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Challenge

Build a Variant Functional Effect Predictor with OpenAI Agents SDK

Missense mutation interpretation requires fast, accurate functional predictions combined with automated triage. Developers will build an OpenAI Agents SDK pipeline integrated with Bolt.new web workflows to ingest genomic VCF files, query missense prediction endpoints, and output structured ACMG classification reports with 95% evaluation accuracy.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Implement an OpenAI Agents SDK workflow that orchestrates genomic variant annotations and evaluates missense mutations.

Delivery guide

How work is evaluated

Evaluation

Evaluates variant triage accuracy, ACMG code assignment precision, and JSON output formatting.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

json_schema_validation

Validates that the output matches the required ACMG report structure.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

classification_accuracy

Accuracy of pathogenic vs benign classification compared to ground truth. • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Ingest VCF variant files and extract missense mutation annotations using Biopython.

  • Build tool-calling agents with OpenAI Agents SDK to query functional prediction tools.

  • Synthesize genomic evidence into ACMG-compliant structured JSON reports.

  • Integrate agent runtime endpoints with Bolt.new frontends for interactive variant triage.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic and open-source genomic variant datasets containing ClinVar annotated missense mutations.

How this agent runs

Evaluates variant triage accuracy, ACMG code assignment precision, and JSON output formatting.

Challenge input

JSON containing genomic variant coordinates and reference/alternate alleles.

OpenAI Agents SDK

Official framework for multi-turn tool-calling agents.

Bolt.new

AI engineering tool for rapidly deploying reactive web interfaces.

Evaluated output

JSON containing ACMG classification, pathogenicity score, and evidence codes.

Checks for
  • Validates that the output matches the required ACMG report structure.
  • Accuracy of pathogenic vs benign classification compared to ground truth. • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: ClinVar Variant Benchmarks
  • Classification Accuracy target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Environment
  • Bolt.new · Optional
Action Space
  • OpenAI · Required
  • Bolt.new · Optional
  • Edge Impulse · Optional

Evaluation contract

  • json_schema_validation · Weight 1
  • classification_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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