zrb-llm-evaluator 0.1.7

Multi-trial experiment runner for zrb chat

zrb-llm-evaluator

Multi-trial experiment runner for testing LLMs against structured test cases via zrb chat.

Given a set of models and test cases, it runs every combination across N trials, collects structured results, and generates reports. Supports concurrent execution, timeout handling, resume from partial runs, and pluggable validators.

Quick Start

# Install
poetry install

# Create a test case directory
mkdir -p my-cases/hello-world
cat > my-cases/hello-world/instruction.txt << 'EOF'
Write a Python function that returns "hello, world".
EOF

# Run an experiment (requires zrb installed and configured)
zrb-llm-evaluator run \
  --models openai:gpt-4o \
  --test-cases ./my-cases/hello-world/ \
  --trials 1

Features

• Runs N models × M test cases × T trials as a flat experiment grid
• Concurrent trial execution with configurable parallelism (asyncio + semaphore)
• Per-trial timeout kills subprocess and preserves partial output
• Resume support: re-run with the same output directory to skip completed cells
• Pluggable validators: each test case provides a validator.py implementing a typed protocol
• Atomic results.json writes (temp file + os.rename)
• Cost summary line parsing from zrb output
• Custom CLI binary name for white-labeled zrb forks

Installation

Prerequisites: Python ≥ 3.11, Poetry, and an installed zrb CLI with API keys configured.

git clone <repo>
cd zrb-llm-evaluator
poetry install

Verify the CLI works:

poetry run zrb-llm-evaluator --help

Usage

Test Case Format

Each test case is a directory containing:

my-cases/<test-name>/
├── instruction.txt    # Required — the prompt sent to the LLM
├── validator.py       # Required — validation logic (see below)
└── workdir/           # Optional — files copied into the trial's working directory

Writing a Validator

validator.py must expose a top-level validator object that implements ValidatorProtocol:

# my-cases/hello-world/validator.py
from pathlib import Path
from zrb_llm_evaluator.models import ValidationResult, ValidationCheck
from zrb_llm_evaluator.protocols import ValidatorProtocol

class HelloValidator:
    def validate(self, output_dir: Path, log_content: str) -> ValidationResult:
        passed = "hello, world" in log_content.lower()
        return ValidationResult(
            status="PASS" if passed else "FAIL",
            score=1.0 if passed else 0.0,
            details=[
                ValidationCheck(
                    name="contains_greeting",
                    passed=passed,
                    message="Output contains 'hello, world'" if passed
                            else "Missing expected greeting",
                )
            ],
        )

validator = HelloValidator()

The framework validates protocol conformance at load time. validator.py that doesn't implement ValidatorProtocol is rejected before any trial runs.

ValidationResult

Field	Type	Description
status	"EXCELLENT" | "PASS" | "FAIL"	Overall outcome
score	float (0.0–1.0)	Normalized score
details	list[ValidationCheck]	Per-check breakdown

CLI Reference

run

Run a full experiment.

zrb-llm-evaluator run \
  --models openai:gpt-4o,google-gla:gemini-2.5-flash \
  --test-cases ./cases/bug-fix,./cases/copywriting \
  --trials 3 \
  --parallelism 4 \
  --timeout 300 \
  --cli-name zrb \
  --output-dir ./out

Option	Default	Description
--models	required	Comma-separated list in provider:name format
--test-cases	required	Comma-separated list of test case directory paths
--trials	3	Trials per model × test case cell
--parallelism	4	Max concurrent subprocesses
--timeout	300	Per-trial timeout in seconds
--cli-name	zrb	CLI binary to invoke
--output-dir	./out	Output directory for results

Output: results.json (structured) + report.md (human-readable).

list

List completed trials from a previous experiment.

zrb-llm-evaluator list --dir ./out

report

Re-generate the Markdown report from existing results.json.

zrb-llm-evaluator report --dir ./out

Architecture

The runner has four layers:

1. CLI (cli.py) — Typer entry point, parses args, validates config
2. Loader (loader.py) — Discovers test cases from directories, imports validators, checks protocol conformance
3. Runner (runner.py) — Async subprocess orchestration with asyncio.Semaphore, asyncio.wait_for, and ResumeManager for idempotent resumption. Each trial creates an isolated directory {output}/{model_safe}/{test_case}/trial-{N}/ (colons in the model name are sanitized) with its own history directory
4. Reporter (reporter.py) — Generates Markdown and JSON output with atomic file writes

Key design decisions are documented in docs/adr/.

Output Structure

./out/
├── results.json          # Structured results (list of TrialResult)
├── report.md             # Human-readable report
├── openai_gpt-4o/
│   └── bug-fix/
│       ├── trial-1/
│       │   ├── stdout.log            # Raw subprocess stdout/stderr
│       │   └── history/              # ZRB_LLM_HISTORY_DIR
│       │       └── <session>.json
│       ├── trial-2/
│       └── trial-3/
└── google-gla_gemini-2.5-flash/
    └── ...

Development

poetry install --with dev
poetry run pytest tests/experiment-runner/
poetry run ruff check
poetry run mypy src/

License

MIT.