15 Repos
Storage solutions that allow AI agents to recall past interactions and user preferences.
Distinguishing note: Focuses on the persistence capability rather than the database implementation.
Explore 15 awesome GitHub repositories matching artificial intelligence & ml · Memory Persistence. Refine with filters or upvote what's useful.
Mem0 is an agent-agnostic memory layer designed to provide intelligent agents with long-term persistence and cross-session state management. By acting as a centralized service, it allows diverse AI agents to recall user preferences, past interactions, and historical context, ensuring continuity across multiple workflows and independent agent systems. The platform distinguishes itself through a multi-signal retrieval engine that combines semantic vectors, keyword matching, and entity-linked metadata to surface the most relevant information. It employs an adaptive memory engine that automatical
Provides AI agents with long-term storage to recall user preferences and historical context.
Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai
Uses pluggable state and store backends to recall information and maintain continuity across user sessions.
Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut
Stores and retrieves specific state variables across conversation threads to provide persistent context to the model.
Eliza is a modular framework designed for building and deploying autonomous agents that operate across diverse digital environments. It functions as an orchestrator for intelligent software, enabling agents to manage tasks, maintain persistent memory, and execute automated processes through a centralized runtime. The framework distinguishes itself through a plugin-based architecture that facilitates cross-platform social automation and blockchain transaction capabilities. By utilizing state-machine logic for decision-making and vector-based memory for context retention, the system allows agen
Ensures agents keep context across sessions by recalling past interactions and data points.
Agent Zero is an autonomous AI agent framework designed to execute complex, multi-step workflows by managing its own environment, persistent memory, and external tool interactions. It functions as a Python-based automation library that enables agents to write code, execute terminal commands, and perform system-level tasks independently. The system is built to handle large-scale operations through hierarchical agent delegation, allowing for the coordination of subordinate agents to maintain focus and context. The platform distinguishes itself through a focus on secure, isolated execution and s
Stores solutions, facts, and instructions in a persistent database to retain important information across work sessions.
Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions. The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orche
Maintains consistent agent understanding by storing and recalling information across multiple sessions.
Leon is a framework for building personal AI assistants that integrates large language models with local tool execution and persistent memory. It functions as an agentic workflow orchestrator and modular skill engine, enabling the creation of autonomous assistants capable of planning and executing multi-step tasks. The system features a retrieval-augmented generation memory architecture that indexes conversation history and user facts for context-aware grounding. It utilizes a modular skill system to interact with external binaries and APIs, supported by a loop that handles tool calling, sche
Stores durable preferences and recent conversation history to maintain context across different interactions.
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
Configures database connections to store and structure long-term memory using standard relational or document-based backends.
BrowserOS is an AI agent browser orchestrator and automation framework designed to manage browser state and execute complex web workflows. It functions as a local AI browser assistant and a Model Context Protocol controller, enabling the control of browser tabs, windows, and navigation through programmable AI agents and standardized context protocols. The system distinguishes itself through a graph-based visual workflow builder for creating repeatable automation sequences and the use of markdown-based files to define agent personalities and task recipes. It supports multi-provider orchestrati
Stores user facts and preferences in local files to maintain persistent context across AI conversations.
MemOS is an open-source persistent memory layer for AI agents and large language models, providing a self-hosted server that stores and retrieves structured memory across sessions. It enables AI systems to recall user preferences, history, and context without retraining, using a graph-based API and a web management interface for viewing, editing, and organizing memory items, skills, traces, and knowledge bases. The system distinguishes itself through a portable memory interchange protocol that allows memory to be transferred between different AI models, devices, and applications, along with a
Provides memory persistence so AI agents recall past interactions and user preferences across sessions.
Maintains a knowledge graph-based memory system that persists across sessions for context retention.
MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API. The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for se
Stores user preferences and conversational history across multiple sessions for consistent AI interactions.
Geist is an open-source font family and typography collection designed for high legibility in technical interfaces. It consists of a series of web-optimized typefaces, including geometric sans-serif, monospaced, and pixel styles. The collection functions as a variable font library, utilizing coordinate interpolation to allow precise control over weight and style within a single font file. These fonts are built as OpenType typefaces, incorporating standardized layout tables to define advanced typographic behaviors such as kerning and ligatures. The project provides specific implementations fo
Implements storage solutions that allow AI assistants to retain context and user data over time.
Rikkahub is an AI model aggregator and frontend interface that provides a unified platform for interacting with multiple large language model providers. It serves as a retrieval-augmented generation chat client with a provider-agnostic gateway, allowing users to switch between different models and endpoints. The platform features a character persona manager for importing structured character cards and behavior settings to define specific interaction styles. It includes a sandboxed code execution environment with a portable Linux agent for running technical scripts and commands within the chat
Implements memory persistence allowing AI agents to recall user preferences and past interactions over long periods.
Mimiclaw is a framework for integrating large language models with microcontroller hardware to create interactive AI agents. It provides an embedded AI agent runtime and a tool-calling engine that allows language model loops to execute on embedded devices. The system acts as a bridge between language model APIs and physical hardware peripherals, enabling the control of sensors and actuators through natural language. The project features a dedicated manager for over-the-air firmware updates, allowing system images to be installed via web browsers or wireless networks to remove local toolchain
Stores personality traits and user preferences on local memory to allow AI agents to recall past interactions.