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Voight is the observability and debugging platform for AI agents and autonomous systems. It captures every prompt, tool call, model response, decision and error your agents and AI applications make, renders them as a live timeline, groups them into traces, and turns failures into an issue queue with AI diagnosis of the root cause. If you ship LLM features to end-users, Voight also answers the question nobody else does cleanly: what does each user cost me? One line of code at your request boundary, no PII handoff required. Full pattern on the per-user spend page.

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Six surfaces, one event stream

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What Voight is for

Three audiences, one platform:
  • You’re building an AI coding agent or autonomous bot. Wire the coding SDK into your IDE or import library mode: every prompt, tool call and error lands in the dashboard with zero code changes.
  • You’re shipping an LLM-powered feature to end-users. Wrap your OpenAI / Anthropic client with the App SDKs, or register @voightxyz/vercel-ai once if you’re on the Vercel AI SDK. Every call gets captured with full token/cost breakdown. Add withTrace at your request boundary to group calls per request and attribute cost per end-user.
  • You sell AI-powered tools to other developers. The per-user spend pattern lets you answer “what does each of my customer’s customers cost me?” without integrating an analytics SDK on the client side.
The same event model is built to extend beyond software agents: robots, drones and other physical autonomous systems are the next surface.

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Primitives

  • Agent: an autonomous entity emitting events. Identified by a folder marker, env var, or explicit agentId.
  • Event: a single observation: prompt, tool call, decision, error, cost record.
  • Trace: a group of events sharing a traceId, typically one user request or workflow.
  • Session: a time-bounded run of an agent.
  • Tag: a free-form key=value pair attached to events inside a withTrace block; the foundation of per-user / per-tenant / per-feature attribution.

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Privacy

Three capture levels: Minimal (metadata only), Standard (full content with local PII scrubbing), Full (raw). Picked once at install, switchable per session via env var. The dashboard renders a per-event chip showing which level captured each row. The numeric data (tokens, USD, latency, tool names, tags) passes through every level unchanged. KPIs and charts work identically in all three. You only lose prompt/response content in Minimal. See the privacy overview for the field-by-field breakdown.

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Who builds Voight

Voight is built by Galaxyhub Labs Inc. (Delaware, 2025), founded by Dangel Rodriguez with a team of four across Spain and London, and is part of Xiaomi Orbit and Z.ai Startups. Traction, timeline, programs and recognition, with dates and links: voight.xyz/company.

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Don’t have agents yet?

Voight Agents is a separate product: autonomous agents that Voight hosts and runs for you, live in about a minute on cloud or GPU. It exists so you have something to observe from day one, and you can point this platform at any Voight Agent like at any other agent.

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Next

  • Quickstart: events streaming in 30 seconds (any of four install paths)
  • AI Apps overview: the dashboard section for production LLM apps
  • Per-user spend: one line of code to attribute cost per end-user