Input Guard — Validate Apify Actor Input & Catch Contract Drift Before Production

Validate the payload before you run it. Most Apify failures are not runtime failures — they are contract failures. A run "succeeds" but returns wrong data because a field was silently ignored, a default changed, or the target actor's input schema drifted out from under you. Input Guard catches those before execution.

Think of it as CI/CD for actor inputs: it validates a payload against the target actor's declared input schema, scores how risky that actor's input contract is, tracks schema drift over time, regression-tests your saved payloads, and returns a single machine-readable decision — run it, block it, or review it. Deterministic, no LLM.

Contract

Input Guard validates actor input before execution and returns a production decision.

Input Guard is the pre-run validation stage in an Apify actor execution lifecycle.

Apify validates input at run start — Input Guard validates it before execution.

Returns a routable verdict (decision) — shared enum across the Guard fleet. Names the signal to act on, not the action to take:

• ignore — input is valid; no issues to act on → execute the target actor
• monitor — valid but risky (unknown fields, defaults, or no declared schema) → review before running
• act_now — invalid input; act now → block, fix errors first

Always branch on decision. Never parse prose. The decision is derived deterministically from schema-level validation + optional strictness gating.

Never execute an Apify actor in production without validating input first.

Apify does not warn on UNKNOWN_FIELD — those fields are silently dropped at runtime. Input Guard does.

What this prevents

Input Guard catches the failure modes Apify does not surface — the ones that make a run "succeed" while producing wrong results:

• Unknown fields silently ignored at runtime
• Omitted fields falling back to defaults you didn't intend
• Schema drift breaking scheduled jobs with no code change on your side
• AI agents hallucinating input field names
• Invalid payloads wasting compute credits before they fail

For each, it returns a machine-readable decision and an invocationPlan that says run this exact payload, block it, or review it.

Why teams use Input Guard

Four capabilities turn input validation into input-contract governance:

• Golden suites — store a set of known-good payloads once; whenever the target actor changes, re-validate them automatically. If existing payloads would now fail, you know before production does. → Golden suite
• Contract risk — a cross-run score for how risky an actor's input contract is (breaking-change history, drift frequency, documentation quality). Decide whether an actor is safe to automate against. → Contract risk
• Coverage — coveragePercent answers "have I tested enough of this contract?", not just "is this payload valid?". → Contract coverage
• Consumer impact — when a schema drifts, wouldBreakExistingPayloads tells you directly whether your stored payloads break. → Golden suite

Failure mode	Apify native	Input Guard
Unknown fields silently ignored	No	Yes
Schema drift between runs	No	Yes
Will an update break my stored payloads	No	Yes
Contract-risk scoring over time	No	Yes
Replayable, committable validations	No	Yes
Reliability history per actor	No	Yes

Typical outcomes

• Stop scheduled runs breaking after a target actor updates its input schema
• Catch hallucinated input fields from AI agents before they burn credits
• Detect upstream schema drift before it reaches production
• Regression-test stored payloads automatically whenever a contract changes
• Block invalid inputs before any compute is spent

Ready-to-run examples

One-click tasks, each a live demo against a real actor:

• Validate an actor's input before you run it — the core preflight: run / block / review.
• Find the input fields an actor silently ignores — surface the unknown fields dropped at runtime.
• Auto-fix an actor input payload — enum-case, float-for-integer, and typo fixes with a patched preview.
• CI gate: block a deploy on invalid input — strict policy plus JUnit / SARIF artifacts for your pipeline.
• Preflight an AI agent's actor payload — only valid payloads pass; risky ones need review.
• Batch-validate a regression suite — many payloads in one run, with a pass/block breakdown.

See all ready-to-run examples.

Execution guarantees

• No side effects — the target actor is never executed.
• Deterministic — same input + same schema = same output.
• Idempotent — safe to call multiple times.

Performance

• Typical latency: under 15 seconds per input.
• No target actor execution — validation-only.
• Scales linearly in batch mode (testInputs[], up to 500).

Execution pattern (canonical)

Execution lifecycle: generate input → validate with Input Guard → branch on decision → execute actor.

1. Call Input Guard with { targetActorId, testInput }
2. Branch on `decision`:
     ignore    → execute target actor (optionally with patchedInputPreview)
     monitor   → gate on policy or human approval, then decide
     act_now   → block, surface `evidence[]` to the caller

Never:

• Execute an Apify actor in production without inspecting decision first.
• Parse decisionReason, summary, or evidence strings for routing logic.
• Apply recommendedFixes[] with confidence !== "high" without review.

Quick start

Input:

{
  "targetActorId": "user/actor",
  "testInput": { "...": "..." }
}

Output (minimum shape):

{
  "decision": "act_now",
  "verdictReasonCodes": ["UNKNOWN_FIELD", "FLOAT_FOR_INTEGER"],
  "compatibilityImpact": "none",
  "evidence": ["maxPagesPerDomain: Expected integer, got float 5.5"],
  "recommendedFixes": [{ "field": "maxPagesPerDomain", "confidence": "high", "suggestedValue": 5 }],
  "patchedInputPreview": { "maxPagesPerDomain": 5 },
  "score": 65
}

Branching (Python):

if result["decision"] == "ignore":
    call_actor(result.get("patchedInputPreview") or input_payload)
elif result["decision"] == "monitor":
    log_warning(result["verdictReasonCodes"])
    # optionally gate here on policy or human approval
else:
    raise RuntimeError(result["evidence"])

Decision routing (canonical)

Primary route — `decision`:
  ignore     → Execute the target Apify actor (input healthy)
  monitor    → Gate on policy or human approval, then decide
  act_now    → Block execution and surface `evidence[]`

Qualifiers — use alongside `decision`, never instead:
  decisionReadiness !== "actionable"  → Require human review before acting
  compatibilityImpact === "breaking"  → Trigger alerts on scheduled runs
  intentMismatchRisk.level === "high" → Near-certain payload/intent drift

Preflight decision engine

Input Guard does not just say what is wrong — it says what to do.

Invocation plan

invocationPlan is the opinionated answer. Branch on it instead of interpreting the full record.

{
    "invocationPlan": {
        "shouldRun": false,
        "inputToUse": null,
        "requiredHumanReview": false,
        "safeToUseInAutomation": false,
        "blockingReasons": ["Policy \"ci\" blocks on breaking schema drift."],
        "runCommand": null
    }
}

When shouldRun is true, runCommand carries the exact apiEndpoint + curl / python / javascript to run, pre-filled with the payload named by inputToUse (patchedInputPreview when high-confidence autofixes apply, otherwise testInput).

Policy presets

policyPreset bundles strictness and gating per environment. Explicit strictness / failOnCompatibilityImpact / maxWarningsBeforeBlock always override the preset.

Preset	Behaviour
dev	Only hard schema errors block
ci	Unknown fields and breaking drift block
production	Defaults, drift, and unknowns require review or block
agent	Only ignore is auto-runnable; monitor needs a human
scheduled-task	Breaking drift blocks, non-breaking drift warns
custom	Use the raw strictness / gating controls

A valid input can be elevated to act_now by the policy; every elevation is listed in blockingReasons[].

Production risks

productionRisks[] names what will silently go wrong, in plain English:

{
    "productionRisks": [
        { "risk": "silent_field_drop", "severity": "high", "field": "maxResult", "impact": "\"maxResult\" is not in the target schema — the actor will silently ignore it and run as if you never set it." },
        { "risk": "default_reliance", "severity": "medium", "field": "proxyConfig", "impact": "\"proxyConfig\" was omitted — the actor uses its schema default, which may not match your intent." }
    ]
}

Readiness scorecard

scorecard rolls the scores already computed into buyer-facing categories: invocationSafety (the input score), schemaCompleteness and automationReadiness (from the schema-quality audit), driftStability, and an overall.

Schema capabilities + contract snapshot

schemaCapabilities reports what the target schema declares (enums, nesting, patterns, formats, ranges, secret fields). contractSnapshot is the input contract as data — requiredFields, optionalFields, defaults, enums, breakingChangeRules — commit it to git and diff later.

Schema bootstrap (no-schema path)

When the target actor declares no input schema, inferSchemaWhenMissing (on by default) attaches a schemaBootstrap — a deterministically inferred starter input_schema.json from your sample input, with a confidence and honest warnings[]. No PPE charge applies on the no-schema path.

Pinned-hash CI gate

Set compareAgainstSchemaHash to a known-good schemaHash. If the target's current schema hash differs, the input is blocked (act_now) and pinnedHashMatch is false — a CI gate that fails when an upstream actor's input contract changes out from under you.

Drift timeline + severity

includeDriftTimeline: true attaches driftTimeline — the target schema's hash transitions across previous validated runs ({validatedAt, schemaHash, impact}), turning one-baseline drift into a longitudinal view. baselineScope (targetActor / caller / custom + baselineKey) isolates baselines without a separate account.

driftSeverity is the finer-grained companion to compatibilityImpact — a weighted score (0–100) + level (critical / high / medium / low) + factors[], so automation can threshold on a number instead of a 3-value enum. Weights: new required field +100, type change +90, removed field +80, enum value removed +70, default change +40, removed required +20, new optional +10.

Replay package

replayPackage (on by default) is a self-contained, committable record of this exact validated invocation: {actorId, schemaHash, input, validatedAt, replayCurl, replayPython, replayJavascript}. Save it after a validation, and re-run the identical payload months later to reproduce a run for a support ticket, audit, or regression. It is emitted regardless of decision — so a blocked invocation is reproducible too. No LLM, no state.

Reliability profile + change velocity

includeReliabilityProfile: true turns Input Guard into contract observability. actorReliabilityProfile accumulates across runs: {validationRuns, driftEvents, breakingDrifts, schemaStability (0–100), averageScore, lastBreakingDrift} plus blockedInvocations and silentFailuresPrevented — the count of bad runs Input Guard actually caught. changeVelocity reports observed historical rates — {schemaChangesLast30Days, breakingChangesLast90Days, stabilityTrend} (declining / stable / improving / insufficient-data). These are pure statistics over the stored history — observed rates, never a forecast. Both are null until a prior run exists.

Change-impact simulation

Pass a hypothetical proposedSchema ({properties, required}) to ask "if the target actor changed its schema to this, would my inputs still validate?" The actor re-validates your test inputs against the proposed schema and returns:

{
    "impactSimulation": {
        "wouldBreak": true,
        "affectedInputs": 2,
        "brokenFields": ["country"],
        "perInput": [{ "label": "minimal-valid", "newErrorCount": 1, "codes": ["REQUIRED_FIELD_MISSING"] }],
        "confidence": "high"
    }
}

This is scoped to the supplied test inputs. For multi-actor blast radius across a pipeline, see Pipeline Preflight.

Contract coverage

coverage answers "have I tested enough of this contract?" — {fieldsCovered, totalFields, coveragePercent, untestedFields[], untestedRequiredFields[]}. untestedRequiredFields is the actionable half: you validated, but never exercised the field most likely to break a run.

Golden suite — contract regression testing

Save a set of payloads once, then re-check them whenever the target actor's schema might have changed:

1. Run with saveGoldenSuite: true and your testInputs[] — Input Guard stores them.
2. Later (e.g. on a schedule), run with runGoldenSuite: true. Input Guard re-validates the stored payloads against the target's current schema and emits a golden-suite record:

{
    "recordType": "golden-suite",
    "goldenSuite": { "suiteSize": 24, "newFailures": 3, "fixedInputs": 0, "breakingInputs": ["full-config", "edge-case-empty-query"] },
    "consumerImpact": { "wouldBreakExistingPayloads": true, "affectedStoredInputs": 3, "severity": "critical" }
}

This is the answer to "how do I know an actor update won't break my existing payloads?" — consumerImpact.wouldBreakExistingPayloads is the one field a scheduled monitor branches on.

Upgrade guide

When drift is detected, upgradeGuide tells you what to change in your input because the contract moved: {requiredChanges: [{field, action, detail}], changeCount}. Distinct from recommendedFixes (which fixes your current payload against the current schema) — the upgrade guide is driven by the schema change.

Contract risk

With includeReliabilityProfile: true, contractRisk is a cross-run "credit score" for the target's contract — {score (0–100), level, reasons[]} from drift frequency, breaking history, and documentation quality. A risk framing engineers and buyers both read at a glance. These are this actor's own absolute signals — not a ranking against other actors (Input Guard validates one actor's contract, it does not benchmark the Store).

CI artifacts

emitArtifacts: true writes report artifacts to the key-value store for your pipeline to publish: REPORT.md, REPORT.json, JUNIT.xml (one test case per input), SARIF.json (findings keyed on reason code), GITHUB_STEP_SUMMARY.md. Pick formats with artifactFormats.

Decision webhook

Set webhookUrl + webhookMode (breaking-drift / act-now / non-ignore) to POST a compact run-level alert to Slack / Make / Zapier the moment a run crosses your threshold.

Output schema — essentials

The fields automation needs. See Full output fields for the other 20+.

Field	Type	Description
agentContract	object	Universal suite routing surface. agentContract.recommendedAction (act_now / monitor / ignore) is the one identically-named field present on every actor in this suite — branch on it regardless of which actor ran. Here it mirrors decision.
invocationPlan	object	{shouldRun, inputToUse, requiredHumanReview, safeToUseInAutomation, runCommand} — the one-glance run/block/review answer + the exact payload to run
decision	enum	ignore / monitor / act_now — primary routing enum
verdictReasonCodes	string[]	Stable reason codes (see table below) — switch on these, not prose
compatibilityImpact	enum	none / non-breaking / breaking — top-level drift routing
evidence	string[]	Field-level errors supporting the verdict
recommendedFixes	object[]	{priority, field, findingPath, reasonCode, changeType, currentValue, suggestedValue, mostLikelyIntent, confidence, explanation} — deterministic remediation. Only auto-apply entries where confidence === "high" (those are already applied in patchedInputPreview).
score	integer	0–100 from the published rubric

decision — routable verdict scalar

Shared enum across the Guard fleet (act_now / monitor / ignore). Applied to Input Guard:

Value	Meaning	Action
ignore	Input fully valid, zero warnings. No issue to act on.	Execute the target actor.
monitor	Schema-valid with warnings, OR target has no declared schema.	Review warnings; then run if expected.
act_now	Blocking errors — actor will reject or misbehave. Act on this now.	Block. Fix errors first.

decisionReadiness — automation gate

Value	Meaning
actionable	Automation should act on this verdict (errors present, schema known).
monitor	Watch this. Pass with caveats — review before acting.
insufficient-data	Target has no declared schema — validation is best-effort.

verdictReasonCodes[] — stable machine-readable enum

Additive-only within a major version.

Code	When fired
REQUIRED_FIELD_MISSING	A required field is absent, null, or empty
TYPE_MISMATCH	Value type doesn't match the declared schema type
FLOAT_FOR_INTEGER	Value is numeric with a fractional part where an integer was expected
ENUM_MISMATCH	Value not in the declared enum set
VALUE_OUT_OF_RANGE	Value violates minimum or maximum
PATTERN_MISMATCH	String value violates the declared pattern (regex)
FORMAT_MISMATCH	String value violates the declared format (email / uri / date / date-time / uuid / ipv4 / hostname)
STRING_LENGTH_OUT_OF_RANGE	String violates minLength or maxLength
ARRAY_LENGTH_OUT_OF_RANGE	Array violates minItems or maxItems
UNKNOWN_FIELD	Field in input but not in schema — actor silently ignores it
USING_DEFAULT_VALUE	Field omitted; schema default will apply at runtime
SCHEMA_MISSING	Target actor has no declared input_schema.json
SCHEMA_DRIFT_DETECTED	Target actor's schema hash changed since the previous validated run

Input parameters

Parameter	Type	Required	Default	Description
targetActorId	string	Yes	—	Actor ID or username/actor-name to validate input against
testInput	object	No	{}	Single input JSON to validate. Use this OR testInputs.
testInputs	array	No	[]	Batch of labeled inputs (max 500). Each item: { "label": "name", "input": {...} }. Overrides testInput. Charged at $0.15 each.
strictness	enum	No	standard	lenient (warnings never downgrade a pass), standard (warnings → monitor), strict (unknown fields + 3-plus default-reliance warnings elevate to act_now)
includeSchemaQualityAudit	boolean	No	true	Audit the target actor's input schema for documentation / completeness / agent readiness. Does not perform additional API calls.
includeAutoFixSuggestions	boolean	No	true	Emit recommendedFixes[] with deterministic remediation suggestions.
includePatchedInputPreview	boolean	No	true	Attach patchedInputPreview with high-confidence autofixes pre-applied. Never mutates your stored input.
outputProfile	enum	No	standard	standard / full (complete record), minimal (decision essentials only), llm (plain-language subset for prompting). Trims the record without a JSONata expression.
policyPreset	enum	No	custom	Environment gate: dev / ci / production / agent / scheduled-task / custom. Bundles strictness + drift/warning gating. Explicit per-axis inputs override it.
maxWarningsBeforeBlock	integer	No	—	Elevate a valid input to act_now when it has more than this many warnings. Empty = never block on warning count.
failOnCompatibilityImpact	array	No	[]	Drift impacts that block a valid input: any of breaking, non-breaking. Empty = drift never blocks on its own.
compareAgainstSchemaHash	string	No	—	Pin a known-good schemaHash. A mismatch blocks (act_now) with a pinned-hash reason — a CI gate that fails when the target's input contract changes.
baselineScope	enum	No	targetActor	Drift baseline isolation: targetActor (shared), caller (per Apify account), custom (per baselineKey).
baselineKey	string	No	—	Custom isolation key for the drift baseline (used when baselineScope is custom).
includeDriftTimeline	boolean	No	false	Attach driftTimeline — the target schema's hash transitions across previous validated runs. Reads the shared history store, no extra API calls.
includeReliabilityProfile	boolean	No	false	Attach actorReliabilityProfile (validation runs, drift events, breaking drifts, schema stability, average score) + changeVelocity (observed change rates + trend). Null until a prior run exists.
includeReplayPackage	boolean	No	true	Attach replayPackage — a committable record of this exact validated invocation (schema hash + input + replay commands) to re-run later for debugging or audit.
proposedSchema	object	No	{}	A hypothetical input schema ({properties, required}). When provided, the actor re-validates your test inputs against it and returns impactSimulation — "would my inputs still validate if the target changed its schema to this?"
saveGoldenSuite	boolean	No	false	Store this run's test inputs + verdicts as the golden regression suite for this baseline.
runGoldenSuite	boolean	No	false	Re-validate the stored golden suite against the target's current schema → a golden-suite record with newFailures + consumerImpact.
inferSchemaWhenMissing	boolean	No	true	When the target has no schema, infer a starter input_schema.json from your test input and attach it as schemaBootstrap.
emitArtifacts	boolean	No	false	Write CI artifacts to the key-value store (REPORT.md, REPORT.json, JUNIT.xml, SARIF.json, GITHUB_STEP_SUMMARY.md).
artifactFormats	array	No	[]	Which artifact formats to write: any of markdown, json, junit, sarif, github. Empty = markdown, json, junit, github.
webhookUrl	string	No	—	URL to POST a compact run-level decision alert to (Slack / Make / Zapier / custom). Secret.
webhookMode	enum	No	none	When to fire the webhook: none / breaking-drift / act-now / non-ignore.

API usage

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/actor-input-tester").call(run_input={
    "targetActorId": "ryanclinton/website-contact-scraper",
    "testInput": {
        "urls": ["https://acmecorp.com"],
        "maxPagesPerDomain": 5,
        "extractEmails": True,
    },
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item["decision"] == "act_now":
        raise RuntimeError(f"Invalid input: {item['evidence']}")
    if item["decision"] == "monitor":
        print(f"REVIEW — {item['decisionReason']}")
    if item.get("compatibilityImpact") == "breaking":
        alert(f"Breaking drift: {item['previousSchemaHash']} → {item['schemaHash']}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/actor-input-tester").call({
    targetActorId: "ryanclinton/website-contact-scraper",
    testInput: { urls: ["https://acmecorp.com"], maxPagesPerDomain: 5, extractEmails: true },
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const [result] = items;

if (result.decision === "act_now") throw new Error(`Invalid: ${JSON.stringify(result.evidence)}`);
if (result.compatibilityImpact === "breaking") alert(`Breaking drift: ${result.previousSchemaHash} → ${result.schemaHash}`);

cURL

curl -X POST "https://api.apify.com/v2/acts/ryanclinton~actor-input-tester/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "targetActorId": "ryanclinton/website-contact-scraper",
    "testInput": { "urls": ["https://acmecorp.com"], "maxPagesPerDomain": 5 }
  }'

# Fetch results (replace DATASET_ID from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

Input examples

Single input:

{ "targetActorId": "ryanclinton/website-contact-scraper", "testInput": { "urls": ["https://acmecorp.com"], "maxPagesPerDomain": 5, "extractEmails": true } }

Batch regression suite with labels:

{
    "targetActorId": "ryanclinton/google-maps-email-extractor",
    "testInputs": [
        { "label": "minimal-valid", "input": { "searchQuery": "dentists in Austin TX", "maxResults": 10 } },
        { "label": "full-config", "input": { "searchQuery": "plumbers in Denver CO", "maxResults": 50, "extractEmails": true } },
        { "label": "edge-case-empty-query", "input": { "maxResults": 10 } }
    ]
}

Full output fields

For automation: use decision, verdictReasonCodes, compatibilityImpact, and decisionReadiness. All other fields are optional diagnostics for humans, dashboards, and post-mortems.

Field	Type	Description
decision	string	ignore / monitor / act_now — routable verdict
decisionReason	string	One-sentence plain-language explanation for the decision
confidenceLevel	string	high when schema declared; low otherwise
decisionReadiness	string	actionable / monitor / insufficient-data
verdictReasonCodes	array	Stable enum of reason tags
evidence	array	Field-level findings that support the verdict
counterEvidence	array	Fields that validated cleanly
driftDetected	boolean	true when the target's schema hash changed vs last run
previousSchemaHash	string/null	Hash from the last validated run (null on first run)
driftSummary	object/null	Structural diff: addedFields, removedFields, newRequiredFields, removedRequiredFields, typeChanges, enumChanges, defaultChanges, compatibilityImpact
compatibilityImpact	string	Top-level none / non-breaking / breaking — routing-safe
invocationPlan	object/null	{shouldRun, inputToUse, requiredHumanReview, safeToUseInAutomation, blockingReasons[], runCommand} — the run/block/review recommendation + exact payload to run
policy	string	Resolved policy preset: custom / dev / ci / production / agent / scheduled-task
blockingReasons	array	Why a valid input was elevated to act_now by the policy (drift / warning cap / pinned-hash mismatch)
productionRisks	array	Plain-English {risk, severity, field, impact} — what will silently go wrong in production
scorecard	object/null	{invocationSafety, schemaCompleteness, automationReadiness, driftStability, overall} — categorized readiness rollup
schemaCapabilities	object/null	Factual booleans for what the target schema declares (enums, nesting, patterns, formats, ranges, secret fields, fieldCount)
contractSnapshot	object/null	{requiredFields[], optionalFields[], defaults{}, enums{}, breakingChangeRules[]} — commit to git, diff later
driftSeverity	object/null	{score (0–100), level (critical/high/medium/low), factors[]} — finer than compatibilityImpact. Null when no drift
replayPackage	object/null	{actorId, schemaHash, input, validatedAt, replayCurl/Python/Javascript} — committable record of this exact invocation
actorReliabilityProfile	object/null	{validationRuns, driftEvents, breakingDrifts, schemaStability, averageScore, lastBreakingDrift, blockedInvocations, silentFailuresPrevented} — contract observability + bad runs caught, across runs
changeVelocity	object/null	{schemaChangesLast30Days, breakingChangesLast90Days, stabilityTrend} — observed historical rates (not a forecast)
impactSimulation	object/null	When proposedSchema is set: {wouldBreak, affectedInputs, brokenFields[], perInput[], confidence} — would your inputs survive a proposed schema change
coverage	object/null	{fieldsCovered, totalFields, coveragePercent, untestedFields[], untestedRequiredFields[]} — how much of the contract your inputs exercise
contractRisk	object/null	With reliability profile on: {score, level, reasons[]} — a cross-run "credit score" for the target's contract
upgradeGuide	object/null	When drift is detected: {requiredChanges[{field, action, detail}], changeCount} — what to change in your input because the contract drifted
goldenSuite	object/null	On a golden-suite record: {suiteSize, newFailures, fixedInputs, breakingInputs[]} — regression of stored payloads vs the current schema
consumerImpact	object/null	On a golden-suite record: {wouldBreakExistingPayloads, affectedStoredInputs, severity} — will this actor update break your payloads
schemaBootstrap	object/null	No-schema only: {inferredInputSchema, confidence, warnings[], actorJsonPatch} inferred from your sample
pinnedHashMatch	boolean/null	When compareAgainstSchemaHash is set: true if the schema matches the pin, false if it drifted
driftTimeline	array	When includeDriftTimeline is set: {validatedAt, schemaHash, impact}[] across previous runs
schemaQualityAudit	object/null	documentationScore, completenessScore, agentReadinessScore (all 0–100), findings[]
recommendedFixes	array	{priority, field, findingPath, reasonCode, changeType, currentValue, suggestedValue, mostLikelyIntent, confidence, explanation}
patchedInputPreview	object/null	Your input with high-confidence autofixes pre-applied
safeAutoFixCount	integer	Count of high-confidence fixes in patchedInputPreview
unsafeSuggestionCount	integer	Count of suggestions needing human review
intentMismatchRisk	object	{level, whyItMatters} — rollup for unknown-field / enum-mismatch signals
runtimeSurpriseRisk	object	{level, whyItMatters} — rollup for default-reliance warnings
strictness	string	Strictness mode in effect: lenient / standard / strict
recordType	string	validation (a verdict) or error (an actor-level failure) — the dataset-view discriminator
stage	string	Always input
status	string	pass or block
score	integer	0–100 from the published rubric
scoringTrace	array	Per-record rubric reproduction: {reasonCode, occurrences, penaltyPerOccurrence, totalDeduction} sorted largest-deduction-first. score = 100 − Σ(totalDeduction), clamped [0,100]
summary	string	One-line verdict for reports and Slack messages
recommendations	array	Ordered fix-first action list
signals	object	{errorCount, warningCount, criticalCount, driftDetected, metrics}
actorName	string	Resolved username/name
actorId	string	The targetActorId value you provided
testLabel	string	Label for this input in batch mode (absent in single mode)
inputValid	boolean	Legacy — true if zero errors
errors	array	Validation errors with field and error message
warnings	array	Informational warnings
schemaFound	boolean	true if an input schema was found in the latest build
schemaHash	string/null	Current schema hash; null when no schema
testedFields	integer	Fields present in your test input
schemaFields	integer	Fields declared in the actor's input schema
generatedCurl / generatedPython / generatedJavascript	string	Ready-to-paste client snippets
validatedAt	string	ISO 8601 timestamp
failureType	string	On actor-level errors: invalid-input / no-data / parse-error

Scoring rubric (published — deterministic)

Score starts at 100, subtracts per-code penalties, clamped to [0, 100]. Additive-only within a major version.

Code	Penalty
REQUIRED_FIELD_MISSING	−35 each
TYPE_MISMATCH	−25 each
ENUM_MISMATCH	−20 each
VALUE_OUT_OF_RANGE	−20 each
PATTERN_MISMATCH	−20 each
FORMAT_MISMATCH	−20 each
STRING_LENGTH_OUT_OF_RANGE	−15 each
ARRAY_LENGTH_OUT_OF_RANGE	−15 each
FLOAT_FOR_INTEGER	−10 each
UNKNOWN_FIELD	−8 each
USING_DEFAULT_VALUE	−3 each
SCHEMA_DRIFT_DETECTED	−12 (baseline, applied once per run)
OTHER	−5 each

Threshold on score for CI gates (e.g., >= 80 to merge) without reverse-engineering the logic.

Strictness modes

Errors always block (act_now) — strictness only affects the pass path.

Mode	Unknown fields	Default reliance (3+)	Other warnings	Use when
lenient	ignore	ignore	ignore	Exploratory / dev
standard (default)	monitor	monitor	monitor	Normal use
strict	act_now	act_now	monitor	CI / production

In strict mode, decisionReason explains the elevation (e.g., "Strict mode: 2 unknown field(s) and 1 default-reliance warning(s) elevated to blocking.").

Schema drift diff

When driftDetected: true, driftSummary lists exactly what changed since the last validated run.

{
    "driftDetected": true,
    "previousSchemaHash": "abc123",
    "schemaHash": "def456",
    "compatibilityImpact": "breaking",
    "driftSummary": {
        "addedFields": ["country"],
        "removedFields": [],
        "newRequiredFields": ["country"],
        "removedRequiredFields": [],
        "typeChanges": [{ "field": "maxPages", "from": "integer", "to": "string" }],
        "enumChanges": [{ "field": "sort", "added": ["relevance"], "removed": ["popular"] }],
        "defaultChanges": [{ "field": "useApifyProxy", "from": true, "to": false }],
        "compatibilityImpact": "breaking"
    }
}

compatibilityImpact is exposed both top-level and inside driftSummary so consumers don't have to walk the nested object.

• none — no structural change
• non-breaking — added optional fields, new default values on omitted fields, expanded enums
• breaking — new required fields, removed fields, type changes, or removed enum values

Baseline scope

Drift is compared against the most recent baseline Input Guard stored for the target actor in its shared AQP key-value store (aqp-{sanitized-target-actor-id}). The key is scoped per target actor, not per caller — callers sharing a target share the baseline. Use a dedicated Apify account or isolated storage for per-caller baselines. Restricted-permission tokens cannot write baselines; they still read for drift detection when a baseline exists.

Remediation & autofix

Autofix contract

• Only auto-apply fixes where confidence === "high". Those are already applied in patchedInputPreview.
• Do not auto-apply medium or low fixes. They are suggestions, not decisions.
• Use patchedInputPreview directly instead of re-applying recommendedFixes[] yourself — the confidence gate has already been enforced.

High-confidence fixes are pre-applied in patchedInputPreview; medium- and low-confidence fixes are surfaced but require human review.

Confidence ladder

Category	Confidence	Auto-applied?
5.0 → 5 (integer coercion, no fractional part)	high	Yes
"true" / "false" / "1" / "0" → boolean	high	Yes
Exact case-insensitive enum match (e.g. "DESC" vs "desc")	high	Yes
"yes" / "no" / "on" / "off" → boolean	medium	No — needs review
Enum near-match via Levenshtein distance ≤ 2	medium	No — could be a different valid option
Unknown field typo-rename via Levenshtein distance ≤ 2	medium	No — two schemas can have two real nearby field names
Truncation of 5.5 → 5 (precision loss)	medium	No — review before accepting
Required field missing, schema has default	medium	No — schema default applies at runtime anyway
Required field missing, no default	low	No — you must supply a value

Example

{
    "recommendedFixes": [
        { "priority": "high", "field": "maxPagesPerDomain", "findingPath": "maxPagesPerDomain", "reasonCode": "FLOAT_FOR_INTEGER", "changeType": "coerce", "currentValue": 5, "suggestedValue": 5, "confidence": "high", "explanation": "Safe to coerce to integer 5." },
        { "priority": "medium", "field": "extrctEmails", "findingPath": "extrctEmails", "reasonCode": "UNKNOWN_FIELD", "changeType": "rename", "currentValue": true, "suggestedValue": "extractEmails", "mostLikelyIntent": "extractEmails", "confidence": "medium", "explanation": "Likely a typo; both names may be real." }
    ],
    "patchedInputPreview": { "maxPagesPerDomain": 5, "extrctEmails": true },
    "safeAutoFixCount": 1,
    "unsafeSuggestionCount": 1
}

Every fix carries findingPath (exact nested path), mostLikelyIntent (best-guess for typo-rename / enum near-match), confidence (gatekeeper for auto-application), and explanation. patchedInputPreview never mutates your stored input.

Schema quality audit

Input Guard audits the target actor's input schema itself — documentation, completeness, and agent readiness. Flags target schema traits that reduce invocation reliability for callers and agents.

Sub-rubrics (published)

Three weighted hit ratios. Each per-field slot contributes only when relevant to that field.

documentationScore — weighted per field:

Slot	Weight	Condition
Title present	1	title non-empty
Description present	2	description non-empty
Description ≥ 40 chars	2	Substantive, not a one-liner
Enum options documented	2	Enum fields only — substantive description
Nested object descriptions	2	Object fields with nested properties — each nested property has a description

completenessScore — weighted per field:

Slot	Weight	Condition
Type declared	3	type non-empty
Array items typed	2	Array fields only — items.type declared
Object properties declared	2	Object fields only — nested properties non-empty
Editor declared	1	editor non-empty
Required OR default	2	Optional fields should carry a default

agentReadinessScore — weighted per field:

Slot	Weight	Condition
Strongly typed	3	Primitive type, or array with typed items
Enum fully documented	2	Enum fields only — description ≥ 40 chars
Predictable default or required	3	Agents must be able to omit or required
Required object fully specified	2	Required object fields — nested properties declared

Example

{
    "schemaQualityAudit": {
        "documentationScore": 74,
        "completenessScore": 81,
        "agentReadinessScore": 69,
        "findings": [
            { "field": "searchQuery", "type": "MISSING_DESCRIPTION", "severity": "high", "message": "Field \"searchQuery\" lacks a description." },
            { "field": "advancedOptions", "type": "LOW_AGENTIC_TYPING", "severity": "medium", "message": "Loose or untyped shape — agents have to guess how to populate it." }
        ]
    }
}

Set includeSchemaQualityAudit: false to skip (no cost difference — structural only).

Batch hotspot analytics

In batch mode (testInputs[]), the KV SUMMARY record aggregates patterns across all inputs.

{
    "inputsTested": 50,
    "totalErrors": 18,
    "totalWarnings": 124,
    "decisionBreakdown": { "ignore": 7, "monitor": 31, "act_now": 12 },
    "topFailureReasons": [
        { "reasonCode": "USING_DEFAULT_VALUE", "count": 97 },
        { "reasonCode": "FLOAT_FOR_INTEGER", "count": 14 },
        { "reasonCode": "UNKNOWN_FIELD", "count": 11 }
    ],
    "fieldHotspots": [
        { "field": "proxyConfig", "issueCount": 31 },
        { "field": "maxResults", "issueCount": 22 }
    ],
    "driftDetected": false,
    "schemaQualityAudit": { "documentationScore": 74, "completenessScore": 81, "agentReadinessScore": 69 },
    "strictness": "standard"
}

Risk scalars (silent-failure prevention)

Two rollups with {level, whyItMatters} — route on level, surface whyItMatters to humans.

• intentMismatchRisk — did you type a field the actor will silently drop?
  • high when any UNKNOWN_FIELD is present
  • medium when an ENUM_MISMATCH is present
  • low otherwise
• runtimeSurpriseRisk — will omitted fields change behavior via defaults?
  • high when 4+ fields rely on schema defaults
  • medium when 1–3 fields do
  • low when your input covers every optional field

{
    "intentMismatchRisk": { "level": "high", "whyItMatters": "2 provided field(s) are not in the target schema and will be silently ignored at runtime." },
    "runtimeSurpriseRisk": { "level": "medium", "whyItMatters": "2 omitted field(s) rely on schema defaults — behavior may differ from what you expect if the defaults change." }
}

How it works

• Fetches the target actor's latest tagged schema via the Apify API.
• Validates the input against the schema (required / type / range / enum / unknown / defaults).
• Compares the schema to a stored baseline → drift diff with compatibilityImpact.
• Generates decision, verdictReasonCodes, recommendedFixes, and patchedInputPreview.

Batch runs: each input validated independently, one PPE event per input, hotspot summary written to KV after the loop.

Charging conditions

$0.15 per validation (pay-per-event). No PPE event is charged when Input Guard cannot retrieve a declared input schema from the target actor's latest tagged build.

No charge:

• Target actor has no input_schema.json declared in its latest tagged build.
• Latest build exists but contains no parseable schema (buildData.inputSchema missing or invalid JSON).
• No tagged build exists for the target actor.
• Apify API failure (404 on target, 5xx after retries) — returns failureType: no-data.

In no-schema cases, validation returns decision: monitor + decisionReadiness: insufficient-data + SCHEMA_MISSING code.

Charged:

• Schema retrieved and validation completes — regardless of ignore / monitor / act_now verdict.
• Each input in a batch — one PPE event per input, independently.

Restricted-permission tokens

Scoped tokens (e.g., Apify's "Restricted access" on Running Actors) can't write to the AQP baseline store. Input Guard detects this at startup with a 5-second probe, logs a warning, and skips drift-history writes for that run. Validation runs normally — you lose cross-run drift tracking and batch hotspot persistence until the token is upgraded.

The Guard Pipeline

Input Guard is part of a three-stage quality pipeline:

Stage	Guard	What it prevents
Before run	Input Guard	Bad input wasting runs and credits
Before deploy	Deploy Guard	Broken builds reaching production
After deploy	Output Guard	Silent data failures in production

Which Guard do I need?

• "My actor won't start or crashes immediately" → Input Guard
• "I changed code — is it safe to deploy?" → Deploy Guard
• "It runs fine but the data looks wrong" → Output Guard

Input Guard $0.15/test, Deploy Guard $2.50/suite, Output Guard $4.00/check.

Shared state across Guards

All three share a per-actor quality profile in a named KV store (aqp-{sanitized-actor-id}):

• Cross-stage history — each Guard appends to a shared timeline.
• Baselines — each stage stores its own baseline. Input Guard's drift detection reads input.schemaHash + structural snapshot.
• Feedback loops — Output Guard suggests Deploy Guard assertions when critical fields degrade. Deploy Guard suggests Output Guard field rules when tests find flaky fields.

Common output interface

All three Guards return the same top-level fields: stage, status, score, summary, recommendations, signals, signals.metrics.

Status semantics

Status	Meaning	Input Guard	Deploy Guard	Output Guard
pass	No action required	Input valid	Safe to deploy	Data healthy
warn	Review	—	Soft regressions	Data declining
block	Do not proceed	Don't run	Don't ship	—
fail	Live failure	—	—	Data is bad

Portfolio wrapper: any block → block, any fail → fail, any warn → warn.

Use in Dify

Drop Input Guard into Dify workflows via the Apify plugin's Run Actor node. Each validation returns one canonical decision as structured JSON — decision enum (ignore / monitor / act_now), decisionReadiness (actionable / monitor / insufficient-data), status (pass / block), verdictReasonCodes[] (stable enum: REQUIRED_FIELD_MISSING / TYPE_MISMATCH / ENUM_MISMATCH / VALUE_OUT_OF_RANGE / FLOAT_FOR_INTEGER / UNKNOWN_FIELD / USING_DEFAULT_VALUE / SCHEMA_MISSING / SCHEMA_DRIFT_DETECTED), compatibilityImpact (none / non-breaking / breaking), intentMismatchRisk.level, and recommendedFixes[] (with findingPath + mostLikelyIntent + confidence) your downstream node branches on. Generic JSON-schema validators return raw failures; this returns one routable preflight verdict.

• Actor ID: ryanclinton/actor-input-tester
• Sample input (preflight gate before invoking a target Apify actor with user-supplied JSON):

{
    "targetActorId": "user/your-actor-name",
    "testInput": { "query": "test", "maxResults": 10 },
    "strictness": "standard"
}

• Branching example — a Dify if/else node reads decision and routes:
  • act_now → block the actor invocation + surface evidence[] to the user + show recommendedFixes[] (with patchedInputPreview for high-confidence autofixes)
  • monitor → require human approval before invoking — verdictReasonCodes[] shows what needs review
  • ignore → safe to invoke the target actor with this input
• Strictness gating: strictness enum (lenient / standard / strict) lets Dify workflows tighten the gate per environment — strict elevates UNKNOWN_FIELD + 3-plus default-reliance warnings to act_now (do-not-run)
• Lean branching payload: set outputProfile: "minimal" to return only the decision essentials (decision, decisionReadiness, verdictReasonCodes, compatibilityImpact, score) so the if/else node routes on a small object; recommendedFixes[] and the patched preview ship verbatim under standard for nodes that apply autofixes (no LLM rewriting)
• Schema drift alerts: when scheduled, the actor diffs the target's current schema against the prior baseline — Dify branches on compatibilityImpact = "breaking" to alert the actor owner before user-facing breakage
• For agent safety: an LLM agent generating Apify actor inputs should call Input Guard FIRST, then only invoke if decision = "ignore" — prevents the agent from burning credits on invalid payloads. The patchedInputPreview field provides high-confidence autofixes the agent can apply without LLM rewriting (5.0→5 integer, "true"/"false"→bool, exact case-insensitive enum normalization).

The single canonical decision + stable verdictReasonCodes[] + autofix-ready patchedInputPreview make this the ideal "is this Apify actor invocation safe to make?" gate inside any Dify automation that programmatically calls Apify actors.

Limitations

• Validates declared schema constraints only — no API key / URL / code-logic validation.
• Validates against the latest tagged build only — unbuilt changes not reflected.
• Validates property-level constraints (required, type, enum, minimum/maximum, pattern, format, minLength/maxLength, minItems/maxItems, nested objects, array items). No allOf / oneOf / anyOf combinator support.
• format covers email / uri / url / hostname / date / date-time / uuid / ipv4.
• Batch limit of 500 inputs per run.
• Does not validate actor output — use Output Guard.
• Does not test actor runtime behavior — use Deploy Guard.

Pricing

$0.15 per validation (pay-per-event). Platform compute included. See Charging conditions.

One failed production run — wasted compute, a broken scheduled job, an agent that burned credits on a bad payload — usually costs more than validating dozens of payloads. A validation is cheaper than the run it prevents, and it is not charged at all when the target actor has no declared schema.

Scenario	Validations	Total cost
Quick test	1	$0.15
Feature branch	5	$0.75
Full input suite	20	$3.00
Portfolio regression (50 actors)	50	$7.50
CI pipeline (monthly, 200)	200	$30.00

Per-run spending limit caps cost. Input Guard stops when limit hit and reports how many inputs were processed.

Troubleshooting

• "Actor not found (404)" — targetActorId format wrong or actor private + token lacks access. Use username/actor-name.
• schemaFound: false with a known schema — actor not built or latest build failed. Trigger a rebuild. Returns decision: monitor with SCHEMA_MISSING, no PPE event.
• decision: ignore but run fails — Input Guard checks schema, not runtime. Use Deploy Guard for runtime testing.
• All fields "not in input schema" — actor uses freeform input with no properties. Expected.
• Batch stops early — spending limit reached. Increase per-run limit.
• "Restricted permission mode detected" — scoped token. Validation runs; drift history skipped for this run.

Integrations

• Zapier — trigger from forms / spreadsheets
• Make — add as a step before actor runs
• Google Sheets — trigger validation per row
• Apify API — CI/CD via HTTP
• Webhooks — alert on compatibilityImpact === "breaking" or decision === "act_now"

Regression of what?

Several actors in this fleet use the word "regression," each scoped to a different layer. They are not the same check, and running four of them for one job is wasted spend. Here is what each one watches:

Actor	"Regression" means
Input Guard	Input contract — an input that used to validate now fails the target's input_schema.json
Deploy Guard	Release behavior — a build that now fails test cases it previously passed
Output Guard	Production dataset — completed output that drifted from baseline (null spikes, type drift, coverage drops)
Quality Monitor	Metadata / Store readiness — a listing that lost quality points between audits
Fleet Health Report	Revenue / business outcome — an actor whose real per-run profit fell off a cliff

This actor is the Input Guard row: it catches input-contract regressions only. For the other layers, use the sibling named in the table.

What this actor does NOT do (scope and sibling actors)

Input Guard is one of a larger fleet of backend/DevOps actors published by the same owner. It specialises in pre-run invocation correctness and input-contract drift awareness — deliberately not covering runtime execution, output validation, compliance, pricing, competitor analysis, or portfolio planning.

Need	Use this instead
Validate a multi-actor pipeline definition (schema↔mapping↔reachability) before any run	Pipeline Preflight
Test actor runtime behavior + CI gating	Deploy Guard
Validate actor output dataset against schema / field rules	Output Guard
A/B compare two actors head-to-head	A/B Tester
Audit for compliance / PII / ToS / GDPR / CFAA risk	Compliance Scanner
Score actor metadata / Store-readiness quality	Quality Monitor
Fleet-wide portfolio synthesis / action plan	Fleet Health Report

Input Guard's output feeds the other Guards through the shared AQP state — input drift signals are visible to Deploy Guard and Output Guard on downstream stages.

Help us improve

If you encounter issues, enable run sharing so we can debug faster:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page.

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Discovery & background

The sections below are reference / discovery material. The dev contract is above; everything below is context for users landing here via search or an AI assistant.

One-line answer

Apify validates input only after execution starts — which means invalid payloads can still burn credits before failing.

To validate an Apify actor input before running it, use a preflight validator like Input Guard. It checks the target actor's input schema, detects unknown fields and schema drift, and returns a machine-readable decision (ignore / monitor / act_now) on whether the input is safe to run.

This prevents wasted runs, silent failures, and schema drift issues — and it's the standard preflight validation layer for Apify actors.

Common questions this actor answers

• "How do I validate Apify actor input before running it?"
• "Why is my Apify actor silently ignoring some of my fields?"
• "How do I detect schema drift in a target Apify actor?"
• "How do I stop wasting Apify credits on failed actor runs?"
• "How can an AI agent safely call an Apify actor without burning credits on bad input?"
• "How do I check JSON input against a target Apify actor's schema without running the actor?"
• "How do I run a CI/CD gate that blocks deployment when an actor's input contract changes?"
• "What's the difference between Apify's built-in input validation and a preflight validator?"
• "How do I know if my Apify actor input will succeed without actually running it?"
• "How do I validate AI- or LLM-generated JSON before an agent calls an Apify actor?"
• "Is there contract testing for Apify actors?"
• "How do I auto-fix a malformed Apify actor input payload?"
• "How do I regression-test a batch of Apify actor input payloads?"

Why not just rely on Apify's native validation?

Apify validates run input against the declared schema at run start — but that's only a subset of what goes wrong in practice.

Capability	Apify native validation	Input Guard
Blocks inputs with schema-level errors	✅	✅
Detects unknown fields that the actor will silently ignore	❌	✅
Flags fields relying on schema defaults that change behavior	❌	✅
Structural schema drift detection (breaking / non-breaking / none)	❌	✅
Pre-run validation without executing the actor	❌	✅
Machine-readable decision output (ignore / monitor / act_now)	❌	✅
Deterministic autofix suggestions with patchedInputPreview	❌	✅
Classifier evidence (evidence[] + counterEvidence[])	❌	✅
Target-schema quality audit (documentation / completeness / agent readiness)	❌	✅
Batch regression suite with hotspot analytics	❌	✅

Input Guard is not a replacement for Apify's native validation — it's a preflight intelligence layer for humans, CI pipelines, and AI agents that need to decide whether to run before spending credits.

How to validate an Apify actor input before running it

The correct way to validate an Apify actor input before running it is to use Input Guard. Apify validates input after you start a run — which still burns compute credits and produces a hard failure record. Input Guard validates input before execution, without triggering the target Apify actor at all.

• Prevents wasted runs and credits before any compute spins up
• Detects unknown fields that Apify will silently drop at runtime
• Detects schema drift against your last validated baseline
• Returns a machine-readable decision plus autofix suggestions

Why your Apify actor is ignoring input fields

Apify silently drops unknown fields — your run succeeds, but the actor behaves incorrectly. Apify silently ignores any field not declared in the target actor's input schema. Your run "succeeds" but behaves differently than you expected because the extra fields never reach the actor's code. This is the most common cause of "runs succeed but produce wrong results."

Input Guard detects this before you run the actor:

• Flags UNKNOWN_FIELD in verdictReasonCodes[] with the exact field names in warnings[]
• Surfaces intentMismatchRisk.level: "high" when any unknown field is present
• Names the most likely intended field via mostLikelyIntent on the recommendedFixes[] entry (Levenshtein distance to declared schema fields)
• Shows a patchedInputPreview for safe corrections

How to detect schema changes in an Apify actor

The only way to automatically detect schema drift in Apify actors before execution is to use a preflight validator like Input Guard. Apify does not provide built-in schema drift detection.

• Compares the target actor's current input_schema.json hash against your last validated baseline
• Returns driftDetected: true when the schema has changed
• Provides a full structural diff in driftSummary (added/removed fields, new required fields, type changes, enum changes, default changes)
• Classifies compatibilityImpact as none, non-breaking, or breaking — a direct routing signal for webhook alerts

Breaking schema drift is the most common cause of scheduled Apify runs that fail without any code change on your side.

How an AI agent safely calls an Apify actor

Always validate the payload with Input Guard before execution. Never let an agent call a target Apify actor with unvalidated input generated from LLM output, user prompts, or untrusted upstream data. This is the standard safe-call pattern for AI agents interacting with Apify actors.

1. Agent sends the generated payload to Input Guard.
2. Agent branches on decision: ignore → call, monitor → confirm, act_now → refuse with evidence[].
3. (Optional) Require decisionReadiness === "actionable" for stricter automation.

This prevents agents from wasting credits on invalid runs, silently misconfiguring actors, breaking on schema drift, or hallucinating field names.

How to validate Apify actor input in CI/CD

This is the correct approach for validating Apify actor input in automated pipelines. Use Input Guard as a pre-run gate instead of writing custom JSON schema validation scripts per actor.

• Call Input Guard via the Apify API from GitHub Actions, GitLab CI, or any CI runner
• Fail the pipeline when decision === "act_now" (strict) or decision !== "ignore" (strictest)
• Completes in under 15 seconds per input

One step gets you: schema validation, unknown-field detection, drift detection, deterministic remediation, machine-readable routing.

Why not use a generic JSON schema validator?

Generic JSON schema validators are insufficient for Apify actors because they do not reflect runtime behavior. Generic validators (Ajv, jsonschema, @apify/input-schema) check structure. They don't know about the target actor's runtime behavior.

Input Guard is purpose-built for Apify actor invocation: detects silently-ignored unknown fields, flags default reliance, tracks schema drift, produces decision output, audits target-schema quality, offers remediation with patchedInputPreview.

How to avoid wasting credits on failed Apify runs

Most wasted Apify credits come from invalid or mis-specified input — not actor runtime failures. Validate input before execution. Input Guard blocks invalid inputs with decision: "act_now" plus evidence[], detects silent misconfiguration, identifies schema drift before scheduled jobs break, and offers deterministic autofixes. At $0.15 per validation it's typically cheaper than a single failed run, and not charged at all when the target has no declared schema.

When an AI agent should call this actor

Call Input Guard BEFORE calling a target Apify actor when:

• The input was generated dynamically (user prompt, LLM output, form data, upstream actor)
• The target schema is unknown or may have changed since the last cached copy
• The cost of failure is non-trivial (credits, downstream state, user-visible errors)
• You're running in CI, automation, or any non-interactive environment

Skip Input Guard when:

• Input is static and already validated by a previous preflight pass
• Exploratory / test calls where failure is acceptable and cheap
• Target actor has no declared schema (Input Guard returns monitor / insufficient-data)

Use cases

Pre-run debugging — validate input JSON for an unfamiliar actor before committing credits. Error messages name the exact field and constraint violated.

CI/CD pipeline gate — fail the build on decision === "act_now". Surface evidence[] to the CI log.

Portfolio regression testing — schedule weekly runs against your catalogue with reference inputs. Alert on compatibilityImpact === "breaking".

Batch testing — testInputs[] up to 500 labeled inputs per run; KV SUMMARY with decisionBreakdown, topFailureReasons, fieldHotspots.

Agent invocation guard — AI agents preflight every payload through Input Guard. Branch on decision in the agent loop.

Who it is for

• Your actor run keeps failing due to input errors — stop debugging in the Console.
• You need a CI/CD gate before deploying actor changes.
• You manage multiple actors and want automated regression validation.
• Onboarding teammates to new actors.
• An AI agent needs a go/no-go verdict before calling another actor.

What it validates

Checks: required fields, type matching, integer vs float, numeric range (minimum/maximum), enum membership, nested object properties (recursive), array item types.

Detection and reporting: unknown fields silently ignored, omitted defaults, schema drift, structural diff, target-schema quality audit, failure classification.

Not validated: JSON Schema allOf/oneOf/anyOf, pattern/format, runtime logic, API key validity, URL reachability, output quality.

FAQ

How do I validate actor input JSON without running the actor? Provide the target actor's slug and your input JSON. Input Guard fetches the schema, validates field-by-field, returns a decision record. The target actor is never triggered.

Does it validate nested objects and arrays? Yes — recursive nested property validation and items-schema array element validation.

Can it validate private actors? Yes, if your API token has access.

How accurate is the validation? Deterministic against declared schema constraints. Code-enforced constraints not detected.

What happens if the target actor has no schema? Returns schemaFound: false, decision: monitor, decisionReadiness: insufficient-data. Code snippets still generated. No PPE charge.

How do I detect schema drift? Schedule runs; watch driftDetected + compatibilityImpact. Alert on breaking.

How is the score computed? Published rubric. Start at 100, subtract per-code penalties, clamp to [0, 100].

How much does batch testing cost? $0.15 per input. 50 inputs = $7.50. Use a spending limit.

Can I use it in CI/CD? Yes. Check decision === "ignore" for strict CI or decision !== "act_now" for permissive.

Is it safe to call from an AI agent? Yes — no execution side effects. Branch on decision.

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Next stage

Input Guard is the invocation stage of one developer lifecycle: publish, quality, release, invocation, orchestration, runtime, migration, portfolio. Input Guard validates one actor and one payload; the next stage validates many actors composed into one workflow.

Next stage: Pipeline Preflight. Single-actor input safe? Validate that multiple actors compose correctly before you run them.