EZVals

Unit Testing for AI agents and LLM apps. Write Python functions, use EvalContext to track results, and EZVals handles storage, scoring, and a small web UI.

Installation

EZVals is intended as a development dependency.

pip install ezvals
# or with uv
uv add --dev ezvals

Quick start

Look at the examples directory for runnable snippets. Run the demo suite and open the UI:

ezvals serve examples

UI highlights

• Expand rows to see inputs, outputs, metadata, scores, and annotations.
• Edit scores or annotations inline; changes persist to JSON.
• Export dropdown: JSON, CSV (raw data), PDF, Markdown (filtered view with charts).

Authoring evals

Write evals like tests. Add a ctx: EvalContext parameter, and EZVals auto-injects a mutable context object.

from ezvals import eval, EvalContext

@eval(input="I want a refund", dataset="customer_service")
async def test_refund(ctx: EvalContext):
    ctx.output = await run_agent(ctx.input)
    assert "refund" in ctx.output.lower(), "Should acknowledge refund"

EvalContext

EvalContext is a mutable builder for constructing eval results. When your function has a parameter with type annotation : EvalContext, EZVals automatically injects an instance.

Key features:

• Auto-injection: Just add ctx: EvalContext parameter
• Direct assignment: Set ctx.output, ctx.input, ctx.reference directly
• Assertion-based scoring: Use assert statements like pytest
• Auto-return: No explicit return needed
• Exception safety: Partial data preserved on errors

Direct field access:

ctx.input = "test input"
ctx.output = "model response"
ctx.reference = "expected output"
ctx.metadata["model"] = "gpt-4"

Scoring with assertions:

assert ctx.output is not None, "Got no output"
assert "expected" in ctx.output.lower(), "Missing expected content"

Manual scoring (when needed):

ctx.add_score(True, "Test passed")  # Boolean
ctx.add_score(0.95, "High score", key="similarity")  # Numeric

Writing your first eval

Set context fields in the decorator when possible:

from ezvals import eval, EvalContext

@eval(
    input="I want a refund",
    reference="I'll help you process your refund request.",
    dataset="customer_service",
    metadata={"model": "gpt-4"}
)
async def test_refund_request(ctx: EvalContext):
    ctx.output = await run_agent(ctx.input)
    assert ctx.output == ctx.reference

Common patterns

1) Assertions (preferred):

@eval(input="What is 2+2?", reference="4", dataset="math")
async def test_arithmetic(ctx: EvalContext):
    ctx.output = await calculator(ctx.input)
    assert ctx.output == ctx.reference

2) Multiple assertions:

@eval(input="Explain quantum computing", dataset="qa")
async def test_explanation(ctx: EvalContext):
    ctx.output = await my_agent(ctx.input)

    assert len(ctx.output) > 50, "Response too short"
    assert "quantum" in ctx.output.lower(), "Should mention quantum"

3) Multiple named scores:

@eval(input="Classify this text", dataset="classification")
async def test_classifier(ctx: EvalContext):
    result = await classifier(ctx.input)
    ctx.output = result["label"]

    ctx.add_score(result["confidence"] > 0.8, "High confidence", key="confidence")
    ctx.add_score("positive" in result["label"], "Sentiment detected", key="sentiment")

@eval decorator

Wraps a function and records evaluation results.

Parameters:

• input (any): Pre-populate ctx.input
• reference (any): Pre-populate ctx.reference
• dataset (str): Groups related evals (defaults to filename)
• labels (list): Filtering tags
• metadata (dict): Pre-populate ctx.metadata
• default_score_key (str): Default key for add_score()
• timeout (float): Maximum execution time in seconds
• target (callable): Pre-hook that runs before the eval
• evaluators (list): Callables that add scores to a result

Examples:

# Minimal
@eval(input="test")
def test(ctx: EvalContext):
    ctx.output = process(ctx.input)
    assert ctx.output

# With timeout
@eval(input="complex task", timeout=5.0, dataset="performance")
async def test_with_timeout(ctx: EvalContext):
    ctx.output = await slow_agent(ctx.input)

# Target hook to run your agent
def call_agent(ctx: EvalContext):
    ctx.output = my_agent(ctx.input)

@eval(input="What is the weather?", target=call_agent, dataset="agent")
def test_with_target(ctx: EvalContext):
    assert "weather" in ctx.output.lower()

File-level defaults

Set global properties for all tests in a file using ezvals_defaults:

ezvals_defaults = {
    "dataset": "sentiment_analysis",
    "labels": ["production", "nlp"],
    "metadata": {"model": "gpt-4"}
}

@eval(input="I love this!")
def test_positive(ctx: EvalContext):
    ctx.output = analyze(ctx.input)
    assert ctx.output == "positive"

@eval(input="This is terrible", labels=["experimental"])  # Override labels
def test_negative(ctx: EvalContext):
    ctx.output = analyze(ctx.input)
    assert ctx.output == "negative"

Priority: Decorator parameters > File defaults > Built-in defaults

@parametrize

Generate multiple evals from one function. Place @eval above @parametrize.

When parameter names match EvalContext fields (input, reference, metadata, etc.), they automatically populate the context:

from ezvals import parametrize

@eval(dataset="sentiment")
@parametrize("input,reference", [
    ("I love this!", "positive"),
    ("This is terrible", "negative"),
    ("It's okay I guess", "neutral"),
])
def test_sentiment(ctx: EvalContext):
    ctx.output = analyze_sentiment(ctx.input)
    assert ctx.output == ctx.reference

Custom parameters:

@eval(dataset="math")
@parametrize("a,b,expected", [
    (2, 3, 5),
    (4, 7, 28),
])
def test_calculator(ctx: EvalContext, a, b, expected):
    ctx.input = {"a": a, "b": b}
    ctx.output = a + b
    assert ctx.output == expected

Cartesian product (stacked parametrize):

@eval(dataset="models")
@parametrize("model", ["gpt-4", "gpt-3.5"])
@parametrize("temperature", [0.0, 0.7, 1.0])
def test_model_grid(ctx: EvalContext, model, temperature):
    ctx.input = {"model": model, "temperature": temperature}
    ctx.output = run_model(model, temperature)
    assert ctx.output is not None

Reference

EvalResult schema

EvalContext automatically builds an EvalResult when the evaluation completes. You can also return EvalResult directly:

from ezvals import EvalResult

@eval(dataset="test")
def test_direct():
    return EvalResult(
        input="...",
        output="...",
        reference="...",      # optional
        latency=0.123,        # optional (auto-calculated if not provided)
        metadata={"model": "gpt-4"},  # optional
        run_data={"trace": [...]},     # optional
        scores=[{"key": "exact", "passed": True}],
    )

Score schema

{
    "key": "metric_name",    # required
    "value": 0.95,           # optional: numeric score
    "passed": True,          # optional: boolean pass/fail
    "notes": "...",          # optional: justification
}

Evaluators

Callables that add scores to results after execution:

def check_length(result):
    return {"key": "length", "passed": len(result.output) > 50}

@eval(input="Explain recursion", evaluators=[check_length], dataset="qa")
async def test_response(ctx: EvalContext):
    ctx.output = await my_agent(ctx.input)

CLI

# Run evals headlessly
ezvals run path/to/evals

# Run with web UI
ezvals serve path/to/evals

# Run specific function
ezvals run path/to/evals.py::function_name

Common flags:

-d, --dataset TEXT      Filter by dataset(s)
-l, --label TEXT        Filter by label(s)
-c, --concurrency INT   Number of concurrent evals
--timeout FLOAT         Global timeout in seconds
-v, --verbose           Show stdout from eval functions

Run flags:

-o, --output FILE       Save JSON summary
--visual                Show progress dots and results table
--no-save               Output JSON to stdout instead of saving

Serve flags:

--session TEXT          Session name to group runs
--run-name TEXT         Name for this run
--port INT              Port (default 8000)

Sessions and runs

Group related eval runs together:

# Named session and run
ezvals serve examples --session model-upgrade --run-name baseline

# Auto-generated friendly names (e.g., "swift-falcon")
ezvals serve examples

Results are saved to .ezvals/runs/ with the pattern {run_name}_{timestamp}.json.

Contributing

uv sync
uv run pytest -q
uv run ruff check ezvals tests

Demo:

uv run ezvals serve examples