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🔌 MCP 🤖 Agent 🔄 工作流 🌐 翻译 🔗 REST API 🧬 Embedding 📚 RAG 🖼 视觉 🧠 Claude ✨ GPT

⚙️

Agent工作流

AI 工作流工具

基于 Java · 无代码搭建完整 AI 自动化流程

英文名：Tools4AI

⭐ 183 Stars 🍴 37 Forks 💻 Java 📄 MIT 🏷 AI 7.5分

7.5AI 综合评分

aijavageminianthropic

⬇ 下载源码 ZIP ⚙️ 配置说明

✦ AI Skill Hub 推荐

AI Skill Hub 推荐使用：AI 工作流工具是一款优质的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

AI 工作流工具是一套完整的 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。

AI 工作流工具工作流的设计遵循"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

AI 工作流工具是一套完整的 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

⭐ 183

开发语言

Java

支持平台

Windows / macOS / Linux / Android

维护状态

轻量级项目，按需更新

开源协议

MIT

AI 综合评分

7.5 分

工具类型

Agent工作流

Forks

📖 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」。

📌 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

# 克隆仓库
git clone https://github.com/vishalmysore/Tools4AI
cd Tools4AI

# 查看安装说明
cat README.md

# 按 README 完成环境依赖安装后即可使用

📋 安装步骤说明

访问 GitHub 仓库获取工作流文件
在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
上传工作流文件
按照提示配置必要的环境变量和 API Key
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

# 查看帮助
tools4ai --help

# 基本运行
tools4ai [options] <input>

# 详细使用说明请查阅文档
# https://github.com/vishalmysore/Tools4AI

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

# tools4ai 配置说明
# 查看配置选项
tools4ai --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量（覆盖配置文件）
export TOOLS4AI_CONFIG="/path/to/config.yml"

📑 README 深度解析真实文档完整度 57/100 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。

💡 Tools4AI

Tools4AI is 100% Java based Agentic AI Framework and light weight ADK library which can be used to build Java based AI agents for integration with enterprise Java applications. It can be used to build AI agents in A2A , MCP, A2UI, UCP and other protocols. This project illustrates the integration of AI Agents with enterprise tools or external tools, converting natural language prompts into <span style="font-size: larger;">agent actions.</span> These prompts can be called <span style="font-size: larger;">"actionble prompts"</span> or <span style="font-size: larger;">"agent prompts"</span> By leveraging AI capabilities, it streamlines user interactions with complex systems, enhancing productivity and innovation across diverse applications.<br>

For example , we can integrate AI Agent with a customer service application. Users can interact with the AI agent by asking questions or making requests in natural language. For example, a user might ask,"Schedule a maintenance appointment for my car." The AI agent interprets the request, extracts relevant information such as the service required and preferred date, and then triggers the appropriate agent action in the customer service application to schedule the appointment. This seamless integration streamlines the process for users and enhances the efficiency of the customer service workflow through agentic ai automation. <br> <div class="rdm-tbl-wrap"><table class="rdm-tbl"><thead><tr><th>Prompt</th><th>Action</th></tr></thead><tbody><tr><td>Create a <span style="color:blue">new task</span> for the marketing campaign.</td><td>The AI agent interprets the request and generates a new task entry within the project management tool dedicated to the marketing campaign, assigning it relevant details such as priority level, due date, and task description.</td></tr><tr><td>Generate a <span style="color:blue">sales report</span> for the previous <span style="color:blue">quarter</span>.</td><td>The AI agent accesses data from the company's sales database, analyzes the information for the previous quarter, and generates a comprehensive sales report, which is then delivered to the user or stored in the appropriate location for access.</td></tr><tr><td>Check the <span style="color:blue">inventory status</span> of <span style="color:blue">product X.</span></td><td>The AI agent retrieves real-time inventory data for product X from the inventory management system and provides the user with information regarding current stock levels, including quantities available, locations, and any pending orders.</td></tr><tr><td>Schedule a <span style="color:blue">video conference</span> with the engineering team for next Monday at 10 AM.</td><td>The AI agent interfaces with the calendar and scheduling tool, creates a new event titled "Engineering Team Video Conference" for the specified date and time, and sends out meeting invitations to all members of the engineering team.</td></tr><tr><td>Submit a reimbursement request for the <span style="color:blue">business</span> trip <span style="color:blue">expenses.</span></td><td>The AI agent guides the user through the reimbursement request process, collecting necessary details such as expense receipts, dates, amounts, and purpose of expenditure. Once compiled, the system submits the reimbursement request to the appropriate department for processing.</td></tr></tbody></table></div>

Prompt prediction is a technique used to anticipate user actions based on their input prompts. For instance, if a user's prompt is "my car broke down," in addition to the action "bookTaxi," the AI agent can predict a set of subsequent actions such as "bookCarService" and "orderFood" (if it's dinner time). This predictive capability enhances the user experience by proactively suggesting relevant actions or services based on the context provided in the prompt.

Test Number

SetUp

Download source and build from scratch

 clean install

<br>

if you are using Intellij or eclipse make sure you set -parameters option for compiler <br>

Or use as maven dependency

<dependency>
    <groupId>io.github.vishalmysore</groupId>
    <artifactId>tools4ai</artifactId>
    <version>0.9.6</version>
</dependency>

check for latest version here https://repo1.maven.org/maven2/io/github/vishalmysore/tools4ai/

✈️ Reference Examples

OpenAI Gemini Anthropic Gemini1.5Pro

Advanced Reference Examples

This will do a google search and return the result can be combined with multiaction

ActionProcessor processor = new ActionProcessor();
String news = (String)processor.processSingleAction("can you search the web for Indian news");

Guard Rails with Spring security

 Security - Guard Rails using Spring Security

TBD <br>

 Application Checkout and monitoring using with Gemini - Prompt - Check if my restaurant system is up and running and able to book the reservation

TBD <br>

 Validation with Prompt  - Prompt - What happened the the flight booking i made whats the status?

TBD <br>

📌 Rapid Start

🧱 Do you want to start building ASAP , Look at Rapid start here https://github.com/vishalmysore/agenticjava

🌱 Integration of Spring Controller and AI Actions - https://github.com/vishalmysore/SpringActions

Selenium integration

Tools4AI's integration with Selenium introduces a flexible way to automate UI testing. Instead of traditional Java code for Selenium scripts, Tools4AI allows you to define test scenarios in plain English, offering a more accessible approach to testing web applications. These English-based commands can be converted into Selenium code to automate web-based interactions and streamline testing.

Example of Selenium Test with Tools4AI

 
 WebDriver driver = new ChromeDriver(options);
 SeleniumProcessor processor = new SeleniumProcessor(driver);
 processor.processWebAction("go to website https://the-internet.herokuapp.com");
 boolean buttonPresent =  processor.trueFalseQuery("do you see Add/Remove Elements?");
 if(buttonPresent) {
    processor.processWebAction("click on Add/Remove Elements");
    // perform other function in simple english
 } //else {
   // processor.processSingleAction("Create Jira by taking screenshot");
  // }
 processor.processWebAction("go to website https://the-internet.herokuapp.com");
 boolean isCheckboxPresent =  processor.trueFalseQuery("do you see Checkboxes?");
 if(isCheckboxPresent) {
   processor.processWebAction("click on Checkboxes");
   processor.processWebAction("select checkbox 1");
 }

In this example, the

processes commands in plain English and converts them into Selenium actions. This approach allows for complex interactions without manually writing Java code for each test. Tools4AI serves as a bridge between natural language and Selenium, making it easier to automate UI testing in a way that is both efficient and intuitive.

This integration offers substantial benefits for teams looking to streamline their UI validation process. By enabling a more straightforward way to define and execute Selenium scripts, Tools4AI provides a flexible framework for automating Selenium-based tests.

Spring integration

All the action processors have Spring integration as well

 
SpringAnthropicProcessor springAnthropic = new SpringAnthropicProcessor(applicationContext)

 
SpringGeminiProcessor springGemini = new SpringGeminiProcessor();

  
SpringOpenAIProcessor springOpenAI = new SpringOpenAIProcessor();

You can use this for spring injection and it works exactly as all other action processors , only difference is that instead of creating new action beans it will reuse the beans already created by spring

look at the example here https://github.com/vishalmysore/SpringActions

Model Integration Protocol

How to include Spring Boot Chat is here Neurocaster-Server Spring Boot Client via Angular is here Neurocaster-Client

With the Neurocaster client you can connect to any spring application and convert into a chat application with integrated tools.

Connect

🧭 Architecture & Comparison with Spring AI

Tools4AI is a retrofit layer — it makes any existing Java system AI-controllable with minimal code change. Spring AI is a platform for building AI-first apps from scratch on Spring Boot.

Dimension	Tools4AI	Spring AI
Core purpose	Agentic action routing — maps prompts to pre-existing Java methods/REST/shell	AI primitives — chat, embeddings, RAG, vector stores for Spring apps
Entry point	Annotate any existing Java class/method — works without Spring	Built on Spring Boot — Spring context is required
Action discovery	Annotation-driven classpath scan + YAML/JSON config for REST/shell	Manual `@Tool` registration, function callbacks
Parameter mapping	Fully automatic — POJOs, Lists, Maps, arrays populated from prompt	Manual — you define function schemas explicitly
Non-Java actions	First-class shell scripts, Swagger/OpenAPI, HTTP REST — no code needed	Not supported natively
Safety layer	`GuardRails`, `HumanInLoop`, hallucination/bias/fact detectors	Not built in
RAG / Embeddings	Not present	Core feature
Weight	Lightweight, single JAR	Full Spring ecosystem