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.

Core TypeScript Framework

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

	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 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.

Examples

For more examples, visit our examples repository.

• Airtable Agent - React to new records and write updates back into Airtable with VoltOps actions.
• Slack Agent - Respond to channel messages and reply via VoltOps Slack actions.
• ChatGPT App With VoltAgent - Deploy VoltAgent over MCP and connect to ChatGPT Apps.
• WhatsApp Order Agent - Build a WhatsApp chatbot that handles food orders through natural conversation. (Source)
• YouTube to Blog Agent - Convert YouTube videos into Markdown blog posts using a supervisor agent with MCP tools. (Source)
• AI Ads Generator Agent - Generate Instagram ads using BrowserBase Stagehand and Google Gemini AI. (Source)
• AI Recipe Generator Agent - Create personalized cooking suggestions based on ingredients and preferences. (Source | Video)
• AI Research Assistant Agent - Multi-agent research workflow for generating comprehensive reports. (Source | Video)

VoltOps Console: LLM Observability - Automation - Deployment

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.

🎬 Try Live Demo

📖 VoltOps Documentation

🚀 VoltOps Platform

Observability & Tracing

Deep dive into agent execution flow with detailed traces and performance metrics.

Dashboard

Get a comprehensive overview of all your agents, workflows, and system performance metrics.

Logs

Track detailed execution logs for every agent interaction and workflow step.

Memory Management

Inspect and manage agent memory, context, and conversation history.

Traces

Analyze complete execution traces to understand agent behavior and optimize performance.

Prompt Builder

Design, test, and refine prompts directly in the console.

Deployment

Deploy your agents to production with one-click GitHub integration and managed infrastructure.

📖 VoltOps Deploy Documentation

Triggers & Actions

Automate agent workflows with webhooks, schedules, and custom triggers to react to external events.

Monitoring

Monitor agent health, performance metrics, and resource usage across your entire system.

Guardrails

Set up safety boundaries and content filters to ensure agents operate within defined parameters.

Evals

Run evaluation suites to test agent behavior, accuracy, and performance against benchmarks.

RAG (Knowledge Base)

Connect your agents to knowledge sources with built-in retrieval-augmented generation capabilities.

Learning VoltAgent

• Start with interactive tutorial to learn the fundamentals building AI Agents.
• Documentation: Dive into guides, concepts, and tutorials.
• Examples: Explore practical implementations.
• Blog: Read more about technical insights, and best practices.

Contribution

We welcome contributions! Please refer to the contribution guidelines (link needed if available). Join our Discord server for questions and discussions.

Contributor ♥️ Thanks

Big thanks to everyone who's been part of the VoltAgent journey, whether you've built a plugin, opened an issue, dropped a pull request, or just helped someone out on Discord or GitHub Discussions.

VoltAgent is a community effort, and it keeps getting better because of people like you.

License

Licensed under the MIT License, Copyright © 2026-present VoltAgent.