Awesome GitHub RepositoriesAgent Memory Architectures

Hermes-agent is an autonomous AI agent framework and runtime designed to execute complex tasks and synthesize new skills from execution traces. It includes a provider-agnostic gateway for routing requests across multiple model backends and a serverless runtime that suspends idle agent instances and resumes them on demand across containers and virtual machines. The project provides a desktop automation toolset that controls native GUI workflows on Linux by querying accessibility APIs and injecting input events. It further distinguishes itself with the ability to generate procedural skills from

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

Embedchain is an LLM memory management framework and RAG orchestration engine designed to provide AI agents with a persistent storage layer. It functions as a long-term memory pipeline that extracts facts from unstructured interactions and stores them as permanent knowledge base entries to retain user preferences and interaction history across sessions. The system employs a hybrid vector database interface that combines semantic embeddings with traditional keyword search. It utilizes an entity-linking knowledge graph to connect related information points and applies temporal ranking to distin

AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel

This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

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

OpenHarness is a framework for building and orchestrating AI agents that utilize tools and plugins to execute complex tasks. It provides an orchestration system for managing language model lifecycles and a multi-agent coordination system for delegating workloads across teams of specialized subagents. The project features an agent gateway that bridges language model agents to external chat platforms and communication channels. It includes a tool integration engine for executing shell, file, and web operations, supported by a memory and skill manager that handles persistent user preferences and

Context Hub is a retrieval-augmented generation framework and context management system designed to provide large language model agents with curated, versioned markdown documentation. It functions as a documentation provider that delivers precise API references and technical context to reduce hallucinations and token waste. The system incorporates an agentic memory layer that maintains persistent local annotations and user feedback to improve how agents retrieve task-specific knowledge. It uses a version-controlled repository of technical documentation designed for both machine readability an

Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

Prompt Master is an AI skill that automates prompt engineering by detecting the target AI system and applying the correct prompt architecture automatically. It generates optimized prompts for over 30 different AI tools, adapting format and syntax to each target system without requiring manual conversion. The system distinguishes itself through several integrated capabilities. It extracts missing dimensions of intent from vague requests by asking up to three targeted clarifying questions before generating a final prompt. A memory block of prior decisions and constraints is prepended to maintai

memU is a long-term memory system for AI agents that provides a persistent knowledge base. It extracts facts and preferences from conversations into structured memories, organizing this information through a hierarchical knowledge base based on a file-system architecture of nested categories and linked resources. The system includes a multimodal data ingestion pipeline that converts audio, video, and images into standardized natural language for storage in large language model contexts. It also features a model provider abstraction layer, offering a unified interface to use interchangeable la

Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation. The framework distinguishes itself through a distributed architecture that enables multiple agents to collaborate and share learned experiences across a network. It operate

Promptbase 是一个提示工程框架，旨在设计、测试和优化大语言模型的提示。它提供了一个系统，通过评估工具包比较输出与基准数据集来衡量模型准确性和性能。该项目还包括一个编排流水线，用于跨云端端点自动化多组件机器学习任务，以及一个用于准备检索增强生成（RAG）数据集的实用程序。 该框架通过高级响应质量优化脱颖而出，利用思维链（chain-of-thought）生成器产生中间推理步骤，并使用基于嵌入的语义搜索进行动态少样本（few-shot）示例检索。它实现了集成方法以提高预测准确性，采用基于复杂性的查询路由和多个模型变体的多数投票聚合。 该系统涵盖了数据管理和自动化的更广泛功能，包括将外部数据格式化为用于训练的结构化文件，以及通过命令行实用程序编排模型执行流水线。

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

Nexent 是一个企业级 AI 控制平面和 LLM 智能体编排平台。它提供了一个零代码环境，用于通过多智能体协作框架设计、部署和管理生产级 AI 智能体，该框架使用标准化消息协议协调专门的自主智能体。 该平台集成了模型上下文协议（Model Context Protocol），通过通用通信接口将智能体与外部工具、插件和服务连接起来。它还以专用的 RAG 知识库管理器脱颖而出，该管理器导入非结构化文档并利用混合搜索为模型响应提供扎实的上下文。 该系统涵盖了广泛的功能，包括多租户基于角色的访问控制、跨文本、语音和图像的多模态交互以及混合向量检索。它还包括用于智能体分发和发现的市场，以及用于捕获执行轨迹的可观测性工具。 该平台通过用于气隙基础设施的容器化离线打包支持安全部署。

AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations

Agentverse 是一个多智能体框架和编排器，专为部署和管理多个大语言模型智能体而设计。它提供了一个模拟环境，智能体在其中基于自定义角色和定义的交互规则进行交互，以解决任务或模拟社会动态。 该系统具有一个工具集成层，将智能体连接到外部功能插件和专业工具，将其能力扩展到文本生成之外。它结合了基于角色的提示词注入和状态管理内存，以在模拟期间保持智能体的一致性和专业技能集。 该框架包括一个模拟运行时和一个带有本地 Web 仪表板的任务引擎，用于执行和监控场景。它支持配置驱动的环境配置，以定义智能体行为、消息路由和基于轮次的编排。

OpenSquilla 是一个 LLM 智能体编排框架，旨在利用有向无环图协调多步 AI 工作流和工具执行。它作为一个集中式系统，用于管理专门的技能包并执行复杂的推理序列。 该项目通过一个路由网关脱颖而出，该网关根据复杂性、成本和性能将任务定向到不同的 AI 提供商。它利用多层 AI 记忆系统，通过本地嵌入和 SQLite 组织工作、情景和语义知识，并配有一个安全执行沙盒，通过基于风险的权限配置文件隔离智能体生成的代码。 该平台涵盖了广泛的功能，包括多渠道部署到 Web 和消息平台、通过 cron 进行自动任务调度，以及用于连接外部工具的 Model Context Protocol 网桥。它还提供全面的监控和可观测性工具，用于跟踪 Token 成本、审计运行时决策以及管理可重用技能目录。 该系统包括用于工作区初始化和技能生命周期管理的命令行工具。

The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec