by zhnt
AI-native agent harness for coding workflows by python: multi-model LLM orchestration, stateful sessions, tool governance, traceable delivery, and provider routing for GPT, Claude, DeepSeek, Qwen, Kimi, GLM, and MiniMax.
# Add to your Claude Code skills
git clone https://github.com/zhnt/loushangLast scanned: 6/16/2026
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"scannedAt": "2026-06-16T09:27:01.602Z",
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}See how loushang compares with popular alternatives.
loushang is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by zhnt. AI-native agent harness for coding workflows by python: multi-model LLM orchestration, stateful sessions, tool governance, traceable delivery, and provider routing for GPT, Claude, DeepSeek, Qwen, Kimi, GLM, and MiniMax. It has 1,441 GitHub stars.
Yes. loushang passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/zhnt/loushang" and add it to your Claude Code skills directory (see the Installation section above).
loushang is primarily written in Python. It is open-source under zhnt on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh loushang against similar tools.
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English | 中文
Loushang is a method-native AI work system for running complex work from intent to verified delivery.
Current focus: loushang code, a CLI and terminal workbench for software development with model routing, persistent sessions, tools, extensions, and method-guided delivery.
Modern AI agents can plan and act, but complex work still breaks down when context is lost, execution cannot be resumed, tools are hard to govern, and results are not verified.
Loushang treats methods, stages, roles, tools, sessions, and work products as runtime objects. The goal is not just to make agents smarter, but to make complex work more reliable, recoverable, auditable, and deliverable.
Method is the work contract, work is the runtime fact, agent is the execution kernel, ai is the model access layer, harness is the cross-product substrate, coding is the V1 product surface, tui is the terminal presentation, and channel is the boundary protocol—together organizing complex knowledge work into a runnable, recoverable, verifiable, and evolvable system.
loushang code: a coding-focused CLI and terminal workbench.loushang.ai: a provider-aware AI SDK with model registry, streaming, tool calls, and cost helpers.Loushang is in early development. The recommended path is to run it from source.
git clone https://github.com/zhnt/loushang.git
cd loushang
uv venv .venv
source .venv/bin/activate
uv pip install -e ".[dev]"
loushang --help
loushang --list-models
loushang --list-commands
loushang -p "Inspect this repository and summarize what it does."
You can also run make bootstrap, which creates .venv/ with uv and installs the project in editable development mode. The Makefile does not currently provide a make install target; use make bootstrap for local development or make install-binary for a local binary install.
For local development in this repository, use the project virtual environment in .venv/.
loushang code as the primary product surface for software development work.loushang work as a personal complex-work workbench, with code, research, and ppt as specialized flows.Loushang is in active early development.
The current stable focus is loushang code and the underlying loushang.ai SDK. Broader work surfaces such as loushang work, loushang research, and loushang ppt are part of the roadmap and should be treated as evolving product directions.
Loushang was initiated by Heng Zhou. He has long worked across low-code systems, workflows, databases, model-driven engineering, DSLs, architecture methods, systems engineering, and artificial intelligence, with a focus on operationalizing ontology and methodology into infrastructure for complex-work delivery.
For questions, feedback, collaboration, or a community group invitation, contact: zhnt@foxmail.com.
Loushang learns from public design and engineering patterns in projects such as OpenAI Codex, pi, python-prompt-toolkit, browser-use, Kimi CLI, superpowers, gstack, openclaw, and hermes-agent. These projects are references and inspiration; unless listed in THIRD_PARTY_NOTICES.md, this repository does not include or redistribute their code.
Loushang is licensed under the Apache License 2.0 unless a file states otherwise.
When redistributing source code, binaries, documents, or modified versions, keep LICENSE and NOTICE, and retain attribution in product documentation, About/Credits pages, or equivalent third-party notices.
Third-party dependency information is available in THIRD_PARTY_NOTICES.md.