VoltOps
VoltAgent Inc. · Coding
VoltOps is an LLM observability platform from the VoltAgent team. It shows AI agent runs as live traces, so you can watch every tool call and step, then export OpenTelemetry data to production.
About VoltOps
What Is VoltOps
VoltOps is the observability half of the VoltAgent platform, a TypeScript AI agent framework. It solves a specific debugging problem: an agent that calls tools, keeps memory, and chains several reasoning steps is hard to follow, and plain text logs don't show you what went wrong or why.
The console turns that black box into a view you can read. Each run becomes an interactive trace with a timeline, and you can open any step to see the exact prompt sent to the model, the response that came back, and the tool inputs and outputs. Sub-agents show up inline, so multi-agent handoffs stay on one screen.
It works in two modes. During local development it connects straight to your agent on localhost (usually port 3141) and keeps all data on your machine. For production, adding a public and secret key turns on remote export over OpenTelemetry (OTLP), and traces stream to the cloud without code changes. That split is why teams can debug locally with no data leaving the laptop, then flip one env var to watch live traffic.
Getting Started
- Install the VoltAgent framework with
npm install @voltagent/coreand define an agent, its model, tools, and memory. - Start the app. A local server comes up, typically on
http://localhost:3141, and prints a readiness banner. - Open the hosted console at
console.voltagent.dev. It auto-connects to localhost, or you set a custom URL in settings. - For production, create a public/secret key pair in the console, drop them into your
.env, and remote export turns on by itself. - Run your agent, then pick a trace in the console to inspect steps, memory, and logs.
Product Information
A quick look at VoltOps's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Backend and full-stack developers building TypeScript agents who need to see why a run failed
- AI engineers on teams that already run OpenTelemetry
- Solo builders shipping an agent MVP
Tasks
- Debugging a multi-step agent run that calls tools in the wrong order
- Inspecting prompts and model responses after a bad answer
- Watching token and latency cost across a workflow
- Setting up production monitoring without rewriting code
Scenarios
- A local debugging session where you want zero data leaves your machine
- A team handing an agent to a teammate
- A production incident where you need the trace from a specific request
Key features
Interactive Workflow Visualization
VoltOps renders each agent run as a trace with a timeline instead of a wall of text. You see the sequence of steps, which tool fired at each one, and the inputs and outputs. That turns a tangle of multi-agent handoffs into something you can read at a glance. Sub-agent steps appear inline, each tagged with its agent id.
Real-Time Execution and Local Debugging
The console connects directly to your running agent, usually on port 3141, and streams the execution flow as it happens. Because the connection stays between your browser and localhost, no data is sent to or stored on external servers in this mode. You inspect steps, logs, and agent state live while the agent works.
OpenTelemetry-Based Production Export
Production observability runs on OpenTelemetry over standard OTLP HTTP. Add a public key and secret key to your environment and remote export initializes lazily, with smart batching and backpressure to keep overhead low. The design is deliberate: local debugging stays instant, and the remote path switches on only when valid keys exist.
Memory Explorer with Steps Correlation
The Memory Explorer has two tabs. Messages shows conversation history for the selected user, and Steps shows structured execution steps tied to the active trace. Pick a trace and the Steps tab follows it, so logs, traces, and memory line up without re-filtering. This is what lets you connect "what the user said" to "what the agent did."
Sampling and Batching Controls
Defaults cover most cases, but you can override them. Set a sampling strategy (always, never, ratio, or inherit from the parent trace) and tune queue size, batch size, and export timeouts. A ratio strategy, for example, sends 25 percent of traces when volume is high. These controls follow OpenTelemetry conventions and apply to traces and logs alike.
Prompt, Evals, and Deployment Tooling
VoltOps is more than a viewer. The same console manages versioned prompts, runs evaluations against your agents, and deploys projects through one-click GitHub integration with managed infrastructure. Knowledge bases for RAG and automation actions and triggers round out the platform, so observability sits next to the workflow it measures.
Pros and cons
Pros
- No-data-leaves-the-machine local mode, which makes it easy to debug sensitive agent runs without a privacy review.
- Works with any framework, not just VoltAgent, as long as you emit OpenTelemetry traces.
- Production export needs no code changes, just two environment variables.
- The trace and timeline view makes multi-agent and tool-calling flows readable far faster than log grepping.
- A free Developer tier with 250 monthly traces and 50,000 traces on the Core paid plan covers small projects.
Cons
- The deepest value is for TypeScript developers; teams on Python frameworks won't get the same out-of-box agent wiring.
- Remote export, evals, and larger trace volumes sit behind paid tiers, so real production monitoring has a cost.
- The console leans on your app exposing a local server, so environments that can't serve localhost need the tunnel or cloud path instead.
- Managed Memory is limited to US and EU regions, which matters if your data has to stay elsewhere.
Frequently asked questions
VoltOps is used to observe and debug AI agents and LLM applications. It shows each run as a trace with a timeline, lets you inspect prompts, model responses, tool calls, and memory, and exports that data to production over OpenTelemetry.
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