Tag
Agent Mesh is a shared memory system for coordinating multiple AI agents, featuring a decision log, backlog, and dashboard UI. It allows agents to communicate via a SQLite database and supports agent-agnostic integration.
Scritty provides shared, searchable memory for every AI coding agent.
Macro is a workspace app that unifies your work into one app with shared memory.
This paper introduces MemClaw, a governed shared memory architecture for multi-agent LLM systems, formalizing failure modes like unauthorized leakage and stale propagation, and evaluating the system via the ArgusFleet harness.
A developer built kaeru, an open-source shared memory system for AI agents that allows them to persist context across sessions, share knowledge between different agents and humans, and visualize memory as a 3D galaxy. The tool supports multiple agent frameworks and includes features like time-travel, importance levels, and reasoning trails.
Bun has an open pull request that adds support for shared-memory threads to JavaScriptCore, the JavaScript engine underlying WebKit. This enhancement could improve performance for multi-threaded JavaScript workloads in Bun.
A tweet sharing a naive CUDA softmax implementation using shared memory reduction, noting that reduction is straightforward.
GateMem is a benchmark for evaluating memory governance in multi-principal shared-memory agents, covering utility, access control, and forgetting across medical, office, education, and household domains. Current methods fail to balance all three, showing that reliable shared institutional deployment remains elusive.
Hivemind is an open-source tool that allows multiple AI Coding Agents (e.g., Claude Code, Codex, Cursor) to share a memory layer, automatically mining high-quality patterns from usage trajectories and converting them into reusable skill files, enabling cross-tool and cross-team skill propagation, significantly reducing token consumption and interaction rounds.
Discusses the challenge of persistent memory for personal AI agents across sessions, comparing setups like Custom GPTs, Mem, and Open Campus's shared memory approach, and asks for community recommendations on handling memory conflicts.
Discusses how to establish shared memory among multiple AI agents to avoid repeating mistakes, and introduces a solution by modifying the MemOS CLI to only record key information and search when necessary.
Glen is a shared memory layer for AI agents that enables agents across a company to share knowledge and skills dynamically, with RBAC and integration with tools like Cursor, Claude code, and others.
NetworkChuck announced a massive deployment of Hermes using a full team with isolated VMs and shared memory from Honcho, in collaboration with Nous Research.
A quote tweet discusses gBrain being state-of-the-art for a specific use case, with a shared memory layer architecture under Hermes Agent.
Shann Holmberg describes an experimental architecture using gBrain as a shared memory layer for a team of Hermes Agents, allowing specialists to read from a centralized brain before acting and write durable context back.
RoBrain is an open-source shared memory layer for AI coding teams that captures technical decisions, rationale, and rejected alternatives across sessions and tools like Claude Code, Cursor, and Copilot, preventing agents from repeating past mistakes.
Details a method for connecting AI agents (e.g., Hermes and OpenClaw) to a shared brain that stores decisions and logs, enabling agents to search and reuse past context rather than starting from scratch.
RoBrain is a shared AI memory product that prevents agents from repeating mistakes.
Introduces Queryable LoRA, a data-adaptive method for efficient fine-tuning that uses a shared memory of low-rank update atoms with attention-based routing and instruction regularization to enable dynamic, context-sensitive parameter updates while maintaining scalability.
Hivemind is an open-source tool from Activeloop/Deeplake that provides auto-learning, cloud-backed shared memory for AI coding agents. It captures traces, codifies patterns, and propagates skills across agents, achieving 25% cost savings and 1.7× fewer tokens on the LoCoMo benchmark.