3 مستودعات
Mechanisms for storing and retrieving user context and interaction history across multiple sessions.
Distinguishing note: Specifically targets long-term context retention for AI assistants, rather than generic database storage.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Conversational Memory Systems. Refine with filters or upvote what's useful.
Mempalace is a long-term memory management system for large language models that orchestrates the storage and retrieval of conversation history and entity relationships. It functions as a memory orchestrator and Model Context Protocol server, providing AI clients with read and write access to structured knowledge. The system utilizes a temporal knowledge graph to track evolving entity relationships and timelines with validity windows. It employs a hierarchical memory partitioning strategy, organizing data into wings and rooms to isolate specialist agent contexts and restrict semantic searches
Provides a complete system for storing and retrieving long-term user context and interaction history across multiple AI sessions.
LibreChat is an artificial intelligence orchestration platform that provides a unified interface for interacting with multiple language models. It functions as a centralized workspace where users can switch between different intelligence engines, manage complex conversational workflows, and maintain persistent memory across sessions through a vector-database-backed storage system. The platform distinguishes itself through an extensible agent framework that supports autonomous task execution and the integration of external tools. It features a secure, containerized environment for executing co
Store user preferences and context across multiple sessions to ensure the assistant recalls past interactions and provides a consistent, personalized experience over long periods of time.
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
Implements a RAG memory system using vector embeddings to maintain long-term conversational context.