voltagent

<div align="center"> <a href="https://voltagent.dev/"> <img width="1500" height="276" alt="voltagent" src="https://github.com/user-attachments/assets/d9ad69bd-b905-42a3-81af-99a0581348c0" /> </a> <h3 align="center"> AI Agent Engineering Platform </h3> <div align="center"> English | <a href="i18n/README-cn-traditional.md">繁體中文</a> | <a href="i18n/README-cn-bsc.md">简体中文</a> | <a href="i18n/README-jp.md">日本語</a> | <a href="i18n/README-kr.md">한국어</a> </div> <br/> <div align="center"> <a href="https://voltagent.dev">Home Page</a> | <a href="https://voltagent.dev/docs/">Documentation</a> | <a href="https://github.com/voltagent/voltagent/tree/main/examples">Examples</a> </div> </div> <br/> <div align="center">

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VoltAgent is an end-to-end AI Agent Engineering Platform that consists of two main parts:

• Open-Source TypeScript Framework – Memory, RAG, Guardrails, Tools, MCP, Voice, Workflow, and more.
• VoltOps Console 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.

<h2 id="core-framework">Core TypeScript Framework</h2>

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.

• Core Runtime (@voltagent/core): Define agents with typed roles, tools, memory, and model providers in one place so everything stays organized.
• Workflow Engine: Describe multi-step automations declaratively rather than stitching together custom control flow.
• Supervisors & Sub-Agents: Run teams of specialized agents under a supervisor runtime that routes tasks and keeps them in sync.
• Tool Registry & MCP: Ship Zod-typed tools with lifecycle hooks and cancellation, and connect to Model Context Protocol servers without extra glue code.
• LLM Compatibility: Swap between OpenAI, Anthropic, Google, or other providers by changing config, not rewriting agent logic.
• Memory: Attach durable memory adapters so agents remember important context across runs.
• Resumable Streaming: Let clients reconnect to in-flight streams after refresh and continue receiving the same response.
• Retrieval & RAG: Plug in retriever agents to pull facts from your data sources and ground responses (RAG) before the model answers.
• VoltAgent Knowledge Base: Use the managed RAG service for document ingestion, chunking, embeddings, and search.
• Voice: Add text-to-speech and speech-to-text capabilities with OpenAI, ElevenLabs, or custom voice providers.
• Guardrails: Intercept and validate agent input or output at runtime to enforce content policies and safety rules.
• Evals: Run agent eval suites alongside your workflows to measure and improve agent behavior.

MCP Server (@voltagent/mcp-docs-server)

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.

📖 How to setup MCP docs server

💛 Sponsors

| | | | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | <a href="https://www.testmuai.com"><picture><source media="(prefers-color-scheme: dark)" srcset="https://cdn.voltagent.dev/awesome-repo/testmui/testmuai-white.png"><img alt="TestMu AI" src="https://cdn.voltagent.dev/awesome-repo/testmui/testmuai-black.png" width="425"></picture></a> | TestMu AI (formerly LambdaTest) is an AI-native testing cloud platform built for modern engineering teams. Covering everything from autonomous test creation and fast execution to testing AI agents, chatbots and voice assistants. |

⚡ Quick Start

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:

1. Open the Console: Click the VoltOps LLM Observability Platform link in your terminal output (or copy-paste it into your browser).
2. Find Your Agent: On the VoltOps LLM Observability Platform page, you should see your agent listed (e.g., "my-agent").
3. Open Agent Details: Click on your agent's name.
4. Start Chatting: On the agent detail page, click the chat icon in the bottom right corner to open the chat window.
5. Send a Message: Type a message like "Hello" and press Enter.

Running Your First Workflow

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 w