learning-commons-evaluators (Python)

Python SDK for Learning Commons evaluators, which measure the quality of AI-generated educational content by assessing specific dimensions of text and identifying areas for improvement.

Every evaluator is built from its shared contract under evals/ (prompts, models, schemas) - and every one returns the same result envelope, so generic code works across all of them.

Installation

pip install learning-commons-evaluators

Requires Python 3.11+. Provider API keys are passed in explicitly; the SDK never reads them from the environment, so setting GOOGLE_API_KEY in your shell does not satisfy google_api_key. Omitting a key an evaluator needs raises ConfigurationError at construction, before any I/O.

Quick start

from learning_commons_evaluators import PurposeClarityEvaluator, read_outcome

evaluator = PurposeClarityEvaluator(google_api_key="your-google-key")

evaluation = evaluator.evaluate_sync(
    text="Trees are important plants that grow in many parts of the world. ...",
    grade_level=5,
)

print(evaluation.result.complexity_score)   # e.g. "slightly_complex"
print(evaluation.result.reasoning)
print(evaluation.metadata.model)            # "google:gemini-3-flash-preview"

outcome = read_outcome(evaluation, PurposeClarityEvaluator.metadata.outcome)
print(outcome.score)                        # the same verdict, as one comparable string

await evaluator.evaluate(...) is the primary form; evaluate_sync wraps it for synchronous code. Inputs are the contract's names (text, grade_level, student_text, feedback_text), passed as keywords or as the evaluator's typed input model (PurposeClarityInput).

Every evaluator resolves to the same three-part envelope, so generic code works across all of them:

class EvaluationResult(BaseModel):
    evaluator: str                     # registry id, e.g. "text_complexity.ela_reading.vocabulary_complexity"
    result: ResultT                    # the evaluator's own payload, exactly as its output schema declares it
    metadata: EvaluationMetadata


class EvaluationMetadata(BaseModel):
    model: str                         # "provider:model" that ran; "a+b" when several did
    processing_time_ms: int
    token_usage: EvaluationTokenUsage  # .input_tokens, .output_tokens

result is the model's structured output with keys and values unaltered. An evaluator whose contract declares several steps sums the token usage across them, and model names every model that ran, joined with +. Which models run can depend on the input: Vocabulary Complexity takes a different branch for grades 3–4 than for 5–12, but construction validates the union of keys it could need, so it demands both keys at any grade.

When you want one comparable value per evaluation regardless of evaluator, use read_outcome. score is always a string, or None for an evaluator that declares no single verdict — which today means Math Standards Alignment.

Evaluators

Text complexity — how demanding a text is for a given grade. Each takes { text, grade_level } and returns a complexity_score on a four-level scale — slightly_complex, moderately_complex, very_complex, exceedingly_complex — with reasoning. Purpose Clarity has a fifth value, more_context_needed, so a match over the four above is not exhaustive for it. Payloads carry more than the verdict, and how much varies: Vocabulary Complexity adds tier_2_words, tier_3_words, archaic_words and other_complex_words; only Sentence Structure returns just the score and reasoning. Each evaluator's payload model (VocabularyComplexityOutput and so on) is the authoritative shape.

Grade band — takes { text } only, and determines the grade rather than judging against one. Returns grade_band, alternative_grade_band, scaffolding_needed, reasoning. Bands are K-1, 2-3, 4-5, 6-8, 9-10, 11-12 — spans on the CCSS text-complexity scale, not single grades.

Feedback quality — judges a teacher comment on a student's writing. Each takes { student_text, feedback_text } and returns a binary quality_score (the integer 0 or 1, not a bool) with reasoning, key_features and proposed_adjustment. key_features is a model, not a dict, and its criterion keys differ per evaluator — Tone Appropriateness declares neutral_professional_language, targets_work_not_student and praise_proportionate_to_work. Read them with list(ToneAppropriatenessOutput.model_fields["key_features"].annotation.model_fields).

Standards alignment — checks a math item against a standard, component by component. See Math Standards Alignment below, which has two entry points.

Discovering evaluators

Every evaluator is listed in a registry, keyed by the registry id that appears on each result:

from learning_commons_evaluators import get_evaluator, get_evaluators

for m in get_evaluators():
    print(f"{m.name} ({m.id}) — grades {', '.join(m.supported_grades)}")

# Renamed ids still resolve, so a stored result stays identifiable.
get_evaluator("conventionality").name          # "Meaning Directness Evaluator"
get_evaluator("grade-level-appropriateness").id  # "text_complexity.ela_reading.grade_level_appropriateness"

Both return EvaluatorMetadata — id, stable_id, id_history, name, description, supported_grades, default_providers, required_credentials, and outcome where the evaluator declares a single verdict. required_credentials lists only non-LLM services — it is ("learning_commons_api_key",) for math standards alignment and empty for the other evaluators, so it is not the answer to "which keys does this need". Provider keys follow default_providers: (Provider.GOOGLE,) means supply google_api_key. To run an evaluator, import it by name — the metadata does not say which named inputs it takes, and each evaluator's are different.

Configuration

Pass an EvaluatorConfig or its fields as keyword arguments:

Old evaluator ids still resolve: get_evaluator("grade-level-appropriateness") finds the evaluator now registered as text_complexity.ela_reading.grade_level_appropriateness.

Telemetry

One event per evaluation, on success and on failure. Telemetry never includes the text you evaluate: the event carries its length, the evaluator, the grade level you passed, the provider and model, latency, status, any error class name, token counts, and the SDK version. There is no option to send the text itself.

Events are anonymous, attributed to a client id kept in ~/.config/learning-commons/config.json (%APPDATA%\learning-commons\config.json on Windows), which is the same file the TypeScript SDK uses. Setting TelemetryOptions(learning_commons_api_key=...) attributes them to your Learning Commons user instead.

Sending is fire-and-forget on a background thread: it never blocks an evaluation, never changes a result, and never raises. Failures are logged at warning, so a restricted-egress environment sees a warning per evaluation at the default level; telemetry=False silences it, and builds no telemetry client at all.

Knowledge Graph

Evaluators that align to academic standards read them from the Learning Commons Knowledge Graph, using the learning_commons_api_key above. The client is also usable on its own — it owns an HTTP connection pool, so close it when you are done:

from learning_commons_evaluators import KnowledgeGraphClient

async with KnowledgeGraphClient(api_key) as kg:
    matches = await kg.search_standards("3.MD.C.7.d")           # StandardNotFoundError if unknown
    components = await kg.get_learning_components(matches[0].case_identifier_uuid)

search_standards takes an optional jurisdiction (default Multi-State) and academic_subject; more than one match means the code is reused across frameworks and you choose. Evaluators that own a client close it from their own aclose() / close(), which every evaluator accepts and which does nothing for the ones that own nothing.

Math Standards Alignment

The one evaluator that reads a dependency. It judges a question against every learning component the Knowledge Graph holds for one standard, and reports a verdict per component plus the aligned and total counts. There is no single score — the contract declares no outcome — so read_outcome reports None for it, as it does in the TypeScript SDK.

There are two ways to name the standard, because there are two ways callers have one. evaluate() takes the CASE Network UUID — what a standard picker hands you, and what the Knowledge Graph itself keys on. evaluate_by_code() takes the standard the way a teacher names it, resolves it to a UUID, and runs the same evaluation.

from learning_commons_evaluators import MathStandardsAlignmentEvaluator

async with MathStandardsAlignmentEvaluator(
    anthropic_api_key="...", learning_commons_api_key="..."
) as evaluator:
    # The primitive: one UUID names exactly one standard.
    evaluation = await evaluator.evaluate(
        question="A playground is shaped like an L ...",
        case_identifier_uuid="6ba25656-d7cc-11e8-824f-0242ac160002",
    )

    # The same evaluation, reached from a code.
    evaluation = await evaluator.evaluate_by_code(
        question="A playground is shaped like an L ...",
        statement_code="3.MD.C.7.d",
        jurisdiction="Multi-State",   # optional; Multi-State is Common Core
        grade_level="3",              # optional; see below
    )

print(evaluation.result.statement_code, evaluation.result.aligned_count)

Both cost two Knowledge Graph calls and return the same payload. Passing one method's input to the other names the method that takes it rather than calling the key unknown.

Spell the code the way its jurisdiction spells it. A code is a lookup key, not an identity: the Knowledge Graph holds one copy of a standard per adopting jurisdiction, each with its own UUID and often its own spelling — Ohio writes 3.MD.7 where Common Core writes 3.MD.C.7, New York NY-3.MD.7, and Florida's B.E.S.T. framework uses a different scheme entirely (MA.3.GR.2.2). Passing a Common Core code with jurisdiction="Ohio" raises StandardNotFoundError rather than finding Ohio's equivalent. Jurisdiction variants of the same Common Core standard share their learning components, so the choice between them does not change the judgement; a different framework, such as Florida's, genuinely does.

grade_level disambiguates. Within one framework a code can be reused across courses, and the Knowledge Graph returns one result per copy. Passing the grade separates them, at the cost of one extra lookup per candidate — and only when a code turned out to be ambiguous, never on the ordinary path. When nothing separates them, the first is evaluated and the choice is logged at warning with the alternatives. evaluation.result.statement_code always reports the Knowledge Graph's own spelling of whatever was resolved.

evaluate_by_code()'s inputs are the contract's, under the contract's names, plus that one optional extra — so anything written against the registry is a valid call, and the contract's own fixtures drive this evaluator as they drive every other one.

A UUID names any standard, including another subject's. evaluate() reads the standard before judging it and raises InputValidationError for a non-mathematics one, before fetching components and before any model call. evaluate_by_code() needs no such check: its search is already scoped to Mathematics, so a non-math code never resolves.

Errors

Errors are grouped by fault domain, so you can catch by who is at fault rather than by individual failure. All extend EvaluatorError, which carries a boolean retryable.

EvaluatorError, EvaluationError and DependencyError are abstract; the SDK always raises a concrete subclass. Every DependencyError carries dependency, status_code, request_id and model, so which external system failed is data rather than a class per provider.

from learning_commons_evaluators import (
    DependencyError,
    InputValidationError,
    RateLimitError,
)

try:
    evaluation = evaluator.evaluate_sync(text=text, grade_level=5)
except RateLimitError as error:
    retry_later(error.retry_after_ms)
except DependencyError as error:
    report_upstream_outage(error.dependency, error.status_code)
except InputValidationError as error:
    fix_the_row(error)

retryable is data, not hierarchy — read the flag rather than memorising which class means what. The SDK already retries internally per max_retries, so an error that reaches you has exhausted that budget.

More resources

• Local development – Local setup, testing, regenerating from evals/
• Error handling — Exception hierarchy, retries, and how provider failures are classified
• Evaluator contracts — The shared evals/ registry every SDK is built from

Full reference at our docs site.

License

MIT