经 AI Skill Hub 精选评估,Voltagent 获评「强烈推荐」。已获得 9.2k 颗 GitHub Star,这款MCP工具在功能完整性、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。
Voltagent 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
Voltagent 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/VoltAgent/voltagent
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"voltagent": {
"command": "npx",
"args": ["-y", "voltagent"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 Voltagent 执行以下任务... Claude: [自动调用 Voltagent MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"voltagent": {
"command": "npx",
"args": ["-y", "voltagent"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
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VoltAgent is an end-to-end AI Agent Engineering Platform that consists of two main parts:
Cloud Self-Hosted – Observability, Automation, Deployment, Evals, Guardrails, Prompts, and more.Build agents with full code control and ship them with production-ready visibility and operations.
With the open-source framework, you can build intelligent agents with memory, tools, and multi-step workflows while connecting to any AI provider. Create sophisticated multi-agent systems where specialized agents work together under supervisor coordination.
@voltagent/core): Define agents with typed roles, tools, memory, and model providers in one place so everything stays organized.You can use the MCP server @voltagent/mcp-docs-server to teach your LLM how to use VoltAgent for AI-powered coding assistants like Claude, Cursor, or Windsurf. This allows AI assistants to access VoltAgent documentation, examples, and changelogs directly while you code.
Design, test, and refine prompts directly in the console.
<img alt="prompts" src="https://github.com/user-attachments/assets/fb6d71eb-8f81-4443-a494-08c33ec9bcc4" />
Deploy your agents to production with one-click GitHub integration and managed infrastructure.
<img alt="deployment" src="https://github.com/user-attachments/assets/e329ab4b-7464-435a-96cc-90214e8a3cfa" />
Create a new VoltAgent project in seconds using the create-voltagent-app CLI tool:
npm create voltagent-app@latest
This command guides you through setup.
You'll see the starter code in src/index.ts, which now registers both an agent and a comprehensive workflow example found in src/workflows/index.ts.
import { VoltAgent, Agent, Memory } from "@voltagent/core";
import { LibSQLMemoryAdapter } from "@voltagent/libsql";
import { createPinoLogger } from "@voltagent/logger";
import { honoServer } from "@voltagent/server-hono";
import { openai } from "@ai-sdk/openai";
import { expenseApprovalWorkflow } from "./workflows";
import { weatherTool } from "./tools";
// Create a logger instance
const logger = createPinoLogger({
name: "my-agent-app",
level: "info",
});
// Optional persistent memory (remove to use default in-memory)
const memory = new Memory({
storage: new LibSQLMemoryAdapter({ url: "file:./.voltagent/memory.db" }),
});
// A simple, general-purpose agent for the project.
const agent = new Agent({
name: "my-agent",
instructions: "A helpful assistant that can check weather and help with various tasks",
model: openai("gpt-4o-mini"),
tools: [weatherTool],
memory,
});
// Initialize VoltAgent with your agent(s) and workflow(s)
new VoltAgent({
agents: {
agent,
},
workflows: {
expenseApprovalWorkflow,
},
server: honoServer(),
logger,
});
Afterwards, navigate to your project and run:
npm run dev
When you run the dev command, tsx will compile and run your code. You should see the VoltAgent server startup message in your terminal:
══════════════════════════════════════════════════
VOLTAGENT SERVER STARTED SUCCESSFULLY
══════════════════════════════════════════════════
✓ HTTP Server: http://localhost:3141
Test your agents with VoltOps Console: https://console.voltagent.dev
══════════════════════════════════════════════════
Your agent is now running! To interact with it:
For more examples, visit our examples repository.
VoltOps Console is the platform side of VoltAgent, providing observability, automation, and deployment so you can monitor and debug agents in production with real-time execution traces, performance metrics, and visual dashboards.
Your new project also includes a powerful workflow engine.
The expense approval workflow demonstrates human-in-the-loop automation with suspend/resume capabilities:
import { createWorkflowChain } from "@voltagent/core";
import { z } from "zod";
export const expenseApprovalWorkflow = createWorkflowChain({
id: "expense-approval",
name: "Expense Approval Workflow",
purpose: "Process expense reports with manager approval for high amounts",
input: z.object({
employeeId: z.string(),
amount: z.number(),
category: z.string(),
description: z.string(),
}),
result: z.object({
status: z.enum(["approved", "rejected"]),
approvedBy: z.string(),
finalAmount: z.number(),
}),
})
// Step 1: Validate expense and check if approval needed
.andThen({
id: "check-approval-needed",
resumeSchema: z.object({
approved: z.boolean(),
managerId: z.string(),
comments: z.string().optional(),
adjustedAmount: z.number().optional(),
}),
execute: async ({ data, suspend, resumeData }) => {
// If we're resuming with manager's decision
if (resumeData) {
return {
...data,
approved: resumeData.approved,
approvedBy: resumeData.managerId,
finalAmount: resumeData.adjustedAmount || data.amount,
};
}
// Check if manager approval is needed (expenses over $500)
if (data.amount > 500) {
await suspend("Manager approval required", {
employeeId: data.employeeId,
requestedAmount: data.amount,
});
}
// Auto-approve small expenses
return {
...data,
approved: true,
approvedBy: "system",
finalAmount: data.amount,
};
},
})
// Step 2: Process the final decision
.andThen({
id: "process-decision",
execute: async ({ data }) => {
return {
status: data.approved ? "approved" : "rejected",
approvedBy: data.approvedBy,
finalAmount: data.finalAmount,
};
},
});
You can test the pre-built expenseApprovalWorkflow directly from the VoltOps console:
1. Go to the Workflows Page: After starting your server, go directly to the Workflows page. 2. Select Your Project: Use the project selector to choose your project (e.g., "my-agent-app"). 3. Find and Run: You will see "Expense Approval Workflow" listed. Click it, then click the "Run" button. 4. Provide Input: The workflow expects a JSON object with expense details. Try a small expense for automatic approval:
{
"employeeId": "EMP-123",
"amount": 250,
"category": "office-supplies",
"description": "New laptop mouse and keyboard"
}
5. View the Results: After execution, you can inspect the detailed logs for each step and see the final output directly in the console.
高质量的AI代理工程平台
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。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:Voltagent 的核心功能完整,质量优秀。对于Claude Desktop / Claude Code 用户来说,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 | voltagent |
| 原始描述 | 开源MCP工具:AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framew。⭐9.2k · TypeScript |
| Topics | aiagentstypescript |
| GitHub | https://github.com/VoltAgent/voltagent |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-05-25 · 更新时间:2026-05-26 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端