24 repository-uri
Mechanisms for multiple agents to read and write to a shared knowledge graph with session-level privacy and isolation.
Distinct from Shared Memory Management: Distinct from Shared Memory Management: specifically focuses on persistent knowledge graph storage for agents.
Explore 24 awesome GitHub repositories matching software engineering & architecture · Shared Knowledge Graph Memory. Refine with filters or upvote what's useful.
Graphify is a knowledge retrieval system that transforms directories of source code and documentation into structured, queryable project maps. It utilizes a code-to-graph parser to extract technical metadata and system connectivity, converting a mix of code, SQL schemas, and documentation into a unified graph structure. The project distinguishes itself by integrating these knowledge graphs with AI coding assistants through a Model Context Protocol server and dedicated tool hooks. This allows AI agents to perform lookups and impact analysis on node neighbors and shortest paths to understand ho
Hosts a centralized knowledge graph over HTTP so multiple team members can query a single source of project truth.
Nanoclaw is an LLM agent orchestrator and multi-platform chat gateway designed to deploy and manage isolated AI agents. It provides a containerized runtime that executes agents within sandboxed Linux containers, ensuring filesystem and state isolation through dedicated workspaces and host bind-mounts. The project distinguishes itself through a unified routing pipeline that connects agents to diverse messaging platforms, including WhatsApp, Discord, Slack, Telegram, Signal, and iMessage. It integrates the Model Context Protocol to extend agent capabilities via managed external data and functio
Allows separate chat sessions to read and write to a common filesystem for shared agent memory.
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
Provides a shared knowledge graph where multiple agents can read and write common patterns with session-level isolation.
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
Allows attaching shared memory blocks to multiple agents for synchronized context.
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
Supports shared knowledge graph access for collaborative environments with session-level isolation.
Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries
Enables multiple agents to share a knowledge graph while maintaining session-level privacy and multi-tenant isolation.
Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio
Utilizes a structured knowledge graph to maintain long-term factual memory across different projects and sessions.
DeepTutor is a framework for personalized AI tutoring and educational content generation. It functions as an agentic workflow system that executes reasoning loops to complete multi-step tasks, transforming raw sources into structured learning materials such as interactive books, quizzes, and concept graphs. The platform distinguishes itself through an extensible skill architecture that allows the installation and auditing of third-party capability packages from community registries. It utilizes persona-driven tool policies to deploy persistent AI companions with unique behavioral profiles and
Synchronizes memory and state across different tools via a shared knowledge base to maintain consistent user context.
This project is a comprehensive framework for the orchestration, evaluation, and context management of large language model agents. It provides a set of architectural patterns and standards for designing agent interactions, integrating external tools, and establishing memory architectures to persist knowledge across sessions. The system focuses on optimizing the limited memory of language models through token-aware context compression and filesystem-based context offloading. It incorporates secure execution environments using sandboxed virtual machines and isolated containers to safely run ba
Implements long-term memory using a network of entities and relationships in a knowledge graph.
jcode este un framework pentru dezvoltarea de agenți de codare AI autonomi care automatizează sarcinile de dezvoltare software. Acesta funcționează ca un orchestrator de agenți, runtime de instrumente și motor de memorie semantică, permițând crearea de agenți care pot modifica codul, rula teste și itera asupra propriei funcționalități. Proiectul se distinge prin utilizarea swarming-ului de agenți recursivi, unde o ierarhie de agenți colaboratori poate genera agenți copii pentru a descompune sarcini complexe. Implementează un sistem de memorie semantică care combină regăsirea bazată pe vectori cu maparea relațiilor bazată pe grafuri pentru a menține contextul între sesiuni. Pentru a gestiona riscul, sistemul utilizează guvernanța acțiunilor pe niveluri care necesită aprobarea umană pentru operațiuni sensibile și izolează activitățile agenților în worktree-uri git separate. Framework-ul include un toolkit cuprinzător de automatizare a browserului pentru interacțiunea cu paginile web, extragerea snapshot-urilor DOM și capturarea capturilor de ecran. De asemenea, implementează Model Context Protocol pentru a integra instrumente și date externe și suportă hot-reloading binar pentru a actualiza serverul fără a pierde conexiunile de rețea active. Sistemul oferă o interfață de linie de comandă pentru gestionarea memoriilor agenților și include instrumente de audit pentru a urmări progresul planului și a vizualiza topologia roiului de agenți.
Provides a command-line interface to view, edit, delete, or export agent memories using human-readable JSON.
Athens is no longer maintainted. Athens was an open-source, collaborative knowledge graph, backed by YC W21
Lets multiple users work on the same knowledge graph simultaneously through hosted or self-hosted instances.
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
Shares persistent memory between different models, devices, and applications using a portable memory interchange protocol.
PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo
Implements thread-safe shared memory coordination so multiple agents can concurrently access a common store.
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
Links a single memory layer to different LLM providers to avoid vendor lock-in using portable protocols.
DashMap este o hartă hash concurentă pentru Rust, oferind un tablou asociativ sigur pentru firele de execuție, conceput pentru acces multi-threaded de înaltă performanță. Servește drept structură de date concurentă care permite citiri și scrieri simultane fără a necesita un lock global. Proiectul utilizează o arhitectură de lock-uri fragmentate (sharded) pentru a reduce contenciosul între firele de execuție, folosind blocarea la nivel granular de fragment. Este o hartă compatibilă cu Serde, implementând serializarea și deserializarea pentru a converti datele hărții în și din formate comune. Biblioteca acoperă capabilități pentru stocarea concurentă a datelor, gestionarea stării partajate și implementarea cache-urilor sigure pentru firele de execuție.
Enables multiple threads to safely read and modify a common memory store without a global lock.
EvoScientist is an autonomous AI scientist and multi-agent research framework designed to plan, code, and execute end-to-end scientific research workflows. It functions as an agentic workflow orchestrator that uses a state-machine to coordinate specialized agents through iterative phases of planning, execution, and verification. The system is distinguished by a persistent knowledge graph memory that distills agent interactions into reusable skills and a hub for integrating external tools via the Model Context Protocol. It features a provider-agnostic model layer for switching between language
Converts recurring research patterns and findings into a graph-based memory system to create reusable skills.
This project is an API gateway optimization manual and implementation guide for OpenResty. It provides a collection of architectural patterns and coding standards for developing scriptable server logic using the Lua language within Nginx. The repository serves as a reference for extending web server functionality and optimizing network traffic gateways. It focuses on deployment strategies and high-performance coding patterns to reduce latency and increase request throughput. The content covers the development of custom gateway logic, edge computing workflows, and high-throughput network engi
Provides thread-safe shared memory stores for fast data retrieval across multiple Nginx worker processes.
Cipher is an AI agent orchestration framework and LLM context memory layer. It provides a collaborative AI knowledge base and a context synchronization service that allows AI agents and CLI tools to maintain a persistent, structured memory of project decisions and codebase patterns across different sessions and machines. The system distinguishes itself through a version-controlled context model, using branches and commits to track how project knowledge evolves. It features a hierarchical knowledge store where information is organized as markdown files and can be synchronized between local env
Creates shared memory environments where teams synchronize project context across different machines.
MIRIX is an AI agent state orchestrator and long-term memory system designed to provide persistent context for large language models. It functions as a multi-modal AI memory pipeline that processes text, voice, and screen captures into structured knowledge stores, including a dedicated screen activity knowledge base. The project distinguishes itself by integrating a multi-modal observation pipeline that monitors desktop activity in real-time to build a searchable history of user actions. It utilizes a multi-tiered memory hierarchy—separating episodic, semantic, procedural, and core stores—and
Integrates a unified memory pipeline across multiple agents to maintain a shared knowledge base.
PromptX is an LLM agent orchestration framework designed to execute multi-step workflows using autonomous agents. It features a sandboxed tool execution environment for secure filesystem operations and external API integrations, alongside a persona management system that defines professional roles and domain expertise to control agent behavior. The system implements a semantic memory network for persistent knowledge storage, utilizing graph-based memory and engrams to retain information across sessions. This cognitive memory includes specialized tools for knowledge graph visualization, allowi
Utilizes a persistent graph-based memory network and engrams to retain cognitive information.