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Agents that actually run in production.

Velorith is the runtime layer your agents run on. You write the logic. Velorith controls execution — fallback routing, loop detection, cost enforcement, and step-level tracing. Ship agents that hold up at scale.

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What Velorith actually controls

Velorith is the execution layer your agents run on — not a wrapper, not a library. It owns the runtime: fallback routing across claude-opus-4-7 → gpt-5.4 → gemini-3.1-pro-preview, loop detection, cost enforcement, step tracing, and human escalation. You write agent logic. Velorith guarantees it runs.

  • Agent execution engine
    Declare your agent logic. Velorith owns the execution — scheduling, queueing, scaling. No infra to manage.
  • Multi-provider fallback chains
    claude-opus-4-7 → gpt-5.4 → gemini-3.1-pro-preview. Velorith routes automatically. No manual failover. No downtime.
  • Loop detection + hard limits
    Max steps, max cost, loop pattern detection. Velorith kills runaway agents before they burn your budget.
  • Step-level trace observability
    Every LLM call, tool result, token count, cost, and latency — traced per step, queryable by run ID.
  • Universal trigger layer
    n8n, Make, Zapier, webhooks, cron, or direct API — Velorith accepts any trigger and takes full control from there.
  • Cost + ops command center
    Real-time spend by agent, by model, by day. Hard budget ceilings. Slack alerts. No surprise invoices.
# Velorith owns the runtime. You own the logic.

name: "incident-response-agent"
model: "claude-opus-4-7"
fallback: "gpt-5.4" | fallback_2: "gemini-3.1-pro-preview-preview"
max_retries: 3 | max_steps: 20
trigger: "webhook" # n8n · Make · Zapier · any source
loop_detection: true | max_cost_usd: 2.00
trace_steps: true | cost_tracking: true

$ velorith deploy incident-response-agent
✓ Runtime active · Velorith controls execution
✓ 3-provider fallback · Loop guard on · $2 ceiling
Critical
Agentic loops
Autonomous agents reason in circles with no exit. claude-opus-4-7's task budgets help — but don't replace a runtime kill switch.
Critical
Context blowout
1M token windows exist but cost money. Multi-step pipelines silently blow past limits — model hallucinates, nobody catches it.
Critical
Parallel tool failures
One call in a parallel batch fails silently. Agent continues with partial data and produces confident wrong output. No signal fires.
Critical
Runaway spend
claude-opus-4-7 costs $25/M output tokens. A stuck agent loop for 14 hours = $34K invoice. Happened. Will happen again without hard limits.
High
Prompt injection
Tool output from web scrapers, emails, or DBs rewrites agent instructions. Documented attacks in the wild since late 2024.
High
Zero trace on failure
Agent fails at step 8 of 15. LangGraph gives you a stack trace. You need to know which LLM call, which prompt, which cost, which token.
High
Single-provider fragility
Anthropic had 23 incidents in 18 months. OpenAI had 47. If your agent is hardcoded to one provider, every incident is your incident.
High
Framework ≠ runtime
LangGraph, CrewAI, PydanticAI build agent logic well. None ship with production retry, observability, fallback, or cost controls. That gap is Velorith.
You define the logic
Write your agent in any framework — LangGraph, PydanticAI, raw API calls. Declare the runtime config: models, limits, triggers, observability.
Velorith takes control
One deploy command. Velorith owns execution from there — routing, retries, fallback chain, loop detection, cost ceiling, trace logging. Your code doesn't change.
Ship with confidence
Full trace on every run. Hard budget enforcement. Instant Slack alerts on failure. You see exactly what each agent did, spent, and why it succeeded or failed.