Open Source Evals Prompt Testing

## What this collection sets up

This collection turns prompt and agent behavior into a repeatable engineering
workflow. It covers fast prompt regression checks, Python-style evaluation
tests, RAG and agent metrics, trace-backed debugging, human review datasets, and
release gates that decide what happens when evals fail.

## Layers

### 1. Prompt and regression tests

- **promptfoo** gives teams prompt matrices, regression tests, and red-team
  checks.
- **deepeval** provides Python-first LLM unit tests and metrics.
- **agent-evals-regression-gate** helps define merge/release gates around eval
  suites.

### 2. RAG, agent, and optimization loops

- **ragas** focuses on RAG and LLM application evaluation.
- **dspy** helps teams program and optimize language-model pipelines with
  metrics and optimizers.
- **giskard** adds broader testing, scanning, and monitoring coverage.

### 3. Observability and review data

- **langfuse**, **trulens**, **mlflow**, and **agenta** capture traces, prompt
  versions, metrics, and experiment evidence.
- **label-studio** supports human review, annotation, benchmark curation, and
  preference data.

## Suggested order

Start with Promptfoo for fast prompt regression coverage. Add DeepEval or Ragas
for application-specific metrics, then wire traces into Langfuse, TruLens,
MLflow, or Agenta. Use Label Studio only after the team has written reviewer
instructions, sampling rules, and export boundaries.

## Source and references

- Promptfoo documentation: https://www.promptfoo.dev/docs
- DeepEval documentation: https://deepeval.com/docs/getting-started
- Ragas documentation: https://docs.ragas.io
- Langfuse documentation: https://langfuse.com/docs

## Duplicate check

Checked existing collections, upstream collection history, open collection PRs,
and repository content for `open-source-evals-prompt-testing`, open-source
evals, prompt testing collection, LLM regression testing, Promptfoo, DeepEval,
Ragas, and eval workflow. Existing collections cover code quality, production
readiness, API development, and data engineering, but none curates an
open-source LLM eval and prompt-testing lifecycle across prompt tests, metrics,
traces, human review, and release gates.

## Disclosure

Editorial collection. No paid placement or affiliate link is used.