holaOS Unifies AI Agents in a Local-First Workspace with Shared Memory 🔗
Runs Claude Code, Codex, or custom agents side-by-side using shared context and 100+ tool integrations
holaOS provides a local-first AI agent workspace where developers can run multiple agents — including Claude Code, Codex, and its own holaOS agent — simultaneously without context switching. Built on TypeScript and Electron, it offers shared memory, tools, skills, and app integrations across all agents, ensuring consistent behavior regardless of which model is driving the task.
The workspace supports over 100 integrations via the Model Context Protocol (MCP), allowing agents to interact with files, browsers, and external apps as interactive surfaces. Memory is stored locally as plain, editable files, enabling durable context across sessions and agent switches — users can close the app and return days later to find the agent aware of prior work. Frontier models like GPT 5.6, Claude Opus 5, and Fable 5 are built in for heavy lifting, while efficient models like Kimi K3 and GLM 5.2 handle routine tasks. Users can also bring their own keys (BYOK) for OpenAI, Anthropic, or compatible endpoints, keeping costs and usage tied to their personal accounts. Agents install from an in-workspace marketplace and run as live, interactive panels where users can step in, observe, and edit outputs directly — results appear in place, not as detached chat logs.
The catch: Despite active development, the project has only seven open issues and a narrow scope focused on desktop agents, leaving open questions about its viability in team workflows, mobile environments, or production-grade agent orchestration at scale.
Why this leads today It offers a unified, open-source platform for running and coordinating AI agents across tools and environments with shared memory, addressing a fragmented workflow developers currently face.
- Developers testing agent performance across coding tasks
- Power users automating multi-tool workflows with persistent context
- Researchers comparing LLM behaviors in shared environments
Source: holaboss-ai/holaOS — based on the README and release notes.