evalprobe 0.1.1

Lightweight, reliable RAG evaluation for solo AI builders. Faithfulness, relevancy, correctness in 5 lines.

evalprobe

evalprobe

Lightweight, reliable RAG evaluation for solo AI builders. Faithfulness, relevancy, and correctness in 5 lines. Works with any LLM via litellm.

Try it in your browser →

Other RAG eval libraries are heavy, brittle, or focused on enterprise. evalprobe is the smallest thing that actually works.

Install

pip install evalprobe

Use it

from evalprobe import evaluate, EvalSample

sample = EvalSample(
    question="When did the Eiffel Tower open?",
    answer="It opened in 1889 and is 330 meters tall.",
    contexts=["The Eiffel Tower was completed in March 1889 for the World's Fair."],
    ground_truth="The Eiffel Tower opened on 31 March 1889.",
)

result = evaluate(sample, model="gpt-4o-mini")

for s in result.scores:
    print(f"{s.name}: {s.score:.2f}")

Output: faithfulness: 0.50 answer_relevancy: 1.00 answer_correctness: 0.67

The height claim got flagged as unfaithful because the context didn't mention it. That's the whole point.

CLI

Evaluate a JSONL file of samples and write results:

export OPENAI_API_KEY=sk-...

evalprobe eval samples.jsonl \
  --model gpt-4o-mini \
  --output results.jsonl

Each line of samples.jsonl is one sample:

{"question": "...", "answer": "...", "contexts": ["..."], "ground_truth": "..."}

You get a per-metric mean summary on stderr and full per-sample results in results.jsonl.

The metrics

• faithfulness — fraction of answer claims actually supported by the retrieved contexts. Catches hallucinations.
• answer_relevancy — how directly the answer addresses the question. Catches evasive or off-topic answers.
• answer_correctness — F1 score of facts in the answer vs. ground truth. Catches factual errors. Requires ground_truth.

All three return scores in [0.0, 1.0]. Higher is better.

Any LLM, any provider

evalprobe uses litellm under the hood, so any model name litellm understands works:

evaluate(sample, model="gpt-4o-mini")
evaluate(sample, model="groq/llama-3.3-70b-versatile")
evaluate(sample, model="anthropic/claude-3-5-sonnet-latest")
evaluate(sample, model="ollama/llama3.1")

Set the corresponding OPENAI_API_KEY, GROQ_API_KEY, ANTHROPIC_API_KEY, etc.

Why evalprobe

	evalprobe	ragas	langsmith
Install size	small	heavy	heavy
LLM provider	any (litellm)	partial	partial
Errors	clear messages	sometimes silent	n/a
Hosted dashboard	coming	no	yes (paid)
Built for	solo devs	research/teams	enterprise

Status

Pre-alpha. Things will change. Star to follow.

License

MIT