Your agent ran. Something broke. Now you know exactly why.
ZeroOps traces every decision across every agent in the chain — in plain language your engineers, your CTO, and your compliance team all understand.
$ pip install zeroops from zeroops import trace trace.init("my-agent")
Hours lost manually tracing failures across agent logs.
Non-deterministic bugs you can't reproduce.
Compliance blocks your production deploy without an audit trail.
Step-by-step replay of every run. Rewind to any decision.
Every LLM call, tool call, and agent handoff — captured with durations, token counts, and payloads. Not a log dump. A timeline you can navigate.
{
"span": "planner.plan",
"duration_ms": 412,
"input_tokens": 487,
"output_tokens": 325,
"status": "ok"
}{
"span": "researcher.search_docs",
"duration_ms": 890,
"input_tokens": 0,
"output_tokens": 0,
"status": "ok"
}{
"span": "researcher.rerank",
"duration_ms": 320,
"input_tokens": 384,
"output_tokens": 256,
"status": "ok"
}{
"span": "coder.write_diff",
"duration_ms": 1280,
"input_tokens": 1488,
"output_tokens": 992,
"status": "ok"
}{
"span": "coder.run_tests",
"duration_ms": 640,
"input_tokens": 0,
"output_tokens": 0,
"status": "ok"
}{
"span": "reviewer.parse_json ×3",
"duration_ms": 1140,
"input_tokens": 1872,
"output_tokens": 1248,
"status": "fail",
"error": "JSONDecodeError at line 3"
}Reviewer failed to parse Coder's output as JSON on all 3 retries. The Coder's tool schema expects diff: string, but it returned diff: object.
From install to root cause in three steps.
Instrument
Two lines of code. OpenTelemetry-native. Works with LangChain, CrewAI, and the OpenAI Agents SDK out of the box.
from zeroops import trace trace.init("checkout-agent")
Trace
Every LLM call, tool use, and agent handoff is captured automatically with inputs, outputs, tokens, and timing.
Diagnose
Plain-language root-cause summaries — not raw logs. Share a link. Your CTO and compliance lead will actually read it.
Tool schema mismatch on Coder. Reviewer expects string, got object.
One SDK. Every agent framework.
Native, OpenTelemetry-compatible instrumentation for the frameworks your team already ships with — no lock-in, no bespoke adapters.
pip install zeroops + 2 lines. Works with any OTel-compatible runtime.One trace. Three answers.
Stop grepping logs.
Replay any failed run in seconds. Every span, every payload, every retry — searchable, shareable, permalinked.
Ship the pilot to production.
SLA-grade visibility, alerting on failure patterns, cost tracking per agent. Confidence to move from demo to load.
An audit trail that stands up.
Immutable record of every agent decision. Fintech- and healthtech-ready. Export any session as PDF or CSV.
Everything you need to run agents in production.
Multi-agent trace waterfall
See every span across every agent on one timeline. Nested handoffs, parallel calls, retries.
Time-travel replay
Rewind to any decision. Inspect the exact prompt, tool payload, and model output at that moment.
Root-cause summaries
Plain language explanations of what broke and why — generated from the trace, not from vibes.
Token & cost tracking
Per-agent, per-span cost attribution. Spot the runaway loop before the invoice arrives.
Alerting on failure patterns
Route to Slack, PagerDuty, or webhook. Alert on rate spikes, cost anomalies, or schema drift.
Audit-grade export
One-click PDF or CSV of any session. Timestamps, hashes, and provenance for your compliance team.
Track spending
Across multiple agents.
Token Counts
Track, save, and monitor every token your agent sees.
Cost Tracking
Manage and visualize agent spend with up-to-date price monitoring.
Fine-tuning
Fine-tune specialized LLMs up to 25x cheaper on saved completions.
Trusted by the teams shipping agents to production.
From engineers to product owners, here's what people say about running multi-agent systems with ZeroOps.
We went from 'the agent did something weird' to 'the reviewer failed to parse the coder's JSON output' in under two minutes. ZeroOps is the debugger we wish we had six months ago.
I can finally show our board exactly where an agent chain failed and what it cost us. That's the difference between a demo and a production system.
Our compliance team loves the audit trail. Our engineers love the replay. As a product owner, I love that we don't have to choose between the two.
We had three teams building agents in parallel. ZeroOps gave us one place to see latency, token spend, and failure patterns across all of them.
The OpenTelemetry integration meant we didn't have to rip anything out. We added two lines of code and started getting traces the same day.
Non-deterministic bugs were killing our release confidence. Being able to replay a failed run step-by-step changed how we think about agent reliability.
Start free. Scale when you're ready.
For solo builders and prototypes.
- 1 project
- 10K traced events / mo
- 7-day retention
- Community support
For teams shipping agents to real users.
- 5 seats
- 1M traced events / mo
- 30-day retention
- Alerting & webhooks
- Slack + email support
Going beyond? Let's chat
- Everything in Team plus:
- SLA
- Slack Connect
- Custom SSO
- On-premise deployment
- Custom data retention policy
- Self-hosting (AWS, GCP, Azure)
- SOC-2, HIPAA, NIST AI RMF
No procurement required for Team — start with a card.
Need help building agents?
We've tested 400+ agents. We know which ones actually work.
The future is High Agency.
Are you ready to build it?
Install ZeroOps in under a minute. Get your first trace before your coffee cools.