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

Evaluations (v2 harness)

Internal evaluation harness for the AI Workflow Builder. Supports local CLI runs and LangSmith-backed runs, using the same evaluators.

Quick Start

Run from the package directory:

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

# Local: run against default prompts (fixtures/default-prompts.csv)
pnpm eval --verbose

# Local: single prompt
pnpm eval --prompt "Create a workflow that..." --verbose

# Local: custom CSV file
pnpm eval --prompts-csv path/to/prompts.csv --verbose

# Local: pairwise + programmatic
pnpm eval:pairwise --prompt "Create a workflow that..." --dos "Must use Slack" --donts "No HTTP Request node" --verbose

# LangSmith: LLM-judge + programmatic
pnpm eval:langsmith --dataset "workflow-builder-canvas-prompts" --name "my-exp" --concurrency 10 --max-examples 20 --verbose

# LangSmith: pairwise + programmatic
pnpm eval:pairwise:langsmith --dataset "notion-pairwise-workflows" --name "pairwise-exp" --filter "technique:content_generation" --max-examples 50 --verbose

popd

Prerequisites

• LLM key (required for generation and any LLM-based evaluators):
  • N8N_AI_ANTHROPIC_KEY (see evaluations/support/environment.ts)
• Node definitions (required for workflow generation, and used by evaluators):
  • export using pnpm export:nodes in this package.
  • evaluations/.data/nodes.json (see evaluations/support/load-nodes.ts)
  • Optional: N8N_EVALS_DISABLED_NODES="n8n-nodes-base.httpRequest,..." to exclude specific nodes from generation.
• LangSmith (only for --backend langsmith runs):
  • LANGSMITH_API_KEY
  • LANGSMITH_TRACING=true (the harness sets this in LangSmith mode, but exporting it is fine)
  • Optional: LANGSMITH_MINIMAL_TRACING=false to disable trace filtering (useful when debugging traces; default is filtered)

Mental Model

flowchart TB
    subgraph Config["runEvaluation(config)"]
        direction LR
        C1["mode: 'local' | 'langsmith'"]
        C2["dataset: TestCase[] | string"]
        C3["generateWorkflow: (prompt) => workflow"]
        C4["evaluators: Evaluator[]"]
    end

    Config --> Loop

    subgraph Loop["For each test case"]
        G["1. generateWorkflow(prompt)"]
        E["2. evaluateWithPlugins (parallel)"]
        A["3. Aggregate feedback"]
        G --> E --> A
    end

    Loop --> Evaluators

    subgraph Evaluators["Evaluators (run in parallel)"]
        direction LR
        LLM["LLM-Judge"]
        Pair["Pairwise"]
        Prog["Programmatic"]
        Bin["Binary-Checks"]
    end

    Evaluators --> Feedback

    subgraph Feedback["Feedback[]"]
        F1["evaluator: string"]
        F2["metric: string"]
        F3["score: 0-1"]
        F4["kind: 'score' | 'metric' | 'detail'"]
        F5["comment?: string"]
    end

Key Concepts

Evaluator

A function that takes a workflow and returns feedback:

interface Evaluator<TContext = EvaluationContext> {
  name: string;
  evaluate(workflow: SimpleWorkflow, ctx: TContext): Promise<Feedback[]>;
}

Evaluators are:

• Independent - no dependencies between evaluators
• Parallel - all evaluators run concurrently
• Error-tolerant - if one fails, others continue

Feedback

The universal output format from all evaluators:

interface Feedback {
  evaluator: string; // e.g., "llm-judge", "pairwise"
  metric: string;    // e.g., "functionality", "judge1", "efficiency.nodeCountEfficiency"
  score: number;    // 0.0 to 1.0
  comment?: string; // Optional explanation/violations
  kind: 'score' | 'metric' | 'detail';
}

kind is used by the harness scoring logic:

• score: the evaluator’s single overall score (preferred for scoring)
• metric: stable per-category metrics (useful to show, but not necessarily used for scoring if a score exists)
• detail: verbose/unstable metrics and details (never used for scoring when a score is present)

Lifecycle Hooks

Centralized logging via hooks (not per-evaluator logging):

interface EvaluationLifecycle {
  onStart(config): void;
  onExampleStart(index, total, prompt): void;
  onWorkflowGenerated(workflow, durationMs): void;
  onEvaluatorComplete(name, feedback): void;
  onEvaluatorError(name, error): void;
  onExampleComplete(index, result): void;
  onEnd(summary): void;
}

Context

Evaluators receive context from multiple sources:

globalContext (from RunConfig.context)
       +
testCase.context (per-test-case overrides)
       +
prompt (always included)
       =
Final context passed to evaluators

Local vs LangSmith Mode

Local Mode

import { createLogger } from './harness/logger';

const logger = createLogger(true); // verbose output

const config: RunConfig = {
  mode: 'local',
  dataset: [
    { prompt: 'Create a workflow...', context: { dos: '...' } },
  ],
  generateWorkflow,
  evaluators: [llmJudge, programmatic],
  lifecycle: createConsoleLifecycle({ verbose: true, logger }),
  logger,
};

await runEvaluation(config);

• Processes test cases sequentially (examples), but LLM-bound work is capped via llmCallLimiter (see evaluations/harness/runner.ts)
• Results logged to console via lifecycle hooks
• The harness returns a RunSummary; the CLI decides exit codes

LangSmith Mode

import { createLogger } from './harness/logger';

const logger = createLogger(false); // non-verbose output

const config: RunConfig = {
  mode: 'langsmith',
  dataset: 'my-dataset-name',  // LangSmith dataset
  generateWorkflow,
  evaluators: [llmJudge, programmatic],
  logger,
  langsmithOptions: {
    experimentName: 'experiment-1',
    repetitions: 1,
    concurrency: 4,
  },
};

await runEvaluation(config);

If you want no output (e.g. unit tests), use createQuietLifecycle() (or pass a stub logger) instead of relying on a "silent logger".

Architecture: The target function does ALL work (generation + evaluation). The LangSmith evaluator just extracts pre-computed feedback. The runner flushes pending trace batches before returning, so traces/results reliably show up in LangSmith.

// Inside runLangsmith():

// IMPORTANT: Create traceable wrapper ONCE outside the target function
// to avoid context leaking in concurrent scenarios. Pass params explicitly.
const traceableGenerateWorkflow = traceable(
  async (args: { prompt: string; genFn: Function }) => {
    return await args.genFn(args.prompt);
  },
  { name: 'workflow_generation', run_type: 'chain', client: lsClient }
);

const target = async (inputs) => {
  const { prompt } = inputs;
  // Call the pre-created wrapper with explicit params (no closures)
  const workflow = await traceableGenerateWorkflow({
    prompt,
    genFn: generateWorkflow,
  });
  const feedback = await evaluateWithPlugins(workflow, evaluators);
  return { workflow, prompt, feedback };  // Pre-computed!
};

// LangSmith evaluator converts internal `{ evaluator, metric }` into `{ key, score, comment? }`:
const feedbackExtractor = (run) => run.outputs.feedback.map(toLangsmithEvaluationResult);

LangSmith Tracing

• Do not wrap the target function with traceable() — evaluate() handles that automatically
• Do create traceable wrappers once outside the target function (not inside concurrent code)
• Do pass all parameters explicitly to avoid closure-based context leaking
• Do use getTracingCallbacks() to bridge traceable context to LangChain calls (pass callbacks to agent.chat() or chain's invoke())

Available Evaluators

LLM-Judge

Uses an LLM to evaluate workflow quality across multiple dimensions:

import { createLLMJudgeEvaluator } from './evaluators';

const evaluator = createLLMJudgeEvaluator(llm, nodeTypes);

Evaluator: llm-judge

Metrics: functionality, connections, expressions, nodeConfiguration, efficiency, dataFlow, maintainability, overallScore

Context required: { prompt: string }

Pairwise

Uses a panel of judges to evaluate against dos/donts criteria:

import { createPairwiseEvaluator } from './evaluators';

const evaluator = createPairwiseEvaluator(llm, { numJudges: 3 });

Evaluator: pairwise

Metrics: pairwise_primary, pairwise_diagnostic, pairwise_judges_passed, pairwise_total_passes, pairwise_total_violations

Additional per-judge details may also be emitted (e.g. judge1, judge2).

Context required: { dos?: string, donts?: string }

Binary-Checks

Per-check binary pass/fail evaluation — 17 deterministic checks (fast, no LLM) plus 5 LLM-judge checks (parallel):

import { createBinaryChecksEvaluator } from './evaluators';

const evaluator = createBinaryChecksEvaluator({ nodeTypes, llm });

Evaluator: binary-checks

Deterministic checks: has_nodes, all_nodes_connected, no_unreachable_nodes, has_trigger, no_empty_set_nodes, agent_has_dynamic_prompt, agent_has_language_model, memory_properly_connected, vector_store_has_embeddings, has_start_node, no_hardcoded_credentials, no_unnecessary_code_nodes, expressions_reference_existing_nodes, valid_required_parameters, valid_options_values, no_invalid_from_ai, tools_have_parameters

LLM checks (require llm option): fulfills_user_request, correct_node_operations, valid_data_flow, handles_multiple_items, descriptive_node_names

Context required: { prompt: string }, optional { annotations?: Record<string, unknown> }

CLI:

# Run all checks
pnpm eval --suite binary-checks --prompt "Create a Slack workflow"

# Run specific checks only
pnpm eval --suite binary-checks --checks has_nodes,has_trigger --prompt "..."

# LangSmith
pnpm eval --suite binary-checks --langsmith --dataset "binary-checks-spec-prompts"

Programmatic

Rule-based checks without LLM calls:

import { createProgrammaticEvaluator } from './evaluators';

const evaluator = createProgrammaticEvaluator(nodeTypes);

Evaluator: programmatic

Metrics: overall, connections, trigger, agentPrompt, tools, fromAi (optional: similarity)

Context required: None

Metric Naming (LangSmith compatibility)

LangSmith metric keys are derived from Feedback in evaluations/harness/feedback.ts:

• llm-judge: unprefixed (e.g. overallScore, maintainability.workflowOrganization)
• programmatic: prefixed (e.g. programmatic.trigger)
• pairwise: v1-compatible keys stay unprefixed (e.g. pairwise_primary); non-v1 details are namespaced (e.g. pairwise.judge1)

CLI Usage

NPM Scripts

# Local mode with LLM-judge evaluator
pnpm eval --prompt "Create a workflow..." --verbose

# LangSmith mode (results in LangSmith dashboard)
pnpm eval:langsmith --name "my-experiment" --verbose

# Pairwise mode (local)
pnpm eval:pairwise --prompt "..." --dos "Must use Slack" --donts "No HTTP"

# Pairwise mode with LangSmith
pnpm eval:pairwise:langsmith --name "pairwise-exp" --verbose

Notes:

• In --backend langsmith mode, the CLI requires --dataset and rejects --prompt, --prompts-csv, and --test-case.
• --output-dir only applies to local mode (it writes artifacts to disk).

Common Flags

--suite <llm-judge|pairwise|programmatic|similarity|binary-checks>
--backend <local|langsmith>   # Or `--langsmith` as a shortcut
--verbose, -v       # Enable verbose output
--name <name>       # Experiment name (LangSmith mode)
--dataset <name>    # LangSmith dataset name
--max-examples <n>  # Limit number of examples to evaluate
--concurrency <n>   # Max concurrent evaluations (default: 5)
--repetitions <n>   # Number of repetitions per example
--test-case <id>    # Run a predefined test case (local)
--prompts-csv <path># Load prompts from CSV (local)
--prompt <text>     # Single prompt for local testing
--dos <text>        # Pairwise: things the workflow should do
--donts <text>      # Pairwise: things the workflow should not do
--checks <names>    # Comma-separated binary check names (binary-checks suite only)
--output-dir <dir>  # Local mode: write artifacts (one folder per example + summary.json)
--template-examples # Enable template examples feature flag
--webhook-url <url> # Send results to webhook URL on completion (HTTPS only)
--webhook-secret <s> # HMAC-SHA256 secret for webhook authentication (min 16 chars)

CSV Format

--prompts-csv supports optional headers. Recognized columns:

• prompt (required)
• id (optional)
• dos / do (optional)
• donts / dont (optional)

Example:

id,prompt,dos,donts
pw-001,"Create a workflow to sync Gmail to Notion","Must use Notion","No HTTP Request node"

Direct Usage

# Local mode (default)
tsx evaluations/cli/index.ts --prompt "Create a workflow..." --verbose

# LangSmith mode
tsx evaluations/cli/index.ts --backend langsmith --name "my-experiment" --verbose

# Pairwise mode
tsx evaluations/cli/index.ts --suite pairwise --prompt "..." --dos "Must use Slack"

Components & Where Things Live

This directory is intentionally split by responsibility:

• evaluations/cli/: CLI entrypoint and input parsing (cli/index.ts, cli/argument-parser.ts, cli/csv-prompt-loader.ts, cli/webhook.ts)
• evaluations/harness/: orchestration, scoring, logging, and artifact writing (harness/runner.ts, harness/lifecycle.ts, harness/score-calculator.ts, harness/output.ts)
• evaluations/evaluators/: evaluator factories used by the harness (LLM-judge, pairwise, programmatic, similarity, binary-checks)
• evaluations/judge/: the LLM-judge “engine” (schemas + category evaluators + judge/workflow-evaluator.ts)
• evaluations/langsmith/: LangSmith-specific helpers (langsmith/trace-filters.ts, langsmith/types.ts)
• evaluations/support/: environment setup, node loading, report generation, and test-case generation
• evaluations/programmatic/: programmatic evaluator implementation (TypeScript) + programmatic/python/ (kept separate)

Extending

Adding a new evaluator

Add an evaluator by implementing the Evaluator interface and returning Feedback[]:

• Put evaluator factories under evaluations/evaluators/<name>/
• Make sure you emit at least one kind: 'score' item (the harness scoring prefers this)
• If you need custom context, extend via Evaluator<MyContext> and validate required fields at runtime (keep the base context cast-free)
• If you want stable LangSmith keys, update evaluations/harness/feedback.ts

Adding a new “runner” (backend)

The harness runner is evaluations/harness/runner.ts. Today it supports:

• mode: 'local' (local dataset array + optional artifacts)
• mode: 'langsmith' (LangSmith dataset or preloaded examples)

To add a new backend, keep evaluators backend-agnostic and extend the runner with a new RunConfig['mode'] branch.

File Structure

evaluations/
├── __tests__/               # Unit tests
├── cli/                     # CLI entry + arg parsing + CSV loader
├── evaluators/              # Evaluator factories
│   ├── binary-checks/       # Binary pass/fail checks (deterministic + LLM)
│   ├── llm-judge/
│   ├── pairwise/
│   ├── programmatic/
│   └── similarity/
├── harness/                 # Runner + lifecycle + scoring + artifacts
├── fixtures/                # Local fixtures (tracked)
│   └── reference-workflows/
├── judge/                   # LLM-judge internals (schemas + judge evaluators)
├── langsmith/               # LangSmith-specific helpers (types + trace filters)
├── programmatic/            # Programmatic evaluation logic
├── support/                 # Environment + node loading + reports + test case gen
├── index.ts                 # Public exports
└── README.md                # This file

Error Handling

The harness uses "skip and continue" error handling:

• If an evaluator throws, it returns error feedback and continues
• If workflow generation fails, the example is marked as error and continues
• Other evaluators still run even if one fails

// Error feedback format:
{ evaluator: 'evaluator-name', metric: 'error', score: 0, kind: 'score', comment: 'Error message' }

Testing

From packages/@n8n/ai-workflow-builder.ee:

pnpm test:eval

CI Integration

Automated Eval Runs

Evaluations run automatically via GitHub Actions:

Trigger	Reps	Judges	Dataset	When
Push to master	1	1	workflow-builder-canvas-prompts	On changes to ai-workflow-builder.ee/
Scheduled	3	3	prompts-v2	Saturdays 22:00 UTC
Minor release	2	3	workflow-builder-canvas-prompts	On vX.Y.0 releases
Manual dispatch	Configurable	Configurable	Configurable	Via GitHub Actions UI

Skipping Evals on Merge

To skip eval runs when merging a PR that doesn't affect prompts/AI behavior, use any of:

• PR label: Add no-prompt-changes label to the PR
• PR title: Include (no-prompt-changes) in the PR title
• Commit message: Include (no-prompt-changes) in the merge commit message

Experiment Naming Convention

LangSmith experiments follow this naming pattern:

Source	Format	Example
Branch with ticket	{TICKET-ID}_{YYYY_MM_DD}	AI-1234_2026_01_20
Branch without ticket	CI_{branch}_{YYYY_MM_DD}	CI_master_2026_01_20
Scheduled run	CI_scheduled_{YYYY_MM_DD}	CI_scheduled_2026_01_20
Minor release	CI_vX.Y_{YYYY_MM_DD}	CI_v1.70_2026_01_20
Manual dispatch	CI_manual_{YYYY_MM_DD}	CI_manual_2026_01_20

CI Metadata

All LangSmith experiments include metadata to distinguish CI runs from local development:

{
  "source": "ci",
  "trigger": "push",
  "commitSha": "abc123...",
  "branch": "master",
  "runId": "12345678"
}

Local runs show "source": "local" with no other CI fields.

Webhook Notifications

The CLI supports sending evaluation results to a webhook URL when evaluations complete. This enables integrations with Slack, Discord, or custom notification systems.

pnpm eval:langsmith --dataset "my-dataset" --webhook-url "https://hooks.slack.com/services/..."

Why custom webhooks?

LangSmith's evaluate() function does not provide native webhook support for experiment run notifications. LangSmith offers webhooks via:

• Trace Rules — triggered on individual traces, not experiment completions
• API endpoint webhooks — for specific API events, but not for evaluate() completions
• API polling — requires external orchestration to detect when experiments finish

Since none of these approaches support the "notify on experiment completion" use case for the evaluate() SDK function, we implemented a custom webhook system that fires after all evaluations complete, sending a summary payload with experiment metadata.

Payload format:

{
  "suite": "llm-judge",
  "summary": {
    "totalExamples": 50,
    "passed": 45,
    "failed": 5,
    "errors": 0,
    "averageScore": 0.87
  },
  "evaluatorAverages": {
    "llm-judge": 0.85,
    "programmatic": 0.92
  },
  "totalDurationMs": 120000,
  "metadata": {
    "source": "ci",
    "trigger": "push",
    "runId": "12345678"
  },
  "langsmith": {
    "experimentName": "AI-1234_2026_01_20",
    "experimentId": "48660e0e-0ed5-4e32-9e04-88803d7c161f",
    "datasetId": "b04d1ce8-8e3f-455a-818c-ee2c7e14c458",
    "datasetName": "workflow-builder-canvas-prompts"
  }
}

The langsmith object (only present in LangSmith mode) contains IDs and names for constructing comparison URLs.

Security:

• Only HTTPS URLs are allowed
• Localhost and private/internal IPs are blocked (SSRF prevention)
• DNS resolution validates that hostnames don't resolve to private IPs
• Webhook URLs are masked in logs to protect embedded tokens
• HMAC-SHA256 signature for request authentication (optional but recommended)

Webhook Authentication (HMAC Signature)

For production use, authenticate webhook requests using HMAC-SHA256 signatures:

# Generate a secret (run once, store securely)
openssl rand -hex 32

# Use with the CLI
pnpm eval:langsmith --dataset "my-dataset" \
  --webhook-url "https://your.endpoint/webhook" \
  --webhook-secret "your-64-char-hex-secret"

When a secret is provided, requests include:

• X-Signature-256: HMAC-SHA256 signature (sha256=<hex>)
• X-Timestamp: Unix timestamp in milliseconds

How it works:

Sender:
1. payload = JSON.stringify(body)
2. signatureInput = `${timestamp}.${payload}`
3. signature = HMAC-SHA256(signatureInput, secret)
4. Send with headers: X-Signature-256, X-Timestamp

Receiver:
1. Extract X-Signature-256 and X-Timestamp headers
2. Check timestamp is recent (< 5 minutes old)
3. Recreate: signatureInput = `${timestamp}.${rawBody}`
4. Compute expected = HMAC-SHA256(signatureInput, secret)
5. Compare signatures (timing-safe)

Verifying in an n8n workflow:

Use a Code node after the Webhook trigger:

const crypto = require('crypto');

// Get from webhook input (adjust based on your webhook node config)
const signature = $input.first().json.headers['x-signature-256'];
const timestamp = $input.first().json.headers['x-timestamp'];
const rawBody = $input.first().json.rawBody ?? $input.first().json.body;
const body = typeof rawBody === 'string' ? rawBody : JSON.stringify(rawBody);

// Your secret (use n8n credentials or environment variable)
const secret = $env.WEBHOOK_SECRET;

// Verify timestamp (reject requests older than 5 minutes)
const MAX_AGE_MS = 5 * 60 * 1000;
const age = Date.now() - parseInt(timestamp, 10);
if (!signature || !timestamp) throw new Error('Missing signature headers');
if (age > MAX_AGE_MS) throw new Error('Request too old');

// Compute and compare signature
const payload = `${timestamp}.${body}`;
const expected = 'sha256=' + crypto.createHmac('sha256', secret)
  .update(payload, 'utf8').digest('hex');

if (signature.length !== expected.length || !crypto.timingSafeEqual(Buffer.from(signature), Buffer.from(expected))) {
  throw new Error('Invalid signature');
}

// Valid! Return parsed payload
return [{ json: JSON.parse(body) }];

CI Configuration:

Add secrets to GitHub:

• EVALS_WEBHOOK_URL: Your webhook endpoint
• EVALS_WEBHOOK_SECRET: The HMAC secret (64-char hex string)

Debug Dataset

For faster iteration during development, use a minimal dataset:

# Use the debug dataset with a single example
pnpm eval:langsmith --dataset "workflow-builder-debug-single" --name "debug-run" --verbose

To create your own debug dataset in LangSmith:

1. Go to LangSmith → Datasets
2. Create a new dataset with 1-3 representative examples
3. Use it with --dataset "your-debug-dataset"

This is useful for:

• Testing workflow changes quickly
• Debugging evaluator issues
• Validating CI workflow changes locally