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HugAgentOS

ZJU's self-hosted enterprise AgentOS on AgentScope 2.0: domain ontology as a control plane for agents, plus RAG, sub-agents, MCP, skills, sandbox, memory and approval-gated self-evolution.

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Curator's take

Pick HugAgentOS when the pitch 'ontology as a machine-executable control plane' matters to you - governed concepts, relations, rules and action contracts feed the skill, memory and orchestration engines one shared business vocabulary, and self-evolution (memory, skills, orchestration) only lands after you approve it. One-command install with SQLite, Docker Compose for Postgres/Redis and a real sandbox. Caveats: the repo is a generated mirror of an upstream (`src/**` PRs not accepted), the licence is Apache-2.0 *plus* supplementary terms, and the Community Edition has no self-registration. If you don't care about ontology governance, maxkb or claraverse are more mature self-hosted workspaces; if you only want the ontology layer, context-ontology-accelerator or semantica are lighter.

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README.md2 min read

HugAgentOS logo

HugAgentOS: The Enterprise AgentOS for Ontology-Grounded Trustworthy Reasoning

The open-source, self-hosted foundation for enterprise AI agents

Give models the context and tools to retrieve knowledge, work with files, run code, and carry real tasks through to completion.

HugAgentOS capability overview poster

English · 简体中文

Website · Try HugAgentOS online

Apache 2.0 with supplementary terms Community Edition One-command installation AgentScope 2.0 Model Context Protocol

HugAgentOS is an enterprise-grade AgentOS that treats domain ontology as a control plane for agent reasoning, decisions, and actions. Its open-source Community Edition combines agentic chat, private knowledge-base RAG, sub-agents, MCP tools, Agent Skills, sandboxed execution, long-term memory, automation, and a data canvas in one self-hosted workspace.

[!NOTE] This Community repository is generated from the upstream main repository for each release and is marked generated. Report changes to src/** through an Issue or Discussion. Pull requests for documentation and examples are welcome. See CONTRIBUTING.md for details.

Demo

A 60-second tour: the same task run twice, with audited self-evolution in between — memory, skills and orchestration each settled, and each taking effect only after you approve it.

https://github.com/user-attachments/assets/635c5b35-10c0-47b9-a6f1-c73df9510e90

Quick start

Use the one-command install to try it out, or Docker Compose for a long-running service with isolation. Either way you need an OpenAI-compatible or local model. The initial account and password are both admin and must be changed on first sign-in; the Community Edition has no self-registration.

Option 1: one-command install (Linux / macOS / WSL2)

Requires Python 3.11+, Node.js 20+, Git and curl. No Docker, PostgreSQL or Redis needed.

curl -fsSL https://raw.githubusercontent.com/ZJU-REAL/HugAgentOS/main/install.sh | bash

The installer fetches the source into ~/.hugagent/source, creates an isolated Python environment, builds the web application and walks you through first-run setup; it then opens http://127.0.0.1:3001. Start it again later with ~/.hugagent/venv/bin/hugagent.

[!WARNING] The installation listens on 127.0.0.1 only. If you genuinely need remote access, use hugagent serve --host 0.0.0.0 --port 3001 --no-browser, and set a strong password, a firewall and HTTPS first. Do not expose it on an untrusted network.

This path uses SQLite, in-process state and a local subprocess sandbox, which suits personal use and developmen