voltagent

VoltAgent is an end-to-end AI agent engineering platform consisting of two main parts: an open-source TypeScript framework, and the VoltOps Console cloud/self-hosted console. Its goal is to let developers build agents with full code control and launch them into production with production-ready visibility and operational capabilities.

1. Core TypeScript Framework Using the open-source framework, you can build agents with memory, tools, and multi-step workflows, and connect to any AI provider. You can create complex multi-agent systems where specialized agents work together under the coordination of a supervisor.

Main capabilities include:

• Core runtime (@voltagent/core): Define agents with typed roles, tools, memory, and model providers in one place, keeping the structure clear.
• Workflow engine: Describe multi-step automation declaratively without having to stitch together custom control flow yourself.
• Supervisor and sub-agents: Run a team of specialized agents under a supervisor runtime, with the supervisor routing tasks and keeping them in sync.
• Tool registry and MCP: Publish tools with Zod types, lifecycle hooks, and cancellation support, and connect to Model Context Protocol servers without extra glue code.
• LLM compatibility: Switch between OpenAI, Anthropic, Google, or other providers by changing configuration, without rewriting agent logic.
• Memory: Attach persistent memory adapters so agents remember important context across runs.
• Resumable streaming: Let clients reconnect to an in-progress stream after a refresh and continue receiving the same response.
• Retrieval and RAG: Connect retrieval agents to pull facts from data sources and perform fact-based augmentation (RAG) before the model answers.
• VoltAgent knowledge base: Use a managed RAG service for document ingestion, chunking, embedding, and search.
• Voice: Add text-to-speech and speech-to-text capabilities through OpenAI, ElevenLabs, or custom voice providers.
• Guardrails: Intercept and validate agent input or output at runtime to enforce content policies and safety rules.
• Evaluation: Run agent evaluation suites alongside workflows to measure and improve agent behavior.

In addition, you can use the MCP server @voltagent/mcp-docs-server to teach LLMs how to use VoltAgent, suitable for Claude, CursoAI coding assistants such as r or Windsurf. This allows AI assistants to directly access VoltAgent documentation, examples, and changelogs while coding.

1. How to use it You can create a new VoltAgent project in seconds using the create-voltagent-app CLI tool, and the command will guide you through setup. The startup code is located in src/index.ts, where an agent is registered, along with a complete workflow example located in src/workflows/index.ts. Then enter the project and run the development command; tsx will compile and run the code, and the terminal will display the VoltAgent server startup message.

Steps to interact with the agent: open the console (click the VoltOps LLM Observability Platform link in the terminal output, or copy and paste it into a browser); on the VoltOps LLM Observability Platform page, find the listed agent (for example, "my-agent"); click the agent name to open the details; on the details page, click the chat icon in the lower right corner to open the chat window; enter a message such as "Hello" and press Enter.

Run your first workflow: the 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 preconfigured expenseApprovalWorkflow directly from the VoltOps console: after starting the server, go to the Workflows page; use the project selector to choose a project (for example, "my-agent-app"); find "Expense Approval Workflow" and click it, then click the "Run" button; provide input. The workflow expects a JSON object containing expense details, and you can try a small expense for automatic approval: { "employeeId" : " EMP-123 " , "amount" : 250 , "category" : " office-supplies " , "description" : " New laptop mouse and keyboard " }; after execution, you can inspect detailed logs for each step and view the final output directly in the console.

More examples are available in the examples repository, including: Airtable Agent (responds to new records and through VoltOps actions write updates back to Airtable), Slack Agent (responds to channel messages and replies via VoltOps Slack actions), ChatGPT App With VoltAgent (deploys VoltAgent via MCP and connects to ChatGPT Apps), WhatsApp Order Agent (builds a WhatsApp chatbot that handles food orders through natural conversation), YouTube to Blog Agent (converts YouTube videos into Markdown blog posts using a supervisor agent with MCP tools), AI Ads Generator Agent (generates Instagram ads using BrowserBase Stagehand and Google Gemini AI), AI Recipe Generator Agent (creates personalized cooking suggestions based on ingredients and preferences), AI Research Assistant Agent (a multi-agent research workflow for generating comprehensive reports).

III. VoltOps Console: LLM Observability, Automation, Deployment VoltOps Console is the platform side of VoltAgent, providing observability, automation, and deployment so that agents can be monitored and debugged in production through real-time execution tracing, performance metrics, and visual dashboards.

Observability and Tracing: Gain deep insight into agent execution flows through detailed tracing and performance metrics; get a comprehensive overview of all agents, workflows, and system performance metrics; track detailed execution logs for every agent interaction and workflow step.

Memory Management: Inspect and manage agent memory, context, and conversation history; analyze complete execution traces to understand agent behavior and optimize performance; design, test, and optimize prompts directly in the console; deploy agents to production through one-click GitHub integration and hosted infrastructure.

Triggers and Actions: Automate agent workflows through webhooks, schedules, and custom triggers to respond to external events; monitor agent health status, performance metrics, and resource usage across the entire system; set safety boundaries and content filters to ensure agents operate within defined parameters; run evaluation suites to benchmark agent behavior, accuracy, and performance against baselines.

RAG (Knowledge Base): Connect agents to knowledge sources through built-in retrieval-augmented generation capabilities.

IV. Learning Resources You can start learning the basics of building AI agents from interactive tutorials; the documentation provides guides, concepts, and tutorials; examples show real-world implementations; the blog provides technical insights and best practices. The project welcomes contributions, and you can join the Discord server to ask questions and discuss. VoltAgent uses the MIT license, Copyright © 2026-present VoltAgent.

V. Limitations and Notes The materials do not provide specific details such as fees, pricing, platform support scope, or performance results, and none are currently provided.