Use and adapt voltagent
Use and adapt voltagent: AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
Use the documented interfaces and source layout for voltagent. The sections below retain the README’s examples and configuration details.
Examples
For more examples, visit our examples repository.
- Airtable Agent - React to new records and write updates back into Airtable with VoltOps actions.
- Slack Agent - Respond to channel messages and reply via VoltOps Slack actions.
- ChatGPT App With VoltAgent - Deploy VoltAgent over MCP and connect to ChatGPT Apps.
- WhatsApp Order Agent - Build a WhatsApp chatbot that handles food orders through natural conversation. (Source)
- YouTube to Blog Agent - Convert YouTube videos into Markdown blog posts using a supervisor agent with MCP tools. (Source)
- AI Ads Generator Agent - Generate Instagram ads using BrowserBase Stagehand and Google Gemini AI. (Source)
- AI Recipe Generator Agent - Create personalized cooking suggestions based on ingredients and preferences. (Source | )Loading video…0:00 / 0:00
- AI Research Assistant Agent - Multi-agent research workflow for generating comprehensive reports. (Source | )Loading video…0:00 / 0:00
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.
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
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 to learn the fundamentals building AI Agents.
- Documentation: Dive into guides, concepts, and tutorials.
- Examples: Explore practical implementations.
- Blog: Read more about technical insights, and best practices.
Commands in the root manifest
The captured package.json declares the following scripts. Run them from the directory containing that manifest.
| Command | Script |
|---|---|
npm run build | lerna run build --ignore @voltagent/vercel-ai-exporter |
npm run dev | lerna run dev --ignore voltagent-example-* |
npm run lint | biome check . |
npm run start | lerna run start |
npm run test | lerna run test --stream |
Troubleshoot a local change
- Reproduce the smallest example from the quick-start guide.
- Compare required configuration and dependency versions with the README.
- Check the linked issue tracker for the same error. Include the command, runtime version, and relevant error when reporting a problem; omit credentials.
Source and help
The catalog identifies the license as MIT. Read the repository license before redistributing source or assets.
This catalog entry is a fork. The README may describe upstream packages, domains, or release procedures; those destinations do not establish a separate release of this fork.
Source captured: 2026-10-11