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Tracing
Capture every step of your LLM workflow and send it to Avido for replay, evaluation, and monitoring.
When your chatbot conversation or agent run is in flight, every action becomes an event.
Bundled together they form a trace – a structured replay of what happened, step‑by‑step.
Event | When to use |
---|---|
trace | The root container for a whole conversation / agent run. |
llm | Start and end of every LLM call. |
tool | Calls to a function / external tool invoked by the model. |
retriever | RAG queries and the chunks they return. |
log | Anything else worth seeing while debugging (system prompts, branches, errors…). |
The full schema lives in API ▸ Ingestion.
Recommended workflow
- Collect events in memory as they happen.
- Flush once at the end (or on fatal error).
- Add a
log
event describing the error if things blow up. - Keep tracing async – never block your user.
- Evaluation‑only mode? Only ingest when the run came from an Avido test → check for
testId
from the Webhook. - LLM events should contain the raw prompt & completion – strip provider JSON wrappers.
Ingesting events
You can send events:
- Directly via HTTP
- Via our SDKs (
avido
)
Tip: map your IDs
If you already track a conversation / run in your own DB, pass that same ID as referenceId
.
It makes liftover between your system and Avido effortless.
Next steps
- Inspect traces in Traces inside the dashboard.
Need more examples or have a tricky edge case? Contact us and we’ll expand the docs! 🎯