{"repo":"VoltAgent/voltagent","free":true,"listed":false,"github":"https://github.com/VoltAgent/voltagent","clone":"git clone https://github.com/VoltAgent/voltagent.git","description":"AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework","language":"TypeScript","stars":10368,"topics":["agents","ai","ai-agents","ai-agents-framework","aiagentframework","chatbots","chatgpt","framework","javascript","llm","llm-observability","mcp","multiagent","nodejs","observability","open-source","openai","rag","tts","typescript"],"license":"MIT","category":"ai_agent_platform","readme_excerpt":"
\n\n\"voltagent\"\n\n\n

\nAI Agent Engineering Platform\n

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\nEnglish | 繁體中文 | 简体中文 | 日本語 | 한국어\n
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\n Home Page |\n Documentation |\n Examples \n
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\n\n[![GitHub issues](https://img.shields.io/github/issues/voltagent/voltagent)](https://github.com/voltagent/voltagent/issues)\n[![GitHub pull requests](https://img.shields.io/github/issues-pr/voltagent/voltagent)](https://github.com/voltagent/voltagent/pulls)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg)](CODE_OF_CONDUCT.md)\n[![npm version](https://img.shields.io/npm/v/@voltagent/core.svg)](https://www.npmjs.com/package/@voltagent/core)\n\n[![npm downloads](https://img.shields.io/npm/dm/@voltagent/core.svg)](https://www.npmjs.com/package/@voltagent/core)\n[![Discord](https://img.shields.io/discord/1361559153780195478.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://s.voltagent.dev/discord)\n[![Twitter Follow](https://img.shields.io/twitter/follow/voltagent_dev?style=social)](https://x.com/voltagent_dev)\n\n
\n\nVoltAgent is an end-to-end AI Agent Engineering Platform that consists of two main parts:\n\n- **[Open-Source TypeScript Framework](#core-framework)** – Memory, RAG, Guardrails, Tools, MCP, Voice, Workflow, and more.\n- **[VoltOps Console](#voltops-console)** `Cloud` `Self-Hosted` – Observability, Automation, Deployment, Evals, Guardrails, Prompts, and more.\n\nBuild agents with full code control and ship them with production-ready visibility and operations.\n\n

Core TypeScript Framework

\n\nWith 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.\n\n- **[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.\n- **[Workflow Engine](https://voltagent.dev/docs/workflows/overview/)**: Describe multi-step automations declaratively rather than stitching together custom control flow.\n- **[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.\n- **[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.\n- **[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.\n- **[Memory](https://voltagent.dev/docs/agents/memory/overview/)**: Attach durable memory adapters so agents remember important context across runs.\n- **[Resumable Streaming](https://voltagent.dev/docs/agents/resumable-streaming/)**: Let clients reconnect to in-flight streams after refresh and continue receiving the same response.\n- **[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.\n- **[VoltAgent Knowledge Base](https://voltagent.dev/docs/rag/voltagent/)**: Use the managed RAG service for document ingestion, chunking, embeddings, and search.\n- **[Voice](https://voltagent.dev/docs/agents/voice/)**: Add text-to-speech and speech-to-text capabilities with OpenAI, ElevenLabs, or custom voice providers.\n- **[Guardrails](https://voltagent.dev/docs/guardrails/overview/)**: Intercept and validate agent input or output at runtime to enforce content policies and safety rules.\n- **[Evals](https://voltagent.dev/docs/evals/overview/)**: Run agent eval suites alongside your workflows to measure and improve agent behavior.\n\n#### MCP Server (@voltagent/mcp-docs-server)\n\nYou 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.\n\n📖 [How to setup MCP docs server](https://voltagent.dev/docs/getting-started/mcp-docs-server/)\n\n## 💛 Sponsors\n\n| | |\n| :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| \"TestMu | [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. |\n\n## ⚡ Quick Start\n\nCreate a new VoltAgent project in seconds using the `create-voltagent-app` CLI tool:\n\n```bash\nnpm create voltagent-app@latest\n```\n\nThis command guides you through setup.\n\nYou'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`.\n\n```typescript\nimport { VoltAgent, Agent, Memory } from \"@voltagent/core\";\nimport { LibSQLMemoryAdapter } from \"@voltagent/libsql\";\nimport { createPinoLogger } from \"@voltagent/logger\";\nimport { honoServer } from \"@voltagent/server-hono\";\nimport { openai } from \"@ai-sdk/openai\";\nimport { expenseApprovalWorkflow } from \"./workflows\";\nimport { weatherTool } from \"./tools\";\n\n// Create a logger instance\nconst logger = createPinoLogger({\n name: \"my-agent-app\",\n level: \"info\",\n});\n\n// Optional persistent memory (remove to use default in-memory)\nconst memory = new Memory({\n storage: new LibSQLMemoryAdapter({ url: \"file:./.voltagent/memory.db\" }),\n});\n\n// A simple, general-purpose agent for the project.\nconst agent = new Agent({\n name: \"my-agent\",\n instructions: \"A helpful assistant that can check weather and help with various tasks\",\n model: openai(\"gpt-4o-mini\"),\n tools: [weatherTool],\n memory,\n});\n\n// Initialize VoltAgent with your agent(s) and workflow(s)\nnew VoltAgent({\n agents: {\n agent,\n },\n workflows: {\n expenseApprovalWorkflow,\n },\n server: honoServer(),\n logger,\n});\n```\n\nAfterwards, navigate to your project and run:\n\n```bash\nnpm run dev\n```\n\nWhen you run the dev command, tsx will compile and run your code. You should see the VoltAgent server startup message in your terminal:\n\n```\n══════════════════════════════════════════════════\nVOLTAGENT SERVER STARTED SUCCESSFULLY\n══════════════════════════════════════════════════\n✓ HTTP Server: http://localhost:3141\n\nTest your agents with VoltOps Console: https://console.voltagent.dev\n══════════════════════════════════════════════════\n```\n\nYour agent is now running! To interact with it:\n\n1. 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).\n2. Find Your Agent: On the VoltOps LLM Observability Platform page, you should see your agent listed (e.g., \"my-agent\").\n3. Open Agent Details: Click on your agent's name.\n4. Start Chatting: On the agent detail page, click the chat icon in the bottom right corner to open the chat window.\n5. Send a Message: Type a message like \"Hello\" and press Enter.\n\n[![VoltAgent Demo](thumbnail.png)](https://github.com/user-attachments/assets/26340c6a-be34-48a5-9006-e822bf6098a7)\n\n### Running Your First Workflow\n\nYour new project also includes a powerful workflow engine.\n\nThe expense approval workflow demonstrates human-in-the-loop automation with suspend/resume capabilities:\n\n```typescript\nimport { createWorkflowChain } from \"@voltagent/core\";\nimport { z } from \"zod\";\n\nexport const expenseApprovalWorkflow = createWorkflowChain({\n id: \"expense-approval\",\n name: \"Expense Approval Workflow\",\n purpose: \"Process expense reports with manager approval for high amounts\",\n\n input: z.object({\n employeeId: z.string(),\n amount: z.number(),\n 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