The production runtime for AI agents. Deploy, monitor, and iterate on agent workflows that work at scale — with full observability, intelligent retries, and zero infra reinvention. Built for engineers who ship.
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Status
Free
Early access
The problem we're solving
🔦
Critical
Zero observability
Agents fail mid-run. Nobody knows at which step, why, or what it cost. A black box with an unpredictable price tag.
💥
Critical
Reliability theater
Works in dev, dies in production. One API timeout kills the entire workflow with no fallback or recovery.
💸
High
Cost blindness
Surprise $10K LLM bills with zero attribution per agent or job. No way to know what's burning your budget.
🔁
High
Perpetual reinvention
Every team rebuilds the same executor, queue, and logger from scratch. Sprint after sprint wasted on plumbing.
How it works
01
Define
Describe your agent in a config file — model, tools, retry policy, trigger, and observability settings.
02
Deploy
One command. Velorith handles the runtime, queueing, scaling, and execution layer entirely.
03
Monitor
Full step-level traces, cost per run, failure alerts, and rollback — live in your dashboard.
What's in the runtime
01
Agent execution engine
Define your agent in config. Velorith runs it — reliably, consistently, at any scale. No boilerplate required.
02
Step-level trace observability
Every LLM call, tool use, input, output, latency, and cost — captured and queryable. Full visibility from trigger to result.
03
Intelligent retry engine
Configurable retry policy with exponential backoff and fallback model support. Agents survive API hiccups.
04
Webhook + cron triggers
Run agents on events or schedules. Connect any system via webhooks. No custom scheduler infra required.
05
Cost + ops dashboard
Real-time run status, spend per agent per day, and full trace drill-down. Know exactly what's happening at all times.