evalwire 0.2.1

Systematic evaluation of LangGraph nodes using Arize Phoenix experiments.

evalwire

Systematic, reproducible evaluation of LangGraph nodes and subgraphs against human-curated testsets, tracked in Arize Phoenix.

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What it does

When iterating on a LangGraph agent, it is hard to know whether a change to a specific node improved or degraded its behaviour. Running the full graph 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 that isolates and invokes individual LangGraph nodes independently of the rest of the graph.
• 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]'

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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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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)