Vantra gives engineers full visibility into every LLM call, tool invocation, and decision their agents make in production — before users find the bugs.
No credit card required · 3-line setup · works with any framework
Duration
4,231ms
Tokens
1,420
Cost
$0.0042
Status
ok
Add Vantra to any Python agent in under a minute.
# Before: flying blind # After: full observability import vantra vantra.init( api_key="van_live_...", project="my-agent" ) @vantra.trace def run_agent(message): # your existing code, untouched return agent.run(message)
pip install vantra — that's it. Works with OpenAI, Anthropic, LangChain, or raw API calls.
init() + one decorator. No changes to your agent logic. Auto-patches LLM clients.
Every LLM call, tool use, latency, token count, and cost — live in your dashboard.
Built for engineers who are tired of finding out about agent failures from angry users.
See every step of every agent run. Nested spans, latency bars, input/output inspection — like Chrome DevTools for your agent.
Know exactly what each agent run costs. Per model, per project, per day. Catch cost spikes before your bill does.
Get emailed when error rate spikes, latency blows up, or cost goes off the rails. Before your users notice.
LangChain, LlamaIndex, raw OpenAI/Anthropic, custom agents — if it makes LLM calls, Vantra captures it.
When a span fails, see the exact input that caused it, the error message, and which tool or model was responsible.
Dashboards for token usage, model distribution, and latency trends. Make data-driven decisions about your stack.
The others are either too expensive, too complex, or built for someone else.
| Tool | Setup | Pricing | Alerts | Framework |
|---|---|---|---|---|
| Vantra | 3 lines | Free → $79/mo | Built-in | Any |
| LangSmith | Medium | $39/user/mo | Limited | LangChain-first |
| Langfuse | Medium | Self-host or $ | None | Any |
| Datadog LLM | Complex | $3K+/mo | Yes | Any |
Start free. Pay when you scale.