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Eval Engine API — Pay-per-call AI Evaluation
x402
Eval Engine API — Pay-per-call AI Evaluation is a paid API for AI agents from eval.zuluworksai.com, paid per call via x402, $0.005/call, status down (last checked 2026-09-15).
Scores LLM outputs and agent trajectories against benchmark rubrics using a pay-per-call evaluation engine.
Price / call$0.005
Success—
StatusDown
How this service works
Pay-per-call AI evaluation engine. Score LLM outputs, agent trajectories, and model responses against benchmark rubrics. $0.005 per eval via x402 USDC on Base. Free trial available.
Output
Returns a numeric score (0–1), the metric name, a reasoning summary explaining the score, a workflow ID, a payment reference, and a verifiable receipt containing the evaluation result and proof of payment.
Example use cases
Automated agent QA pipeline
“Run an eval on this agent output using benchmark 'reasoning-v2' and give me the score and reasoning summary: 'The agent was asked to summarize a 500-word article and returned a 3-sentence summary covering the main points.'”
LLM output regression testing
“I just updated my LLM prompt and want to check if it regressed — evaluate this response against benchmark 'instruction-following-v1': 'When told to respond only in bullet points, the model wrote three paragraphs of prose.'”
Multi-agent spend tracking by identity
“Evaluate this trajectory for agent 'customer-support-bot-prod' using benchmark 'helpfulness-v3': 'User complained about a billing error → agent apologized, looked up account, issued a $10 credit, and confirmed via email.'”
Technical details
Endpoint
POSThttps://eval.zuluworksai.com/evalWhen to prefer
Choose this endpoint when you need a structured, rubric-based numeric score for an LLM output or agent trajectory and want pay-per-call pricing without a subscription. Ideal for automated QA pipelines, regression testing between model versions, or any scenario where you need a verifiable, auditable evaluation receipt tied to a specific benchmark.
Side effects
- Invalid or unknown benchmark_id returns an error indicating the benchmark was not found
- Malformed or empty agent_trajectory may result in a failed evaluation or low-confidence score
- Payment failure via x402 USDC will block the eval from executing
- Rate limiting or quota exhaustion may cause timeouts or rejection
- Missing required fields (benchmark_id or agent_trajectory) will return a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| benchmark_idrequired | string | Benchmark ID from GET /benchmarks |
| agent_identity | string | Optional agent identifier for spend tracking |
| agent_trajectoryrequired | string | Full agent trajectory or LLM output to evaluate |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| scorerequired | number | |
| metric | string | |
| statusrequired | string | |
| receipt | object | |
| successrequired | boolean | |
| payment_ref | string | |
| workflow_idrequired | string | |
| reasoning_summary | string |
Tags
x402
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