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VoltOps
Trace, debug, and monitor AI agents apps in n8n-style
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Omer Aplak

VoltOps - Trace, debug, and monitor AI agents apps in n8n-style

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VoltOps is a developer-first observability platform for AI agents. Built to trace, debug, and monitor agent workflows with full context. Gain complete visibility into your AI systems with structured traces, rich logs, and an n8n-style visual interface.

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Omer Aplak
Maker
📌

👋 Hey Product Hunt, I’m Omer, co-creator of VoltOps.

We’re the team behind VoltAgent, and today we’re excited to launch VoltOps. A framework-agnostic observability and monitoring infrastructure for AI agents & LLM apps.

🎯 Why did we build this?

After building AI agents with VoltAgent and other frameworks, we kept running into the same problem: we had little visibility into what was happening inside the agents.

Traditional observability tools were built for web apps not for AI agents running long chains of thought, RAG pipelines, or tool calls. So we built VoltOps to give AI teams clear, visual insight into their agents' inner workings.

⚙️ What does VoltOps do?

VoltOps adds real observability to your AI stack:
• Trace and debug every agent step, like thoughts, tools, and memory reads
• View live logs and context-aware metrics
• View dashboards your team can actually understand

VoltOps is framework-agnostic, it can be used with or without VoltAgent.

Supported Framework Integrations:
• VoltAgent
• Vercel AI SDK

Available SDKs:
• JavaScript/TypeScript SDK
• Python SDK

✨ What makes it special?
• Designed specifically for AI agents, not generic app telemetry
• Natively supports multi-step chains, tools, thoughts, memory
• Lightweight, easy to drop in

📌 Check it out here: https://github.com/VoltAgent/vol...
📍 Live demo: https://console.voltagent.dev/demo
📎 Docs: https://voltagent.dev/voltops-ll...
👋 Discord: https://s.voltagent.dev/discord

We’d love to hear what you think, feel free to comment, try it out, or share what observability features you wish existed for agents!

Kadir Inip

Congrats on the launch!!

Reid Crooks
@omeraplak @necatiozmen question: how do you handle storing and querying trace data efficiently—especially when workflows involve lots of small LLM calls and memory reads?
Rachit Magon

This looks super useful! AI agent behavior is such a black box right now, so real observability is huge. Curious if VoltOps can also help spot and explain hallucinations in complex chains? @omeraplak