by entity, process, or session identifiers to ensure secure and context-aware retrieval.
Distinct from Context Scoping: Distinct from Context Scoping: focuses on data isolation for multi-tenant memory systems rather than hierarchical UI or plugin boundaries.
Explore 4 awesome GitHub repositories matching user interface & experience · Memory Scoping. Refine with filters or upvote what's useful.
Memori is an AI agent memory middleware platform designed to provide persistent, context-aware recall for language models. It functions as a non-intrusive layer that intercepts outbound model requests to automatically capture interaction history and execution traces, ensuring that agents maintain continuity across sessions without requiring modifications to existing application logic. The platform distinguishes itself through a dual-model storage architecture that maintains information as both structured relational primitives for precise fact retrieval and rolling narrative summaries for situ
Links stored memories to specific users or processes to ensure information is properly indexed and retrieved.
This repository is a technical documentation site and a collection of guides and references for implementing networking, security, and cloud infrastructure services. It functions as a static-site generated portal and a headless content platform, separating source files from the presentation layer to enable flexible rendering. The project utilizes markdown-based documentation stored in a version-controlled Git repository. It provides specialized technical content including an AI platform documentation for building agents and managing inference, a cloud infrastructure guide for DNS and CDN conf
Isolates stored AI memory into distinct profiles based on users, teams, or environments.
Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project
Tracks user preferences and conversation history using scoped storage to maintain personalized context.
OpenMemory is an embeddable memory engine for LLM agents that stores, retrieves, and manages conversational context and agent state using semantic indexing and temporal facts. It functions as a semantic memory store backed by vector indexing, where memories are organized by meaning rather than by exact key, and includes a tiered decay engine that gradually reduces the salience of unused memories while compressing cold vectors and fingerprinting dormant entries to conserve storage. The system also maintains a temporal fact database that records factual statements with subject-predicate-object s
Removes every memory entry associated with a given user identifier from the store.