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 ist ein Prompt-Engineering-Framework, das für das Design, das Testen und die Optimierung von Prompts für Large Language Models entwickelt wurde. Es bietet ein System zur Messung der Modellgenauigkeit und -leistung durch ein Evaluierungs-Toolkit, das Ausgaben mit Ground-Truth-Datensätzen vergleicht. Das Projekt enthält zudem eine Orchestrierungspipeline für die Automatisierung von Machine-Learning-Aufgaben mit mehreren Komponenten über Cloud-basierte Endpunkte sowie ein Dienstprogramm zur Vorbereitung von RAG-Datensätzen (Retrieval-Augmented Generation). Das Framework zeichnet sich durch fortgeschrittene Optimierung der Antwortqualität aus und nutzt Chain-of-Thought-Generatoren, um zwischenzeitliche Argumentationsschritte zu erzeugen, sowie dynamisches Few-Shot-Beispiel-Retrieval unter Verwendung embedding-basierter semantischer Suche. Es implementiert Ensemble-Methoden zur Steigerung der Vorhersagegenauigkeit und verwendet komplexitätsbasiertes Query-Routing sowie Mehrheitsentscheidungs-Aggregation mehrerer Modellvariationen. Das System deckt breitere Funktionen im Datenmanagement und der Automatisierung ab, einschließlich der Formatierung externer Daten in strukturierte Dateien für das Training und der Orchestrierung von Modellausführungspipelines über Kommandozeilen-Dienstprogramme.

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 ist eine Enterprise-KI-Control-Plane und eine Plattform zur Orchestrierung von LLM-Agenten. Sie bietet eine Zero-Code-Umgebung zum Entwerfen, Bereitstellen und Verwalten von KI-Agenten in der Produktion durch ein Multi-Agent-Collaboration-Framework, das spezialisierte autonome Agenten mithilfe standardisierter Messaging-Protokolle koordiniert. Die Plattform integriert das Model Context Protocol, um Agenten mit externen Tools, Plugins und Diensten über eine universelle Kommunikationsschnittstelle zu verbinden. Sie zeichnet sich zudem durch einen dedizierten RAG-Knowledge-Base-Manager aus, der unstrukturierte Dokumente importiert und hybride Suche nutzt, um fundierten Kontext für Modellantworten bereitzustellen. Das System deckt ein breites Spektrum an Funktionen ab, darunter mandantenfähige rollenbasierte Zugriffskontrolle, multimodale Interaktion über Text, Sprache und Bilder sowie hybrides Vektor-Retrieval. Es enthält zudem einen Marktplatz für die Verteilung und Entdeckung von Agenten sowie Observability-Tools zur Erfassung von Ausführungs-Traces. Die Plattform unterstützt sichere Bereitstellung durch containerisierte Offline-Paketierung für Air-Gapped-Infrastrukturen.

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 ist ein Multi-Agenten-Framework und Orchestrator, der für das Deployment und die Verwaltung mehrerer Large-Language-Model-Agenten entwickelt wurde. Es bietet eine Simulationsumgebung, in der Agenten basierend auf benutzerdefinierten Personas und definierten Interaktionsregeln interagieren, um Aufgaben zu lösen oder soziale Dynamiken zu simulieren. Das System verfügt über eine Tool-Integrationsschicht, die Agenten mit externen funktionalen Plugins und spezialisierten Tools verbindet und deren Fähigkeiten über die Textgenerierung hinaus erweitert. Es verwendet eine Kombination aus Persona-basiertem Prompt-Injection und zustandsverwaltetem Speicher, um die Konsistenz der Agenten und spezialisierte Fähigkeiten während der Simulationen aufrechtzuerhalten. Das Framework umfasst eine Simulations-Runtime und eine Aufgaben-Engine mit einem lokalen webbasierten Dashboard zum Ausführen und Überwachen von Szenarien. Es unterstützt konfigurationsgesteuerte Umgebungsbereitstellung zur Definition von Agentenverhalten, Nachrichten-Routing und turn-basierter Orchestrierung.

OpenSquilla ist ein LLM-Agent-Orchestration-Framework zur Koordination mehrstufiger KI-Workflows und Tool-Ausführungen mittels gerichteter azyklischer Graphen. Es fungiert als zentrales System zur Verwaltung spezialisierter Skill-Pakete und zur Ausführung komplexer Reasoning-Sequenzen. Das Projekt zeichnet sich durch ein Routing-Gateway aus, das Aufgaben basierend auf Komplexität, Kosten und Performance an verschiedene KI-Anbieter weiterleitet. Es nutzt ein mehrstufiges KI-Gedächtnissystem, das Arbeits-, episodisches und semantisches Wissen mittels lokaler Embeddings und SQLite organisiert, sowie eine sichere Ausführungsumgebung (Sandbox), die Agent-generierten Code über risikobasierte Berechtigungsprofile isoliert. Die Plattform deckt ein breites Spektrum an Funktionen ab, einschließlich Multi-Channel-Deployment für Web- und Messaging-Plattformen, automatisierter Aufgabenplanung via Cron und einer Model Context Protocol-Bridge zur Anbindung externer Tools. Zudem bietet sie umfassende Monitoring- und Observability-Tools zur Verfolgung von Token-Kosten, zum Auditing von Laufzeitentscheidungen und zur Verwaltung eines Katalogs wiederverwendbarer Skills. Das System enthält CLI-Utilities für die Workspace-Initialisierung und das Skill-Lifecycle-Management.

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