voltagent

AI Agent Engineering Platform

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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.

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

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⚡ 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(),