Coming from Another Evaluation Tool

If you wrote evaluators for Opik, Braintrust, LangSmith or Langfuse, most of what you know carries over. This page maps their concepts to Oodle's.

Concepts in common

Concept	In Oodle
The function that scores	evaluate(ctx) in a code template
What it gets	ctx: the span's input, output and attributes, the agent's steps in an experiment, the evaluator's settings, and other evaluators' scores. See the ctx object
What it returns	EvaluationResult(scores=[Score(...)]), one or more named scores
Score types	NUMERIC, BOOLEAN, CATEGORICAL, and higher_is_better on each score
Pre-built metrics	Built-in code checks, and the same checks as functions in the oodle_eval library
Parameters of a scorer	Template settings, with values per evaluator
Code that many scorers share	Shared libraries
Combined or composite scores	combine in the library, or an evaluator that reads other evaluators' scores
Online evaluation	An evaluator (rule) with filters, sampling and an hourly cap
Offline evaluation	Experiments on a dataset
Expected output	ctx.expected_output in an experiment. The reference checks use it when you set no reference. On a live span, pass a reference as a setting

The sandbox runs Python with the standard library modules that are useful for scoring and the oodle_eval library. Each span gets 5 seconds and 128 MB, and the code has no network access. Rewrite a scorer that calls a model as an LLM-as-Judge evaluator, and combine its score with code checks by reading its scores.

Opik

Opik	Oodle
BaseMetric subclass with score(...)	evaluate(ctx)
score_result.ScoreResult(name, value, reason)	Score(name=..., value=..., comment=...)
A list of ScoreResult from one metric	A list of Score in one EvaluationResult
Equals, Contains, RegexMatch, IsJson	metrics.exact_match, metrics.contains, metrics.regex_match, metrics.json_validity
LevenshteinRatio, SentenceBLEU, ROUGE, Sentiment	metrics.levenshtein_ratio, metrics.bleu, metrics.rouge, metrics.sentiment
ConversationDegenerationMetric, KnowledgeRetentionMetric	metrics.conversation_degeneration, metrics.knowledge_retention
AggregatedMetric(metrics=[...], aggregator=...)	combine.weighted_mean(scores), or your own arithmetic on the list
Online evaluation rule	Evaluator with filters and a sampling rate

A metric that you constructed with arguments, such as Contains(case_sensitive=True), is a call with keyword arguments: metrics.contains(ctx, values=[...], case_sensitive=True). The cookbook shows an Opik custom metric and the same evaluator in Oodle side by side.

Braintrust

Braintrust	Oodle
Code scorer handler(input, output, expected, metadata)	evaluate(ctx). Read ctx.observation.input, ctx.observation.output and ctx.observation.metadata
A number, or {"name", "score", "metadata"}	Score(name=..., value=..., comment=...) in an EvaluationResult
autoevals ExactMatch, Levenshtein, NumericDiff, JSONDiff	metrics.exact_match, metrics.levenshtein_ratio, metrics.numeric_diff, metrics.json_diff
Scorer parameters	Template settings
project.scorers.create(...) and pushing functions from the CLI	A code template, created in the UI, with oodle genai templates create, or through the API
Online scoring rule	Evaluator with filters and a sampling rate. Add a filter on resource::oodle.project to score one project
Scorer that calls your own service	A webhook evaluator
Eval(), experiments and datasets in your code	The same code, pointed at Oodle. See Migrate from Braintrust

Where a Braintrust scorer reads expected, read ctx.expected_output in an experiment. On live spans, give the reference as a setting (reference=).

You can also keep your Braintrust SDK code and its scorers. Oodle serves a Braintrust-compatible API: Eval() with scorers in your code, init() and log(), datasets and log_feedback() send their experiments, rows and scores to Oodle when you set BRAINTRUST_API_URL and BRAINTRUST_API_KEY. A scorer that you run as a service becomes a webhook evaluator. See Migrate from Braintrust, which also has a script that copies your projects, datasets and experiments.

LangSmith

LangSmith	Oodle
perform_eval(run, example)	evaluate(ctx)
run["inputs"], run["outputs"]	ctx.observation.input, ctx.observation.output
example["outputs"] (reference outputs)	ctx.expected_output in an experiment, or a reference setting
{"metric_name": score} with several keys	Several named Score objects
Composite evaluator (weighted average or sum of feedback keys)	An evaluator that reads other evaluators' scores and uses combine.weighted_mean, or arithmetic of your own
Online evaluation rule	Evaluator with filters and a sampling rate

A LangSmith composite score has no value when one of its inputs is missing on a run. In Oodle, an evaluator that reads scores runs only on the spans that every input evaluator scored, which gives the same result.

Langfuse

Oodle's code evaluator contract follows Langfuse's: evaluate(ctx) returns an EvaluationResult of Score objects with name, value, data_type and comment. Most Langfuse Python code evaluators run in Oodle with few changes.

Langfuse	Oodle
ctx.observation.input, .output, .metadata	The same
ctx.observation.tool_calls	text.tool_calls(ctx), which gives {"id", "name", "arguments"} dicts
ctx.experiment.item_expected_output	ctx.experiment.expected_output, or the shortcut ctx.expected_output
ctx.experiment.item_metadata	ctx.experiment.item_metadata
Score(name, value, data_type, comment)	The same, plus higher_is_better
One evaluator's scores in another	ctx.scores and ctx.score(...)

If your evaluator defines its own Score or EvaluationResult dataclasses so that it runs outside Langfuse, keep them: Oodle reads the fields that it needs from your classes.

Move an evaluator

1. Find the checks that the library already has, and use them. Most heuristic metrics are in metrics.
2. Move each constant that differs between uses (a keyword list, a threshold) into a setting.
3. Move helper code that several scorers share into a shared library.
4. Run the template on sample spans in the editor, or with try_code in MCP, before you create evaluators.