Every trace, in full
The span tree for every trace: plans, LLM calls, tool calls, retrievals and sub-agents, with inputs, outputs, timing, tokens and cost.
Tracing in the docsAgent observability that doesn’t stop at the trace. See it, check it, fix it.
Trodo records every trace your agent produces, from the first LLM call to the last tool, then checks each one and tells you which failed, why, and what to change.
- Every span, tool call, token and cost
- Every production trace checked, not a sample
- Failures grouped with their cause
- OpenTelemetry, Python and Node.js
What is agent observability?
Agent observability is seeing what an agent did in production: every step it planned, every model call, every tool it used, what each returned, how long it took and what it cost. It is the agent equivalent of application monitoring, built around traces and spans instead of requests.
It differs from LLM observability in scope. LLM observability looks at single model calls. An agent makes many calls, picks tools, retries, hands work to sub-agents and decides when it is done. Most real failures live in that orchestration, so agent observability follows the whole trace end to end.
Seeing a trace is only half the job. An agent can finish without an error and still give the wrong answer. Trodo checks every trace as it arrives, so the failures that never throw show up as issues instead of as customer complaints.
What Trodo gives you. From the trace to the fix.
Checks on every trace
Code checks, semantic checks and Trodo’s own models decide first; an LLM judge only sees what they can’t settle. Every trace scored at a fraction of the cost.
How checks workFailures grouped, with a cause
Traces that fail the same way become one issue, with the failing step, the evidence and how many users it touched.
Issues in the docsA proposed fix
Each issue comes with a change to the agent or to the check. Approve it and it ships as a new version you can roll back.
Cost and latency per trace
Token and tool spend per trace, per agent, per user. Catch a runaway loop before the bill does.
Cost trackingKeep your instrumentation
Auto-instrumentation for OpenAI, Anthropic, LangChain, LlamaIndex, the Vercel AI SDK and more, or send OpenTelemetry you already export.
Supported frameworksAgent observability, LLM observability and APM
| Capability | Trodo | LLM tracing tools | APM |
|---|---|---|---|
| Model call traces | Partial | ||
| Multi-step agent traces | Partial | – | |
| Tool call success, latency and cost | – | ||
| Every production trace checked | – | – | |
| Failures grouped by cause | – | – | |
| Proposed fixes, versioned | – | – | |
| OpenTelemetry ingest | Partial |
Questions? Quick answers.
What is agent observability?
Seeing what an agent did in production: every planned step, model call and tool call, with inputs, outputs, latency and cost, organised as traces and spans. Trodo adds a check on every trace, so failures that don’t throw an error are caught too.
How is agent observability different from LLM observability?
LLM observability covers single model calls. Agent observability covers the whole trace: plans, tool choices, retries and hand-offs between sub-agents, which is where most agent failures happen.
Does Trodo work with OpenTelemetry?
Yes. Point your existing OpenTelemetry exporter at Trodo, or use the Python or Node.js SDK, which auto-instruments the common frameworks and model providers.
Can Trodo run alongside Datadog or another APM?
Yes. Trodo covers the agent layer and runs alongside whatever watches your infrastructure.
How much does it cost?
Developer is free forever with 10,000 units and 1,000 executions a month. Pro is $249 a month with 250,000 units and 50,000 executions. Every feature is on every plan.