Derivations are snippets of code that run against traces and experiments to extract data, monitor for errors, and more. Derivations can optionally run an agent to analyze the trace (a variant of LLM-as-judge that we call Agent-as-judge). They show up as columns in the Gentrace UI.Documentation Index
Fetch the complete documentation index at: https://next.gentrace.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.

Structure of a derivation
Language
Write derivations in Python or JavaScript.Return type
All derivations must return a typed value. The type is specified in the dropdown at the bottom of the derivation and must match the return type of the function. Some types can be marked as “eval”. Eval derivations are averaged to compute a trace’s score.
Function signature and arguments
Derivations are functions written in Python or JavaScript. Derivations receive the following arguments:- The trace
- (If available) The source test case from the test dataset
- All other derivations in the same view
LLM-as-judge (Agent-as-judge)
Derivations can use an LLM to analyze traces withcallAgent() / call_agent().
The function accepts parameters for instructions, resources (like the trace), output schema, and optionally images.
Running derivations
Derivations run in the context of a view. Derivations are run in three ways:- Automatically by Gentrace Chat
- Automatically on trace ingest when sampled according to the view’s auto-run settings
- Manually, by:
- Pressing “Run last 10” or “Run last 100” in the top bar of the view
- Right clicking on a column header in the table
- Right clicking on a row or cell in the traces table
- Pressing “Run” with a derivation selected
