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These cookbooks show how to use Graph Evolve on real tasks. Each example demonstrates a different graph type and use case.
  • Banking77 Classification: Customer messages in, intent labels out. Train a policy graph for 77-class classification without hand-authored rules. See the Banking77 cookbook.
  • Crafter Verifier: Agent traces in, calibrated scores out. Train a verifier graph to evaluate agent performance using V3 traces. See the Crafter Verifier cookbook.

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All Included Examples

CookbookGraph TypeTask
Banking77 ClassificationPolicyIntent classification (77 classes)
Crafter VerifierVerifierCustom verifier for agent traces

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Policy vs Verifier

Policy graphs solve tasks - they take inputs and produce outputs:
  • Banking77: {customer_message} → {intent}
Verifier graphs evaluate results - they take traces and produce scores:
  • Crafter: {trace} → {score, reasoning}

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Each Cookbook Includes

  • A GraphEvolveTaskSet JSON dataset
  • Runnable training code
  • Inference examples
  • Notes on verifier configuration

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Getting Started

  1. Pick the cookbook matching your use case
  2. Copy the dataset format
  3. Adapt to your domain
  4. Train with Graph Evolve
Need help? Send us your dataset and we’ll walk you through it.
Learn more about the Graphs API in our introductory blog post.

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