TencentCloud/Octop
A smarter, self-hosted AI assistant — multi-user, multi-agent.
What it solves
Octop is a self-hosted AI assistant platform designed for individuals, families, and small teams. It addresses the need for a private, multi-user AI environment where users can maintain their own specialized agents and data locally on their own machine, avoiding vendor lock-in and privacy compromises.
How it works
Octop runs as a single process that integrates a web dashboard, a command-line interface (CLI), and various instant messaging (IM) channels. It is built on the "Harness" stack, which provides the agent runtime, a multi-platform IM bridge, hierarchical memory, and browser automation. All user data, including conversations, workspaces, and credentials, is stored in a local SQLite database and directory structure under ~/.octop/.
Who it’s for
It is intended for users who want a fully self-hosted AI assistant with multi-user support, those who need to integrate AI into their existing communication tools (like Discord or Feishu), and developers who want to extend AI capabilities via the Agent Client Protocol (ACP) for IDE integration.
Highlights
- Multi-user Expert Team: Supports an admin and shared household with a built-in expert library to switch specialists per scenario.
- Extensive IM Integration: Connects with Feishu, DingTalk, QQ, Discord, and WeCom.
- Local-First Security: Features JWT multi-user isolation, shell command guardrails, and PII redaction to keep data local.
- Advanced AI Capabilities: Includes headless Chromium sessions for web automation, remote desktop access for GUI apps, and an interactive shell in the browser.
- ACP Bidirectional Support: Allows external tools to use Octop agents or delegates tasks to external coding agents like Claude Code or OpenCode.
- Flexible Backends: Supports local disk, Docker containers, PostgreSQL, and S3/COS for workspace storage.
Related
- Project
modelscope/ms-agentMS‑Agent is a lightweight Python framework for building autonomous LLM agents with tool‑calling, memory, multimodal input, and a skill‑retrieval system. It uses ModelScope’s Model Calling Protocol (MCP) for LLM access, provides a React/FastAPI web UI, and includes ready‑made projects for code generation, short video creation, and deep‑research workflows.
- Project
Mininglamp-OSS/octo-cliA command-line interface for AI Agent bots to interact with the Octo ecosystem via structured JSON outputs and OpenAPI-driven commands.
- Project
ArtificialAnalysis/StirrupStirrup is a lightweight Python framework for building LLM‑driven agents. It supplies ready‑made tools (code execution, web search, file I/O, multimodal handling) and a flexible `Tool`/`ToolProvider` system, while automatically managing context limits and session lifecycles. Install via `pip install stirrup` (optional extras for Docker, browsers, etc.), create a client (OpenRouter, LiteLLM, or any OpenAI‑compatible API), instantiate an `Agent`, and run a session that lets the model invoke tools to solve tasks. Custom tools and providers are easy to add, making Stirrup suitable for rapid prototyping, domain‑specific assistants, and research on tool‑using agents.
- Project
yuruotong1/autoMateautoMate is a locally hosted service that gives AI chat clients persistent personal storage (notes, files, reminders, long‑term memory) and a toolbox of real‑world actions (shell, browser, SaaS APIs). It talks to clients via an MCP‑over‑HTTP bridge, works with OpenClaw, Claude Desktop, Cursor, Cline, etc., and can also be used through its own web chat, Chrome extension, Docker container, or binary.
- Project
jaylfc/taOStaOS is an open‑source, self‑hosted AI platform that provides a browser‑based desktop, a distributed compute cluster, and a local memory service (taOSmd) for AI agents. It runs on any hardware (PCs, SBCs, phones), lets you install and manage agents from 17 frameworks, and keeps all conversations, files and knowledge graphs on your own machines, with cloud models optional.