VoltAgent is an end-to-end AI Agent Engineering Platform that consists of two main parts:
- **[Open-Source TypeScript Framework](#core-framework)** – Memory, RAG, Guardrails, Tools, MCP, Voice, Workflow, and more.
- **[VoltOps Console](#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](https://voltagent.dev/docs/agents/overview/) (`@voltagent/core`)**: Define agents with typed roles, tools, memory, and model providers in one place so everything stays organized.
- **[Workflow Engine](https://voltagent.dev/docs/workflows/overview/)**: Describe multi-step automations declaratively rather than stitching together custom control flow.
- **[Supervisors & Sub-Agents](https://voltagent.dev/docs/agents/sub-agents/)**: Run teams of specialized agents under a supervisor runtime that routes tasks and keeps them in sync.
- **[Tool Registry](https://voltagent.dev/docs/agents/tools/) & [MCP](https://voltagent.dev/docs/agents/mcp/)**: Ship Zod-typed tools with lifecycle hooks and cancellation, and connect to [Model Context Protocol](https://modelcontextprotocol.io/) servers without extra glue code.
- **[LLM Compatibility](https://voltagent.dev/docs/getting-started/providers-models/)**: Swap between OpenAI, Anthropic, Google, or other providers by changing config, not rewriting agent logic.
- **[Memory](https://voltagent.dev/docs/agents/memory/overview/)**: Attach durable memory adapters so agents remember important context across runs.
- **[Resumable Streaming](https://voltagent.dev/docs/agents/resumable-streaming/)**: Let clients reconnect to in-flight streams after refresh and continue receiving the same response.
- **[Retrieval & RAG](https://voltagent.dev/docs/rag/overview/)**: Plug in retriever agents to pull facts from your data sources and ground responses (RAG) before the model answers.
- **[VoltAgent Knowledge Base](https://voltagent.dev/docs/rag/voltagent/)**: Use the managed RAG service for document ingestion, chunking, embeddings, and search.
- **[Voice](https://voltagent.dev/docs/agents/voice/)**: Add text-to-speech and speech-to-text capabilities with OpenAI, ElevenLabs, or custom voice providers.
- **[Guardrails](https://voltagent.dev/docs/guardrails/overview/)**: Intercept and validate agent input or output at runtime to enforce content policies and safety rules.
- **[Evals](https://voltagent.dev/docs/evals/overview/)**: 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](https://voltagent.dev/docs/getting-started/mcp-docs-server/)
## Sponsors
| | |
| :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
| [TestMu AI (formerly LambdaTest)](https://www.testmuai.com) 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:
```bash
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`.
```
…
```
Afterwards, navigate to your project and run:
```bash
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](https://console.voltagent.dev) 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:
```
…
```
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](https://console.voltagent.dev/workflows).
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:
```json
{
"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](https://github.com/VoltAgent/voltagent/tree/main/examples).
- **[Airtable Agent](https://voltagent.dev/recipes-and-guides/airtable-agent)** - React to new records and write updates back into Airtable with VoltOps actions.
- **[Slack Agent](https://voltagent.dev/recipes-and-guides/slack-agent)** - Respond to channel messages and reply via VoltOps Slack actions.
- **[ChatGPT App With VoltAgent](https://voltagent.dev/examples/agents/chatgpt-app)** - Deploy VoltAgent over MCP and connect to ChatGPT Apps.
- **[WhatsApp Order Agent](https://voltagent.dev/examples/agents/whatsapp-ai-agent)** - Build a WhatsApp chatbot that handles food orders through natural conversation. ([Source](https://github.com/VoltAgent/voltagent/tree/main/examples/with-whatsapp))
- **[YouTube to Blog Agent](https://voltagent.dev/examples/agents/youtube-blog-agent)** - Convert YouTube videos into Markdown blog posts using a supervisor agent with MCP tools. ([Source](https://github.com/VoltAgent/voltagent/tree/main/examples/with-youtube-to-blog))
- **[AI Ads Generator Agent](https://voltagent.dev/examples/agents/ai-instagram-ad-agent)** - Generate Instagram ads using BrowserBase Stagehand and Google Gemini AI. ([Source](https://github.com/VoltAgent/voltagent/tree/main/examples/with-ad-creator))
- **[AI Recipe Generator Agent](https://voltagent.dev/examples/agents/recipe-generator)** - Create personalized cooking suggestions based on ingredients and preferences. ([Source](https://github.com/VoltAgent/voltagent/tree/main/examples/with-recipe-generator) | [Video](https://youtu.be/KjV1c6AhlfY))
- **[AI Research Assistant Agent](https://voltagent.dev/examples/agents/research-assistant)** - Multi-agent research workflow for generating comprehensive reports. ([Source](https://github.com/VoltAgent/voltagent/tree/main/examples/with-research-assistant) | [Video](https://youtu.be/j6KAUaoZMy4))
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](https://console.voltagent.dev/demo)
[VoltOps Documentation](https://voltagent.dev/voltops-llm-observability-docs/)
[VoltOps Platform](https://voltagent.dev/voltops-llm-observability/)
### 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](https://voltagent.dev/docs/deployment/voltops/)
### 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](https://voltagent.dev/tutorial/introduction/)** to learn the fundamentals building AI Agents.
- **[Documentation](https://voltagent.dev/docs/)**: Dive into guides, concepts, and tutorials.
- **[Examples](https://github.com/voltagent/voltagent/tree/main/examples)**: Explore practical implementations.
- **[Blog](https://voltagent.dev/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](https://s.voltagent.dev/discord) server for questions and discussions.
## Contributor ♥