promptum 0.0.2

Async LLM benchmarking library with protocol-based extensibility

promptum

Benchmark LLMs Like a Pro. In 5 Lines of Code.

Stop writing boilerplate to test LLMs. Start getting results.

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What's This?

A dead-simple Python library for benchmarking LLM providers. Write tests once, run them across any model, get beautiful reports.

benchmark = Benchmark(provider=client, name="my_test")
benchmark.add_test(TestCase(
    prompt="What is 2+2?",
    model="gpt-3.5-turbo",
    validator=Contains("4")
))
report = await benchmark.run_async()

That's it. No setup. No config files. Just results.

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Why You Need This

Before promptum:

# Custom API client for each provider
openai_client = OpenAI(api_key=...)
anthropic_client = Anthropic(api_key=...)

# Manual validation logic
if "correct answer" not in response:
    failed_tests.append(...)

# Track metrics yourself
latency = end_time - start_time
tokens = response.usage.total_tokens

# Write your own retry logic
for attempt in range(max_retries):
    try:
        response = client.chat.completions.create(...)
        break
    except Exception:
        sleep(2 ** attempt)

After promptum:

report = await benchmark.run_async()
summary = report.get_summary()  # Metrics captured automatically

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Quick Start

pip install promptum  # (or: uv pip install promptum)
export OPENROUTER_API_KEY="your-key"

import asyncio
from promptum import Benchmark, TestCase, OpenRouterClient, Contains

async def main():
    async with OpenRouterClient(api_key="your-key") as client:
        benchmark = Benchmark(provider=client, name="quick_test")

        benchmark.add_test(TestCase(
            name="basic_math",
            prompt="What is 15 * 7? Reply with just the number.",
            model="openai/gpt-3.5-turbo",
            validator=Contains("105")
        ))

        report = await benchmark.run_async()
        summary = report.get_summary()

        print(f"✓ {summary['passed']}/{summary['total']} tests passed")
        print(f"⚡ {summary['avg_latency_ms']:.0f}ms average")
        print(f"💰 ${summary['total_cost_usd']:.6f} total cost")

asyncio.run(main())

Run it:

python your_script.py

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What You Get

• One API for 100+ Models - OpenRouter support out of the box (OpenAI, Anthropic, Google, etc.)
• Smart Validation - ExactMatch, Contains, Regex, JsonSchema, or write your own
• Automatic Retries - Exponential/linear backoff with configurable attempts
• Metrics Tracking - Latency, tokens, cost - automatically captured
• Async by Default - Run 100 tests in parallel without breaking a sweat
• Type Safe - Full type hints, catches errors before runtime
• Zero Config - No YAML files, no setup scripts, just Python

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Real Example

Compare GPT-4 vs Claude on your tasks:

from promptum import Benchmark, TestCase, ExactMatch, Contains, Regex

tests = [
    TestCase(
        name="json_output",
        prompt='Output JSON: {"status": "ok"}',
        model="openai/gpt-4",
        validator=Regex(r'\{"status":\s*"ok"\}')
    ),
    TestCase(
        name="json_output",
        prompt='Output JSON: {"status": "ok"}',
        model="anthropic/claude-3-5-sonnet",
        validator=Regex(r'\{"status":\s*"ok"\}')
    ),
    TestCase(
        name="creative_writing",
        prompt="Write a haiku about Python",
        model="openai/gpt-4",
        validator=Contains("Python", case_sensitive=False)
    ),
]

benchmark.add_tests(tests)
report = await benchmark.run_async()

# Side-by-side model comparison
for model, summary in report.compare_models().items():
    print(f"{model}: {summary['pass_rate']:.0%} pass rate, {summary['avg_latency_ms']:.0f}ms avg")

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Use Cases

🔬 Model Evaluation - Compare GPT-4, Claude, Gemini on your specific tasks 🎯 Prompt Engineering - Test 100 prompt variations, find what works ⚡ Latency Testing - Measure real-world response times across providers 💰 Cost Analysis - Track spending per model/task before production 🔄 Regression Testing - Ensure model updates don't break your prompts 📊 A/B Testing - Data-driven model selection for your product

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Requirements

• Python 3.13+
• An OpenRouter API key (or implement your own provider)

That's it. No Docker, no complex setup.

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Why Protocol-Based?

Most libraries force inheritance:

class MyProvider(BaseProvider):  # Tightly coupled
    def generate(self): ...

We use protocols (structural typing):

class MyProvider:  # No inheritance needed
    async def generate(self) -> tuple[str, Metrics]:
        # Your implementation
        return response, metrics

# It just works
benchmark = Benchmark(provider=MyProvider())

Cleaner. More flexible. More Pythonic.

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Contributing

Found a bug? Want a feature? PRs welcome!

# Development setup
git clone https://github.com/deyna256/promptum.git
cd promptum
just sync       # Install dependencies
just test       # Run tests

# Development commands
just lint       # Check code style
just format     # Format code
just typecheck  # Type checking

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License

MIT - do whatever you want with it.

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⭐ Star on GitHub | 🐛 Report Bug | 💡 Request Feature

Made for developers who value their time.