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multi-grader-gate

README.md

Multi-Grader Gate Example

This example demonstrates multi-grader gates that combine multiple evaluation metrics with sophisticated pass/fail logic.

What This Example Shows

  • Logical gates with AND/OR operators
  • Weighted average gates for combining metrics with different importance
  • Different aggregation functions (avg_score, accuracy, min, max, percentiles)
  • Custom pass thresholds for accuracy calculations
  • Combining rubric grading (LLM judge) with tool grading (deterministic functions)

Key Takeaway

Multi-grader gates enable sophisticated evaluation criteria that go beyond single metrics. You can:

  • Require multiple conditions to pass simultaneously (AND logic)
  • Pass if any condition passes (OR logic)
  • Combine metrics with weighted importance
  • Use different aggregation functions per metric

Running This Example

Local Setup (Default)

Start your local Letta server:

letta server

Set your OpenAI API key (required for rubric grader):

export OPENAI_API_KEY=your-key

Then run either suite:

cd examples/multi-grader-gate
letta-evals run suite.logical-and.yaml
letta-evals run suite.weighted-average.yaml

Letta Cloud Setup

export LETTA_API_KEY=your-api-key
export LETTA_PROJECT_ID=your-project-id
export OPENAI_API_KEY=your-openai-key

Update base_url in the suite files:

target:
  base_url: https://api.letta.com/

Gate Types

1. Simple Gate

Gates on a single metric:

gate:
  kind: simple
  metric_key: quality
  aggregation: avg_score
  op: gte
  value: 0.6

2. Logical Gate (suite.logical-and.yaml)

Combines multiple conditions with AND/OR logic. All conditions must pass for AND, at least one must pass for OR.

Example (AND):

gate:
  kind: logical
  operator: and
  conditions:
    - metric_key: quality
      aggregation: avg_score
      op: gte
      value: 0.6
    - metric_key: ascii_only
      aggregation: accuracy
      pass_threshold: 1.0
      op: gte
      value: 60

This passes only if:

  • Quality (avg_score) >= 0.6: Average quality score across all samples >= 0.6
  • ASCII-only (accuracy) >= 60%: At least 60% of samples have perfect ASCII scores (1.0)

Example (OR):

gate:
  kind: logical
  operator: or
  conditions:
    - metric_key: exact_match
      aggregation: accuracy
      op: eq
      value: 100
    - metric_key: quality
      aggregation: avg_score
      op: gte
      value: 0.9

This passes if EITHER exact matches are 100% OR quality is very high (>= 0.9).

Nested logical conditions:

Logical gates can be nested for complex logic like "(A AND B) OR C":

gate:
  kind: logical
  operator: or
  conditions:
    - kind: logical
      operator: and
      conditions:
        - metric_key: accuracy
          aggregation: avg_score
          op: gte
          value: 0.8
        - metric_key: coherence
          aggregation: min
          op: gte
          value: 0.6
    - metric_key: fallback_check
      aggregation: accuracy
      op: eq
      value: 100

3. Weighted Average Gate (suite.weighted-average.yaml)

Combines multiple metrics with configurable weights. The weighted average is compared against a threshold.

gate:
  kind: weighted_average
  aggregation: avg_score
  weights:
    quality: 0.7
    ascii_only: 0.3
  op: gte
  value: 0.75

This computes: 0.7 * avg(quality) + 0.3 * avg(ascii_only) and passes if the result >= 0.75.

Key points:

  • Weights are automatically normalized (don't need to sum to 1.0)
  • The same aggregation function is applied to all metrics before weighting
  • Useful for expressing relative importance of different quality dimensions

Aggregation Functions

Each condition in a gate can use different aggregation functions:

Aggregation Description Use Case
avg_score Average score across all samples Overall quality metrics
accuracy Percentage of samples passing pass_threshold Binary pass/fail checks
min Minimum score across all samples Worst-case requirements
max Maximum score across all samples Best-case requirements
median Median score Robust to outliers
p50 50th percentile (same as median) Robust to outliers
p95 95th percentile High-end performance
p99 99th percentile Tail behavior

Examples:

# average quality must be high
- metric_key: quality
  aggregation: avg_score
  op: gte
  value: 0.8

# worst-case safety must be acceptable
- metric_key: safety
  aggregation: min
  op: gte
  value: 0.5

# at least 90% of samples must pass
- metric_key: format_check
  aggregation: accuracy
  pass_threshold: 1.0
  op: gte
  value: 90

# 95th percentile latency must be acceptable
- metric_key: response_time
  aggregation: p95
  op: lte
  value: 5.0

Pass Threshold for Accuracy

When using aggregation: accuracy, the pass_threshold parameter defines what score counts as "passing" for a sample:

- metric_key: ascii_only
  aggregation: accuracy
  pass_threshold: 0.8  # sample passes if score >= 0.8
  op: gte
  value: 90  # at least 90% of samples must pass

How it works:

  1. Each sample gets a score from the grader (e.g., 0.0 to 1.0)
  2. A sample "passes" if its score >= pass_threshold (defaults to 1.0)
  3. Accuracy = (passed samples / total samples) * 100
  4. The gate condition checks if accuracy meets the threshold

Example:

  • Scores: [1.0, 0.9, 0.85, 0.7, 0.6]
  • pass_threshold: 0.8
  • Passed samples: 3 (1.0, 0.9, 0.85)
  • Accuracy: 3/5 = 60%

Comparison Operators

All gate conditions support these operators:

Operator Symbol Description
gte ≥ Greater than or equal
gt > Greater than
lte ≤ Less than or equal
lt < Less than
eq = Equal

Graders in This Example

Quality (Rubric Grader)

LLM-as-judge using detailed scoring rubric in rubric.txt:

  • Instruction compliance (0.30)
  • Requested item match (0.30)
  • Recognizability (0.20)
  • Craftsmanship (0.10)
  • Effort (0.10)

Scores sum to 1.0 for normalized evaluation.

ASCII-only (Tool Grader)

Deterministic check using built-in ascii_printable_only function:

  • Returns 1.0 if all characters are printable ASCII
  • Returns 0.0 if non-ASCII characters are found

Suite Variants

suite.logical-and.yaml

Demonstrates logical AND gate requiring:

  • High quality (avg_score >= 0.6)
  • Valid ASCII in most samples (accuracy >= 60%)

Use case: Strict requirements where multiple independent criteria must all pass.

suite.weighted-average.yaml

Demonstrates weighted average gate combining:

  • Quality (70% weight)
  • ASCII validation (30% weight)
  • Overall threshold >= 0.75

Use case: Balancing multiple quality dimensions with different importance levels.

Design Guidelines

When to use Logical Gates:

  • Multiple independent requirements must all pass (AND)
  • Any of several alternatives can satisfy the requirement (OR)
  • Complex boolean logic (nested conditions)
  • Example: "Safety must be perfect AND quality must be good"

When to use Weighted Average:

  • Trading off between multiple quality dimensions
  • Some metrics are more important than others
  • Continuous quality assessment with balanced criteria
  • Example: "60% quality + 40% style must average to 0.7"

When to use Simple Gates:

  • Single metric is sufficient
  • Clear threshold for pass/fail
  • Example: "Average quality must be >= 0.6"

Common Patterns

Strict Safety + Flexible Quality

gate:
  kind: logical
  operator: and
  conditions:
    - metric_key: safety
      aggregation: accuracy
      op: eq
      value: 100  # all samples must pass safety
    - metric_key: quality
      aggregation: avg_score
      op: gte
      value: 0.6  # quality can be moderate

Balanced Multi-Dimensional Scoring

gate:
  kind: weighted_average
  aggregation: avg_score
  weights:
    accuracy: 0.4
    coherence: 0.3
    style: 0.2
    format: 0.1
  op: gte
  value: 0.75

Fallback Criteria

gate:
  kind: logical
  operator: or
  conditions:
    - metric_key: exact_match
      aggregation: accuracy
      op: eq
      value: 100  # prefer exact matches
    - metric_key: semantic_similarity
      aggregation: avg_score
      op: gte
      value: 0.9  # but accept high semantic similarity

Worst-Case Guarantees

gate:
  kind: logical
  operator: and
  conditions:
    - metric_key: quality
      aggregation: avg_score
      op: gte
      value: 0.8  # average must be high
    - metric_key: quality
      aggregation: min
      op: gte
      value: 0.5  # worst case must be acceptable