n8n/packages/@n8n/ai-workflow-builder.ee/evaluations

AI Workflow Builder Evaluations

This module provides a evaluation framework for testing the AI Workflow Builder's ability to generate correct n8n workflows from natural language prompts.

Architecture Overview

The evaluation system is split into two distinct modes with a parallel evaluation architecture for optimal performance:

1. CLI Evaluation - Runs predefined test cases locally with progress tracking and parallel metric evaluation
2. Langsmith Evaluation - Integrates with Langsmith for dataset-based evaluation and experiment tracking

Directory Structure

evaluations/
├── cli/                 # CLI evaluation implementation
│   ├── runner.ts       # Main CLI evaluation orchestrator
│   └── display.ts      # Console output and progress tracking
├── langsmith/          # Langsmith integration
│   ├── evaluator.ts    # Langsmith-compatible evaluator function
│   └── runner.ts       # Langsmith evaluation orchestrator
├── core/               # Shared evaluation logic
│   ├── environment.ts  # Test environment setup and configuration
│   └── test-runner.ts  # Core test execution logic
├── types/              # Type definitions
│   ├── evaluation.ts   # Evaluation result schemas
│   ├── test-result.ts  # Test result interfaces
│   └── langsmith.ts    # Langsmith-specific types and guards
├── chains/             # LLM evaluation chains
│   ├── test-case-generator.ts    # Dynamic test case generation
│   ├── workflow-evaluator.ts     # Main orchestrator for parallel evaluation
│   └── evaluators/               # Individual metric evaluators
│       ├── index.ts              # Evaluator exports
│       ├── functionality-evaluator.ts      # Functional correctness evaluation
│       ├── connections-evaluator.ts        # Node connection evaluation
│       ├── expressions-evaluator.ts        # n8n expression syntax evaluation
│       ├── node-configuration-evaluator.ts # Node parameter evaluation
│       ├── efficiency-evaluator.ts         # Workflow efficiency evaluation
│       ├── data-flow-evaluator.ts          # Data flow logic evaluation
│       └── maintainability-evaluator.ts    # Code maintainability evaluation
├── utils/              # Utility functions
│   ├── evaluation-calculator.ts  # Metrics calculation
│   ├── evaluation-helpers.ts     # Common helper functions
│   ├── evaluation-reporter.ts    # Report generation
└── index.ts            # Main entry point

Implementation Details

Core Components

1. Test Runner (core/test-runner.ts)

The core test runner handles individual test execution:

• Generates workflows using the WorkflowBuilderAgent
• Validates generated workflows using type guards
• Evaluates workflows against test criteria
• Returns structured test results with error handling

2. Environment Setup (core/environment.ts)

Centralizes environment configuration:

• LLM initialization with API key validation
• Langsmith client setup
• Node types loading
• Concurrency and test generation settings

3. Workflow Evaluator (chains/workflow-evaluator.ts)

The main orchestrator that coordinates parallel evaluation across all metric categories:

• Parallel Execution: Runs all 7 evaluators concurrently using Promise.all() for optimal performance
• Score Calculation: Computes weighted overall score using the weight distribution
• Summary Generation: Creates evaluation summaries based on all metric results
• Critical Issues Identification: Aggregates critical violations from all evaluator categories

4. Individual Evaluators (chains/evaluators/)

Each metric category has its own specialized evaluator chain with tailored prompts and scoring logic:

Functionality Evaluator: Focuses on whether the workflow achieves explicitly requested goals Connections Evaluator: Analyzes node connections and data flow paths Expressions Evaluator: Validates n8n expression syntax and data references Node Configuration Evaluator: Checks parameter configuration and required fields Efficiency Evaluator: Evaluates redundancy, path optimization, and node count efficiency Data Flow Evaluator: Analyzes data transformations and validation logic Maintainability Evaluator: Assesses naming, organization, and structural quality

5. Langsmith Integration

The Langsmith integration provides two key components:

Evaluator (langsmith/evaluator.ts):

• Converts Langsmith Run objects to evaluation inputs
• Validates all data using type guards before processing
• Safely extracts usage metadata without type coercion
• Returns structured evaluation results from the parallel evaluation system

Runner (langsmith/runner.ts):

• Creates workflow generation functions compatible with Langsmith
• Validates message content before processing
• Extracts usage metrics safely from message metadata
• Handles dataset verification and error reporting

6. CLI Evaluation

The CLI evaluation provides local testing capabilities:

Runner (cli/runner.ts):

• Orchestrates parallel test execution with concurrency control
• Manages test case generation when enabled
• Generates detailed reports and saves results

Display (cli/display.ts):

• Progress bar management for real-time feedback
• Console output formatting
• Error display and reporting

Evaluation Metrics

The system evaluates workflows across seven categories, with each category having its own specialized evaluator chain that runs in parallel:

1. Functionality (25% weight)

   • Does the workflow achieve the intended goal?
   • Are the right nodes selected?
   • Is core functionality explicitly requested implemented?

2. Connections (15% weight)

   • Are nodes properly connected?
   • Is data flow logical?
   • Are connection paths optimized?

3. Expressions (15% weight)

   • Are n8n expressions syntactically correct?
   • Do they reference valid data paths?
   • Are expressions efficient and maintainable?

4. Node Configuration (15% weight)

   • Are node parameters properly set?
   • Are required fields populated?
   • Are configurations appropriate for the use case?

5. Efficiency (10% weight)

   • Redundancy Score: Avoiding duplicate operations that could be consolidated
   • Path Optimization: Using optimal execution paths
   • Node Count Efficiency: Using minimal necessary nodes
   • Are backup/fallback paths intentional vs. wasteful?

6. Data Flow (10% weight)

   • Is data flowing correctly between nodes?
   • Are data transformations logical and necessary?
   • Is data validation properly implemented?

7. Maintainability (5% weight)

   • Node Naming Quality: Are nodes descriptively named?
   • Workflow Organization: Is the structure logically organized?
   • Modularity: Are components reusable and well-structured?

8. Structural Similarity (5% weight, optional)

   • How closely does the structure match a reference workflow?
   • Only evaluated when reference workflow is provided

Violation Severity Levels

Violations are categorized by severity:

• Critical (-40 to -50 points): Workflow-breaking issues
• Major (-15 to -25 points): Significant problems affecting functionality
• Minor (-5 to -15 points): Non-critical issues or inefficiencies

Running Evaluations

CLI Evaluation

# Run with default settings
pnpm eval

# Run a specific test case
pnpm eval --test-case google-sheets-processing
pnpm eval --test-case extract-from-file

# With additional generated test cases
GENERATE_TEST_CASES=true pnpm eval

# With custom concurrency
EVALUATION_CONCURRENCY=10 pnpm eval

Langsmith Evaluation

# Set required environment variables
export LANGSMITH_API_KEY=your_api_key
# Optionally specify dataset
export LANGSMITH_DATASET_NAME=your_dataset_name

# Run evaluation
pnpm eval:langsmith

Configuration

Required Files

nodes.json

IMPORTANT: The evaluation framework requires a nodes.json file in the evaluations root directory (evaluations/nodes.json).

This file contains all n8n node type definitions and is used by the AI Workflow Builder agent to:

• Know what nodes are available in n8n
• Understand node parameters and their schemas
• Generate valid workflows with proper node configurations

Why is this required? The AI Workflow Builder agent needs access to node definitions to generate workflows. In a normal n8n runtime, these definitions are loaded automatically. However, since the evaluation framework instantiates the agent without a running n8n instance, we must provide the node definitions manually via nodes.json.

How to generate nodes.json:

1. Run your n8n instance
2. Download the node definitions from locally running n8n instance(http://localhost:5678/types/nodes.json)
3. Save the node definitions to evaluations/nodes.json curl -o evaluations/nodes.json http://localhost:5678/types/nodes.json

The evaluation will fail with a clear error message if nodes.json is missing.

Environment Variables

• N8N_AI_ANTHROPIC_KEY - Required for LLM access
• LANGSMITH_API_KEY - Required for Langsmith evaluation
• USE_LANGSMITH_EVAL - Set to "true" to use Langsmith mode
• LANGSMITH_DATASET_NAME - Override default dataset name
• EVALUATION_CONCURRENCY - Number of parallel test executions (default: 5)
• GENERATE_TEST_CASES - Set to "true" to generate additional test cases
• LLM_MODEL - Model identifier for metadata tracking

Output

CLI Evaluation Output

• Console Display: Real-time progress, test results, and summary statistics
• Markdown Report: results/evaluation-report-[timestamp].md
• JSON Results: results/evaluation-results-[timestamp].json

Langsmith Evaluation Output

• Results are stored in Langsmith dashboard
• Experiment name format: workflow-builder-evaluation-[date]
• Includes detailed metrics for each evaluation category

Adding New Test Cases

Test cases are defined in chains/test-case-generator.ts. Each test case requires:

• id: Unique identifier
• name: Descriptive name
• prompt: Natural language description of the workflow to generate
• referenceWorkflow (optional): Expected workflow structure for comparison

Extending the Framework

To add new evaluation metrics:

1. Create a new evaluator file in chains/evaluators/ following the existing pattern
2. Update the EvaluationResult schema in types/evaluation.ts to include the new metric
3. Add the new evaluator to the exports in chains/evaluators/index.ts
4. Import and call the new evaluator in chains/workflow-evaluator.ts's Promise.all() array
5. Adjust weight calculations in the calculateWeightedScore function
6. Update the evaluator in langsmith/evaluator.ts to include new metrics