经 AI Skill Hub 精选评估，Clawith 获评「强烈推荐」。已获得 4.0k 颗 GitHub Star，这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.5 分，适合有一定技术背景的用户使用。
Clawith 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
Clawith 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一：pip 安装（推荐）
pip install clawith
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install clawith
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/dataelement/Clawith
cd Clawith
pip install -e .
# 验证安装
python -c "import clawith; print('安装成功')"
# 命令行使用
clawith --help
# 基本用法
clawith input_file -o output_file
# Python 代码中调用
import clawith
# 示例
result = clawith.process("input")
print(result)
# clawith 配置文件示例（config.yml） app: name: "clawith" debug: false log_level: "INFO" # 运行时指定配置文件 clawith --config config.yml # 或通过环境变量配置 export CLAWITH_API_KEY="your-key" export CLAWITH_OUTPUT_DIR="./output"
<p align="center"> <img src="assets/slogan.png" alt="Clawith — OpenClaw for Teams" width="800" /> </p>
<p align="center"> <a href="https://www.clawith.ai/blog/clawith-technical-whitepaper"><img src="https://img.shields.io/badge/Technical%20Whitepaper-Read-8A2BE2" alt="Technical Whitepaper" /></a> <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="Apache 2.0 License" /></a> <a href="https://github.com/dataelement/Clawith/stargazers"><img src="https://img.shields.io/github/stars/dataelement/Clawith?style=flat&color=gold" alt="GitHub Stars" /></a> <a href="https://github.com/dataelement/Clawith/network/members"><img src="https://img.shields.io/github/forks/dataelement/Clawith?style=flat&color=slateblue" alt="GitHub Forks" /></a> <a href="https://github.com/dataelement/Clawith/commits/main"><img src="https://img.shields.io/github/last-commit/dataelement/Clawith?style=flat&color=green" alt="Last Commit" /></a> <a href="https://github.com/dataelement/Clawith/graphs/contributors"><img src="https://img.shields.io/github/contributors/dataelement/Clawith?style=flat&color=orange" alt="Contributors" /></a> <a href="https://github.com/dataelement/Clawith/issues"><img src="https://img.shields.io/github/issues/dataelement/Clawith?style=flat" alt="Issues" /></a> <a href="https://x.com/ClawithHQ"><img src="https://img.shields.io/badge/𝕏-Follow-000000?logo=x&logoColor=white" alt="Follow on X" /></a> <a href="https://discord.gg/NRNHZkyDcG"><img src="https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white" alt="Discord" /></a> </p>
<p align="center"> <a href="README.md">English</a> · <a href="README_zh-CN.md">中文</a> · <a href="README_ja.md">日本語</a> · <a href="README_ko.md">한국어</a> · <a href="README_es.md">Español</a> · <a href="README_ar.md">العربية</a> </p>
<p align="center"> <strong>Live Demo:</strong> <a href="https://try.clawith.ai">try.clawith.ai</a> — open-source feature preview; shared demo environment, not guaranteed stable. <br /> <strong>Clawith Cloud:</strong> <a href="https://cloud.clawith.ai">cloud.clawith.ai</a> — hosted production service. </p>
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Clawith is an open-source multi-agent collaboration platform. Unlike single-agent tools, Clawith gives every AI agent a persistent identity, long-term memory, and its own workspace — then lets them work together as a crew, and with you.
Agents can discover and install new tools at runtime (Smithery + ModelScope), and create new skills for themselves or colleagues.
Note: Clawith does not run any AI models locally — all LLM inference is handled by external API providers (OpenAI, Anthropic, etc.). The local deployment is a standard web application with Docker orchestration.
| Scenario | CPU | RAM | Disk | Notes |
|---|---|---|---|---|
| Personal trial / Demo | 1 core | 2 GB | 20 GB | Use SQLite, skip Agent containers |
| Full experience (1–2 Agents) | 2 cores | 4 GB | 30 GB | ✅ Recommended for getting started |
| Small team (3–5 Agents) | 2–4 cores | 4–8 GB | 50 GB | Use PostgreSQL |
| Production | 4+ cores | 8+ GB | 50+ GB | Multi-tenant, high concurrency |
git clone https://github.com/dataelement/Clawith.git
cd Clawith
bash setup.sh # Production: installs runtime dependencies only (~1 min)
bash setup.sh --dev # Development: also installs pytest and test tools (~3 min)This will: 1. Create .env from .env.example 2. Set up PostgreSQL — uses an existing instance if available, or automatically downloads and starts a local one 3. Install backend dependencies (Python venv + pip) 4. Install frontend dependencies (npm) 5. Create database tables and seed initial data (default company, templates, skills, etc.)
Note: If you want to use a specific PostgreSQL instance, create a.envfile and setDATABASE_URLbefore runningsetup.sh:> DATABASE_URL=postgresql+asyncpg://user:pass@localhost:5432/clawith?ssl=disable >
Then start the app:
```bash bash restart.sh
```bash git clone https://github.com/dataelement/Clawith.git cd Clawith && cp .env.example .env docker compose up -d
If git clone is slow or times out:
| Solution | Command |
|---|---|
| **Shallow clone** (download only latest commit) | git clone --depth 1 https://github.com/dataelement/Clawith.git |
| **Download release archive** (no git needed) | Go to [Releases](https://github.com/dataelement/Clawith/releases), download .tar.gz |
| **Use a git proxy** (if you have one) | git config --global http.proxy socks5://127.0.0.1:1080 |
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高质量的开源AI工作流平台，支持多智能体和LLM
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
AI Skill Hub 点评：Clawith 的核心功能完整，质量优秀。对于自动化工程师和运维人员来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | Clawith |
| Topics | AI工作流智能体LLM |
| GitHub | https://github.com/dataelement/Clawith |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-06-07 · 更新时间：2026-06-07 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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