evalwire 0.6.0

Systematic evaluation of LangGraph nodes using Arize Phoenix experiments.

evalwire

Systematic, reproducible evaluation of any async callable — LangGraph nodes, plain Python functions, REST API endpoints, and more — tracked in Arize Phoenix.

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What it does

When iterating on any LLM-powered callable — a LangGraph node, a plain Python function, or a REST endpoint — it is hard to know whether a change improved or degraded behaviour. Running the full system end-to-end is expensive and makes it difficult to attribute a score change to a specific component.

evalwire solves this by:

• Turning a human-curated CSV of queries and expected outputs into versioned Arize Phoenix datasets.
• Letting you define a task — any async def callable — that isolates and invokes the component under test.
• Running those tasks against the stored datasets, scoring each output with one or more evaluators, and recording results in Phoenix — giving you a reproducible, comparable experiment per run.

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Installation

pip install evalwire
# With LangGraph node-isolation helpers:
pip install 'evalwire[langgraph]'
# With LLM-as-a-judge evaluator:
pip install 'evalwire[llm-judge]'
# Everything:
pip install 'evalwire[all]'

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Quick start

1. Upload your testset

evalwire upload --csv data/testset.csv

The CSV must contain a tags column whose values name the target Phoenix dataset (multiple tags can be pipe-delimited: es_search|source_router).

2. Structure your experiments

experiments/
├── es_search/
│   ├── task.py        # defines: async def task(example) -> Any
│   └── top_k.py       # defines: def top_k(output, expected) -> float
└── source_router/
    ├── task.py
    └── accuracy.py

3. Run experiments

evalwire run --experiments experiments/

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Built-in evaluators

All factories are importable from evalwire.evaluators and return a callable with signature (output, expected: dict) -> float | bool.

Factory	Returns	Use case
make_top_k_evaluator(K=20)	float	Position-weighted retrieval scoring
make_membership_evaluator()	bool	Classification / routing label check
make_exact_match_evaluator()	bool	Extractive QA, single ground-truth string
make_contains_evaluator()	bool	Free-text generation, required phrase present
make_regex_evaluator()	bool	Structured format validation (dates, IDs, …)
make_json_match_evaluator(keys)	float	Tool-call / structured-output key matching
make_schema_evaluator(schema)	bool	JSON Schema conformance
make_numeric_tolerance_evaluator(atol, rtol)	bool	Math / calculation tasks with tolerance
make_llm_judge_evaluator(model, prompt, schema)	float|bool	LLM-as-a-judge with structured output

Example

from evalwire.evaluators import make_top_k_evaluator, make_exact_match_evaluator

# Drop the factory return value into your experiment directory as the evaluator
top_k = make_top_k_evaluator(K=5)
exact = make_exact_match_evaluator()

LLM judge

from pydantic import BaseModel
from langchain.chat_models import init_chat_model
from evalwire.evaluators import make_llm_judge_evaluator

class Verdict(BaseModel):
    explanation: str
    score: bool  # True = correct

llm_judge = make_llm_judge_evaluator(
    model=init_chat_model("gpt-4o-mini"),
    prompt_template=(
        "Output: {output}\n"
        "Expected: {expected_output}\n"
        "Is the output correct? Think step by step, then set score."
    ),
    output_schema=Verdict,
)

Requires pip install 'evalwire[llm-judge]'.

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Node isolation

Use invoke_node to call a single LangGraph node without compiling a full graph:

from evalwire.langgraph import invoke_node

async def task(example) -> list[str]:
    result = await invoke_node(retrieve, example.input["user_query"], RAGState)
    return result["retrieved_titles"]

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CLI reference

Command	Description
evalwire upload --csv PATH	Upload CSV testset to Phoenix
evalwire run --experiments DIR	Discover and run all experiments
evalwire run --name NAME	Run a single named experiment
evalwire run --dry-run N	Run N examples without recording results
evalwire run --concurrency N	Run N experiments in parallel

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Configuration

Create evalwire.toml in your project root to avoid repeating flags:

[dataset]
csv_path = "data/testset.csv"
on_exist = "skip"

[experiments]
dir = "experiments"
prefix = "eval"
concurrency = 4

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Requirements

• Python >= 3.10
• arize-phoenix >= 13.0, < 14
• A running Phoenix instance (local or cloud)