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Agent工作流

UCAgent

基于 Python · 无代码搭建完整 AI 自动化流程
⭐ 165 Stars 🍴 35 Forks 💻 Python 📄 Apache-2.0 🏷 AI 7.5分
7.5AI 综合评分
AI工作流Python
✦ AI Skill Hub 推荐

AI Skill Hub 推荐使用:UCAgent 是一款优质的Agent工作流。AI 综合评分 7.5 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。

📚 深度解析
UCAgent 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

UCAgent 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 7.5 分,是同类 Agent 工作流中的精选推荐。
📋 工具概览

UCAgent 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。

GitHub Stars
⭐ 165
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
Apache-2.0
AI 综合评分
7.5 分
工具类型
Agent工作流
Forks
35
📖 中文文档
以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

UCAgent 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。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:pip 安装(推荐)
pip install ucagent

# 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 ucagent

# 方式三:从源码安装(获取最新功能)
git clone https://github.com/XS-MLVP/UCAgent
cd UCAgent
pip install -e .

# 验证安装
python -c "import ucagent; print('安装成功')"
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
ucagent --help

# 基本用法
ucagent input_file -o output_file

# Python 代码中调用
import ucagent

# 示例
result = ucagent.process("input")
print(result)
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# ucagent 配置文件示例(config.yml)
app:
  name: "ucagent"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
ucagent --config config.yml

# 或通过环境变量配置
export UCAGENT_API_KEY="your-key"
export UCAGENT_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 82/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

UCAgent (UnityChip Verification Agent)

AI-powered automated UT verification agent based on large language models

中文介绍 | UCAgent Online Documentation

Introduction

UCAgent is an automated hardware verification AI agent based on large language models, focusing on Unit Test verification for chip design. It automatically analyzes hardware designs, generates test cases, executes verification tasks, and produces test reports through AI technology, thereby improving verification efficiency.

Key Features:

  • Automated chip verification workflow
  • Support for functional coverage and code coverage analysis
  • Consistency assurance among documentation, code, and reports
  • Deep collaboration with mainstream Code Agents (OpenHands, Copilot, Claude Code, Gemini-CLI, Qwen-Code, etc.) via MCP protocol
  • Three intelligent interaction modes (standard, enhanced, advanced)

For more details, please refer to UCAgent Online Documentation

---

More Features

  1. Launch page: create workspace, upload/import files, parse modules, compile, and preview launch command.
  2. Task page: filter, paginate, inspect task details/logs, and stop/delete managed tasks.
  3. Enhanced Agent page: stage multi-select and bulk toggles (HM/Skip/LFail/LPass), plus stage artifact content/diff review.
  4. Unified proxy access: Master proxies cmd/terminal/web-console paths for both task and agent entries.
  5. Improved Web Terminal: multiple terminal sessions across different URLs.
📖 Detailed Operations: See TUI Usage Documentation

---

System Requirements

  • Python 3.11+
  • Supported OS: Linux, macOS
  • Memory: 4GB+ recommended
  • Network: Access to AI model API (OpenAI compatible)
  • picker: https://github.com/XS-MLVP/picker

---

2. Install Dependencies

pip3 install -r requirements.txt

requires configuration: OPENAI_API_BASE and other environment variables

Edit a custom file to export environment variables required by ucagent, for example:

1. Initial setup: Install dependencies

make docs-install

Dependencies

  • pandoc
  • XeLaTeX (TexLive)
  • Chinese font "Noto Serif CJK SC"
  • Monospace font (default DejaVu Sans Mono)
  • Optional filter pandoc-crossref

3. Install and Configure qwen

Please refer to https://qwenlm.github.io/qwen-code-docs/en/ to install qwen-code-cli, then configure the MCP Server as shown below.

Example ~/.qwen/settings.json:

{
    "mcpServers": {
           "unitytest": {
            "httpUrl": "http://localhost:5000/mcp",
            "timeout": 300000
        }
    }
}

Since running test cases may take a long time, it is recommended to set a larger timeout value, for example 300 seconds.

For other Code Agents, please refer to their documentation, e.g., claude code, opencode, copilot-cli, kilo-cli, iflow, etc.

Or, if ucagent is installed, you can directly run ucagent to start master mode

ucagent --as-master-persist --as-master ```

Then visit http://localhost:8800 in your browser.

Docker Startup

docker run -it --rm \
  -e OPENAI_API_BASE=<your_openai_api_base> \
  -e OPENAI_API_KEY=<your_openai_api_key> \
  -e OPENAI_MODEL=<your_openai_model> \
  -p 8800:8800 \
  ghcr.io/xs-mlvp/ucagent:latest ucagent --as-master-persist --as-master

If ghcr.io is not accessible, you can directly replace it with mirror addresses such as ghcr.nju.edu.cn.

After successful startup, visit http://localhost:8800 in your browser.

Documentation Build and Preview (MkDocs)

The Makefile provides documentation-related helper targets (MkDocs + Material):

TargetPurposeUse Case
make docs-helpShow documentation-related target helpView available commands
make docs-installInstall build dependencies from docs/requirements-docs.txtFirst use or dependency updates
make docs-serveLocal preview (default 127.0.0.1:8030)Develop and preview docs
make docs-buildBuild static site to docs/siteGenerate production version
make docs-cleanDelete docs/site directoryClean build artifacts

3. Local generation: Build production version

make docs-build # Generate docs/site directory

PDF Manual Build (Pandoc + XeLaTeX)

For generating high-quality developer PDF manuals:

TargetPurpose
make pdfGenerate ucagent-doc.pdf from ordered Markdown sources
make pdf-oneEquivalent to pdf (convenient for CI calls)
make pdf-cleanClean generated PDF and LaTeX temporary files

Quick Start

Usage Flow

First-time use (install dependencies):

make docs-install    # Install mkdocs and material theme dependencies

Daily development (preview documentation):

```bash make docs-serve # Start local server, visit http://127.0.0.1:8030

Complete Workflow Example

```bash

Examples

make pdf
make MONO="JetBrains Mono" pdf      # Override monospace font
make TWOSIDE=1 pdf                   # Two-sided layout (adds -twoside to filename)
make pdf-clean

5. How to Improve Verification Quality (Optional)

By default, UCAgent only enables the internal Python Checker for stage checking, which is heuristic. If you need verification quality improvement, you can enable LLM stage checking. If you need to reach "delivery level" quality, you further need to enable Human stage checking.

  1. Enable LLM stage checking
  1. Enable human stage checking

Default stage checking order: Python Checker -> LLM -> Human

---

Then load the environment variables

source ~/.ucagent_env


#### 2. Start UCAgent Master
bash make as_master_persist

4. Cleanup (optional)

make docs-clean # Delete docs/site directory ```

Interact via Web Interface

UCAgent provides Master mode, based on which you can perform centralized Agent management, create tasks, view status, use online terminals, and other operations through the web interface.

export OPENAI_API_BASE=<your_openai_api_base>

export OPENAI_API_KEY=<your_openai_api_key>

Frequently Asked Questions (FAQ)

Q: How to configure different AI models?

A: Modify the openai.model_name field in config.yaml, which supports any OpenAI-compatible API. See Configuration Documentation.

Q: What to do when errors occur during verification?

A: Use Ctrl+C to enter interactive mode, check current status with status, and use help to get debugging commands.

Q: MCP server cannot connect?

A: Check if the port is occupied, verify firewall settings, and you can specify a different port with --mcp-server-port.

Q: Why is there information from the last execution?

A: UCAgent by default looks for the .ucagent/ucagent_info.json file in the working directory to load previous execution information and continue. If you don't need history, delete this file or use the --no-history parameter to ignore loading history.

Q: How to run long-duration verification?

A: Please refer to CodeAgent's custom backend mode examples/CustomBackend/README.md.

Q: Can verification stages be customized?

A: Yes, see Customization Documentation.

Q: How to add custom tools?

A: Create a new tool class in the ucagent/tools/ directory, inherit from the UCTool base class, and load it with the --ex-tools parameter. See Tool List Documentation.

🔍 More Questions: Check the complete FAQ Documentation

---

🎯 aiskill88 AI 点评 A 级 2026-05-27

高质量的开源AI工作流项目

⚡ 核心功能
👥 适合人群
自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队
🎯 使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
✅ 优点
  • +Apache-2.0 协议,可免费商用
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

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。

📄 License 说明

✅ 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。

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❓ 常见问题 FAQ
Modify the `openai.model_name` field in `config.yaml`, which supports any OpenAI-compatible API. See [Configuration Documentation](https://ucagent.open-verify.cc/content/02_usage/01_direct/).
💡 AI Skill Hub 点评

总体来看,UCAgent 是一款质量良好的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ Apache-2.0 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 UCAgent
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 UCAgent
原始描述 开源AI工作流:UnityChip Verification AI-Agent。⭐165 · Python
Topics AI工作流Python
GitHub https://github.com/XS-MLVP/UCAgent
License Apache-2.0
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/XS-MLVP/UCAgent 🌐 官方网站  https://ucagent.open-verify.cc

收录时间:2026-05-27 · 更新时间:2026-05-27 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。