[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-10175":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":46,"readmeContent":47,"aiSummary":48,"trendingCount":16,"starSnapshotCount":16,"syncStatus":49,"lastSyncTime":50,"discoverSource":51},10175,"voltagent","VoltAgent\u002Fvoltagent","VoltAgent","AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework","https:\u002F\u002Fvoltagent.dev",null,"TypeScript",9542,990,92,28,0,22,180,750,110,114.99,"MIT License",false,"main",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"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","2026-06-12 04:00:49","\u003Cdiv align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fvoltagent.dev\u002F\">\n\u003Cimg width=\"1500\" height=\"276\" alt=\"voltagent\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fd9ad69bd-b905-42a3-81af-99a0581348c0\" \u002F>\n\u003C\u002Fa>\n\n\u003Ch3 align=\"center\">\nAI Agent Engineering Platform\n\u003C\u002Fh3>\n\n\u003Cdiv align=\"center\">\nEnglish | \u003Ca href=\"i18n\u002FREADME-cn-traditional.md\">繁體中文\u003C\u002Fa> | \u003Ca href=\"i18n\u002FREADME-cn-bsc.md\">简体中文\u003C\u002Fa> | \u003Ca href=\"i18n\u002FREADME-jp.md\">日本語\u003C\u002Fa> | \u003Ca href=\"i18n\u002FREADME-kr.md\">한국어\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cbr\u002F>\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fvoltagent.dev\">Home Page\u003C\u002Fa> |\n    \u003Ca href=\"https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002F\">Documentation\u003C\u002Fa> |\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fvoltagent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples\">Examples\u003C\u002Fa> \n\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\n\u003Cbr\u002F>\n\n\u003Cdiv align=\"center\">\n\n[![GitHub issues](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues\u002Fvoltagent\u002Fvoltagent)](https:\u002F\u002Fgithub.com\u002Fvoltagent\u002Fvoltagent\u002Fissues)\n[![GitHub pull requests](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues-pr\u002Fvoltagent\u002Fvoltagent)](https:\u002F\u002Fgithub.com\u002Fvoltagent\u002Fvoltagent\u002Fpulls)\n[![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-yellow.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT)\n[![Contributor Covenant](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FContributor%20Covenant-2.0-4baaaa.svg)](CODE_OF_CONDUCT.md)\n[![npm version](https:\u002F\u002Fimg.shields.io\u002Fnpm\u002Fv\u002F@voltagent\u002Fcore.svg)](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@voltagent\u002Fcore)\n\n[![npm downloads](https:\u002F\u002Fimg.shields.io\u002Fnpm\u002Fdm\u002F@voltagent\u002Fcore.svg)](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@voltagent\u002Fcore)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1361559153780195478.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https:\u002F\u002Fs.voltagent.dev\u002Fdiscord)\n[![Twitter Follow](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fvoltagent_dev?style=social)](https:\u002F\u002Fx.com\u002Fvoltagent_dev)\n\n\u003C\u002Fdiv>\n\n\u003Ch3 align=\"center\">\n⭐ Like what we're doing? Give us a star ⬆️\n\u003C\u002Fh3>\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\u003Ch2 id=\"core-framework\">Core TypeScript Framework\u003C\u002Fh2>\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:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Foverview\u002F) (`@voltagent\u002Fcore`)**: Define agents with typed roles, tools, memory, and model providers in one place so everything stays organized.\n- **[Workflow Engine](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fworkflows\u002Foverview\u002F)**: Describe multi-step automations declaratively rather than stitching together custom control flow.\n- **[Supervisors & Sub-Agents](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Fsub-agents\u002F)**: Run teams of specialized agents under a supervisor runtime that routes tasks and keeps them in sync.\n- **[Tool Registry](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Ftools\u002F) & [MCP](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Fmcp\u002F)**: Ship Zod-typed tools with lifecycle hooks and cancellation, and connect to [Model Context Protocol](https:\u002F\u002Fmodelcontextprotocol.io\u002F) servers without extra glue code.\n- **[LLM Compatibility](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fgetting-started\u002Fproviders-models\u002F)**: Swap between OpenAI, Anthropic, Google, or other providers by changing config, not rewriting agent logic.\n- **[Memory](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Fmemory\u002Foverview\u002F)**: Attach durable memory adapters so agents remember important context across runs.\n- **[Resumable Streaming](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Fresumable-streaming\u002F)**: Let clients reconnect to in-flight streams after refresh and continue receiving the same response.\n- **[Retrieval & RAG](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Frag\u002Foverview\u002F)**: Plug in retriever agents to pull facts from your data sources and ground responses (RAG) before the model answers.\n- **[VoltAgent Knowledge Base](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Frag\u002Fvoltagent\u002F)**: Use the managed RAG service for document ingestion, chunking, embeddings, and search.\n- **[Voice](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fagents\u002Fvoice\u002F)**: Add text-to-speech and speech-to-text capabilities with OpenAI, ElevenLabs, or custom voice providers.\n- **[Guardrails](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fguardrails\u002Foverview\u002F)**: Intercept and validate agent input or output at runtime to enforce content policies and safety rules.\n- **[Evals](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fevals\u002Foverview\u002F)**: Run agent eval suites alongside your workflows to measure and improve agent behavior.\n\n#### MCP Server (@voltagent\u002Fmcp-docs-server)\n\nYou can use the MCP server `@voltagent\u002Fmcp-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:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fgetting-started\u002Fmcp-docs-server\u002F)\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\u002Findex.ts`, which now registers both an agent and a comprehensive workflow example found in `src\u002Fworkflows\u002Findex.ts`.\n\n```typescript\nimport { VoltAgent, Agent, Memory } from \"@voltagent\u002Fcore\";\nimport { LibSQLMemoryAdapter } from \"@voltagent\u002Flibsql\";\nimport { createPinoLogger } from \"@voltagent\u002Flogger\";\nimport { honoServer } from \"@voltagent\u002Fserver-hono\";\nimport { openai } from \"@ai-sdk\u002Fopenai\";\nimport { expenseApprovalWorkflow } from \".\u002Fworkflows\";\nimport { weatherTool } from \".\u002Ftools\";\n\n\u002F\u002F Create a logger instance\nconst logger = createPinoLogger({\n  name: \"my-agent-app\",\n  level: \"info\",\n});\n\n\u002F\u002F Optional persistent memory (remove to use default in-memory)\nconst memory = new Memory({\n  storage: new LibSQLMemoryAdapter({ url: \"file:.\u002F.voltagent\u002Fmemory.db\" }),\n});\n\n\u002F\u002F 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\u002F\u002F 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:\u002F\u002Flocalhost:3141\n\nTest your agents with VoltOps Console: https:\u002F\u002Fconsole.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:\u002F\u002Fconsole.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:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F26340c6a-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\u002Fresume capabilities:\n\n```typescript\nimport { createWorkflowChain } from \"@voltagent\u002Fcore\";\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    category: z.string(),\n    description: z.string(),\n  }),\n  result: z.object({\n    status: z.enum([\"approved\", \"rejected\"]),\n    approvedBy: z.string(),\n    finalAmount: z.number(),\n  }),\n})\n  \u002F\u002F Step 1: Validate expense and check if approval needed\n  .andThen({\n    id: \"check-approval-needed\",\n    resumeSchema: z.object({\n      approved: z.boolean(),\n      managerId: z.string(),\n      comments: z.string().optional(),\n      adjustedAmount: z.number().optional(),\n    }),\n    execute: async ({ data, suspend, resumeData }) => {\n      \u002F\u002F If we're resuming with manager's decision\n      if (resumeData) {\n        return {\n          ...data,\n          approved: resumeData.approved,\n          approvedBy: resumeData.managerId,\n          finalAmount: resumeData.adjustedAmount || data.amount,\n        };\n      }\n\n      \u002F\u002F Check if manager approval is needed (expenses over $500)\n      if (data.amount > 500) {\n        await suspend(\"Manager approval required\", {\n          employeeId: data.employeeId,\n          requestedAmount: data.amount,\n        });\n      }\n\n      \u002F\u002F Auto-approve small expenses\n      return {\n        ...data,\n        approved: true,\n        approvedBy: \"system\",\n        finalAmount: data.amount,\n      };\n    },\n  })\n  \u002F\u002F Step 2: Process the final decision\n  .andThen({\n    id: \"process-decision\",\n    execute: async ({ data }) => {\n      return {\n        status: data.approved ? \"approved\" : \"rejected\",\n        approvedBy: data.approvedBy,\n        finalAmount: data.finalAmount,\n      };\n    },\n  });\n```\n\nYou can test the pre-built `expenseApprovalWorkflow` directly from the VoltOps console:\n\n[![expense-approval](thumbnail.png)](https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F3d3ea67b-4ab5-4dc0-932d-cedd92894b18)\n\n1.  **Go to the Workflows Page:** After starting your server, go directly to the [Workflows page](https:\u002F\u002Fconsole.voltagent.dev\u002Fworkflows).\n2.  **Select Your Project:** Use the project selector to choose your project (e.g., \"my-agent-app\").\n3.  **Find and Run:** You will see **\"Expense Approval Workflow\"** listed. Click it, then click the **\"Run\"** button.\n4.  **Provide Input:** The workflow expects a JSON object with expense details. Try a small expense for automatic approval:\n    ```json\n    {\n      \"employeeId\": \"EMP-123\",\n      \"amount\": 250,\n      \"category\": \"office-supplies\",\n      \"description\": \"New laptop mouse and keyboard\"\n    }\n    ```\n5.  **View the Results:** After execution, you can inspect the detailed logs for each step and see the final output directly in the console.\n\n## Examples\n\nFor more examples, visit our [examples repository](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples).\n\n- **[Airtable Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fguides\u002Fairtable-agent)** - React to new records and write updates back into Airtable with VoltOps actions.\n- **[Slack Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fguides\u002Fslack-agent)** - Respond to channel messages and reply via VoltOps Slack actions.\n- **[ChatGPT App With VoltAgent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fagents\u002Fchatgpt-app)** - Deploy VoltAgent over MCP and connect to ChatGPT Apps.\n- **[WhatsApp Order Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fagents\u002Fwhatsapp-ai-agent)** - Build a WhatsApp chatbot that handles food orders through natural conversation. ([Source](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples\u002Fwith-whatsapp))\n- **[YouTube to Blog Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fagents\u002Fyoutube-blog-agent)** - Convert YouTube videos into Markdown blog posts using a supervisor agent with MCP tools. ([Source](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples\u002Fwith-youtube-to-blog))\n- **[AI Ads Generator Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fagents\u002Fai-instagram-ad-agent)** - Generate Instagram ads using BrowserBase Stagehand and Google Gemini AI. ([Source](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples\u002Fwith-ad-creator))\n- **[AI Recipe Generator Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fagents\u002Frecipe-generator)** - Create personalized cooking suggestions based on ingredients and preferences. ([Source](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples\u002Fwith-recipe-generator) | [Video](https:\u002F\u002Fyoutu.be\u002FKjV1c6AhlfY))\n- **[AI Research Assistant Agent](https:\u002F\u002Fvoltagent.dev\u002Fexamples\u002Fagents\u002Fresearch-assistant)** - Multi-agent research workflow for generating comprehensive reports. ([Source](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples\u002Fwith-research-assistant) | [Video](https:\u002F\u002Fyoutu.be\u002Fj6KAUaoZMy4))\n\n\u003Ch2 id=\"voltops-console\">VoltOps Console: LLM Observability - Automation - Deployment\u003C\u002Fh2>\n\nVoltOps 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.\n\n🎬 [Try Live Demo](https:\u002F\u002Fconsole.voltagent.dev\u002Fdemo)\n\n📖 [VoltOps Documentation](https:\u002F\u002Fvoltagent.dev\u002Fvoltops-llm-observability-docs\u002F)\n\n🚀 [VoltOps Platform](https:\u002F\u002Fvoltagent.dev\u002Fvoltops-llm-observability\u002F)\n\n### Observability & Tracing\n\nDeep dive into agent execution flow with detailed traces and performance metrics.\n\n\u003Cimg alt=\"1\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F21c6d05d-f333-4c61-9218-8862d16110fd\" \u002F>\n\n### Dashboard\n\nGet a comprehensive overview of all your agents, workflows, and system performance metrics.\n\n\u003Cimg alt=\"dashboar\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fc88a5543-219e-4cf0-8f41-14a68ca297fb\" \u002F>\n\n### Logs\n\nTrack detailed execution logs for every agent interaction and workflow step.\n\n![VoltOps Logs](https:\u002F\u002Fcdn.voltagent.dev\u002Fconsole\u002Flogs.png)\n\n### Memory Management\n\nInspect and manage agent memory, context, and conversation history.\n\n![VoltOps Memory Overview](https:\u002F\u002Fcdn.voltagent.dev\u002Fconsole\u002Fmemory.png)\n\n### Traces\n\nAnalyze complete execution traces to understand agent behavior and optimize performance.\n\n![VoltOps Traces](https:\u002F\u002Fcdn.voltagent.dev\u002Fconsole\u002Ftraces.png)\n\n### Prompt Builder\n\nDesign, test, and refine prompts directly in the console.\n\n\u003Cimg  alt=\"prompts\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Ffb6d71eb-8f81-4443-a494-08c33ec9bcc4\" \u002F>\n\n### Deployment\n\nDeploy your agents to production with one-click GitHub integration and managed infrastructure.\n\n\u003Cimg alt=\"deployment\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fe329ab4b-7464-435a-96cc-90214e8a3cfa\" \u002F>\n\n📖 [VoltOps Deploy Documentation](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002Fdeployment\u002Fvoltops\u002F)\n\n### Triggers & Actions\n\nAutomate agent workflows with webhooks, schedules, and custom triggers to react to external events.\n\n\u003Cimg width=\"1277\"  alt=\"triggers\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F67e36934-2eb5-4cf1-94f8-3057d805ef65\" \u002F>\n\n### Monitoring\n\nMonitor agent health, performance metrics, and resource usage across your entire system.\n\n\u003Cimg  alt=\"monitoring\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F1fd1151f-5ee4-4c7c-8ec7-29874e37c48f\" \u002F>\n\n### Guardrails\n\nSet up safety boundaries and content filters to ensure agents operate within defined parameters.\n\n\u003Cimg  alt=\"guardrails\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F52bd51f0-944e-4202-9f54-7bb2e0e2d1f6\" \u002F>\n\n### Evals\n\nRun evaluation suites to test agent behavior, accuracy, and performance against benchmarks.\n\n\u003Cimg  alt=\"evals\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F510cc180-2661-4973-a48f-074d4703d90b\" \u002F>\n\n### RAG (Knowledge Base)\n\nConnect your agents to knowledge sources with built-in retrieval-augmented generation capabilities.\n\n\u003Cimg  alt=\"rag\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fa6c2f668-7ad1-4fb6-b67f-654335285f1e\" \u002F>\n\n## Learning VoltAgent\n\n- **[Start with interactive tutorial](https:\u002F\u002Fvoltagent.dev\u002Ftutorial\u002Fintroduction\u002F)** to learn the fundamentals building AI Agents.\n- **[Documentation](https:\u002F\u002Fvoltagent.dev\u002Fdocs\u002F)**: Dive into guides, concepts, and tutorials.\n- **[Examples](https:\u002F\u002Fgithub.com\u002Fvoltagent\u002Fvoltagent\u002Ftree\u002Fmain\u002Fexamples)**: Explore practical implementations.\n- **[Blog](https:\u002F\u002Fvoltagent.dev\u002Fblog\u002F)**: Read more about technical insights, and best practices.\n\n## Contribution\n\nWe welcome contributions! Please refer to the contribution guidelines (link needed if available). Join our [Discord](https:\u002F\u002Fs.voltagent.dev\u002Fdiscord) server for questions and discussions.\n\n## Contributor ♥️ Thanks\n\nBig 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.\n\nVoltAgent is a community effort, and it keeps getting better because of people like you.\n\n![Contributors](https:\u002F\u002Fcontrib.rocks\u002Fimage?repo=voltagent\u002Fvoltagent&max=500&columns=20&anon=1)\n\n## License\n\nLicensed under the MIT License, Copyright © 2026-present VoltAgent.\n","VoltAgent 是一个基于开源 TypeScript AI 代理框架构建的端到端 AI 代理工程平台。它提供了核心功能如记忆、检索增强生成（RAG）、工具集成、多步骤工作流等，并支持与多种 AI 提供商连接。通过 VoltOps 控制台，用户可以获得可观测性、自动化部署等功能，便于管理和监控 AI 代理。该平台适用于需要构建复杂多代理系统或希望对 AI 代理进行精细控制和管理的应用场景，如聊天机器人开发、企业级智能助手等。采用 MIT 许可证，确保了其在开源社区中的广泛应用潜力。",2,"2026-06-11 03:27:00","top_topic"]