ezvals 0.1.10

A lightweight, code-first evaluation framework for testing AI agents and LLM applications

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

Cases (cases=)

Generate multiple evals from one function with the cases= argument on @eval.

Cases are list-of-dict overrides for the same fields you can pass to @eval (plus id).

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

Custom case data:

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

Explicit grids:

@eval(
    dataset="models",
    cases=[
        {"input": {"model": "gpt-4", "temperature": 0.0}},
        {"input": {"model": "gpt-4", "temperature": 0.7}},
        {"input": {"model": "gpt-4", "temperature": 1.0}},
        {"input": {"model": "gpt-3.5", "temperature": 0.0}},
        {"input": {"model": "gpt-3.5", "temperature": 0.7}},
        {"input": {"model": "gpt-3.5", "temperature": 1.0}},
    ],
)
def test_model_grid(ctx: EvalContext):
    ctx.output = run_model(ctx.input["model"], ctx.input["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

Frontend Build Artifacts Policy

Generated UI assets in ezvals/static/ are intentionally not committed in normal development. Release packaging builds the UI and bundles these files into published artifacts so installed users can run ezvals serve without extra steps.

If you run from a git checkout and UI assets are missing:

cd ui
npm ci
npm run build

Serve flags:

--session TEXT          Session name to group runs
--run-name TEXT         Name for this run
--port INT              Port (default 8000)
--open / --no-open      Automatically open browser (default: open)

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.

Agent Skill

EZVals includes a skill that teaches AI coding agents how to write and analyze evals.

Install from package (version-matched)

ezvals skills add --claude

Install from marketplace (latest)

npx skills add camronh/evals-skill

Check installation

ezvals skills doctor

At least one target flag is required (--agents, --claude, --codex, --cursor, --windsurf, --kiro, --roo). EZVals installs to selected target directories and symlinks additional selected targets to a canonical source. If --agents is selected, .agents/skills/evals/ is canonical. Invoke with /evals in your AI coding agent.

Contributing

make setup-skills # link agent skills for claude and codex
uv run pytest -q
uv run ruff check ezvals tests

Demo:

uv run ezvals serve examples