DeepEval and Promptfoo are the two most widely adopted open-source frameworks for evaluating LLM outputs in CI pipelines. Both enable automated quality checks on model outputs, but they take different approaches: DeepEval integrates as pytest test cases with built-in LLM-powered metrics, while Promptfoo uses YAML configuration with a CLI-first approach and supports multi-provider comparison. This comparison helps you choose the right tool for your evaluation workflow.

Architecture

DeepEval is a Python library that integrates with pytest. You write evaluation tests as Python functions, define test cases with inputs and expected outputs, and run metrics that score the outputs. Results appear as pytest pass/fail outcomes with detailed metric scores.

from deepeval import assert_test
from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric
from deepeval.test_case import LLMTestCase

def test_rag_quality():
    test_case = LLMTestCase(
        input="What is the company's revenue?",
        actual_output="The company reported $10M in revenue.",
        retrieval_context=["Revenue was $10M in Q3 2025."]
    )
    relevancy = AnswerRelevancyMetric(threshold=0.7)
    faithfulness = FaithfulnessMetric(threshold=0.8)
    assert_test(test_case, [relevancy, faithfulness])

Promptfoo is a CLI tool configured via YAML. You define prompts, providers (model APIs), and test cases in a configuration file, then run promptfoo eval to execute all combinations and produce a results table.

prompts:
  - "Answer based on context: {{context}}\nQuestion: {{question}}"
providers:
  - openai:gpt-4o-mini
tests:
  - vars:
      question: "What is the revenue?"
      context: "Revenue was $10M in Q3 2025."
    assert:
      - type: llm-rubric
        value: "Answer mentions $10M revenue"
      - type: contains
        value: "$10M"

Metrics

DeepEval provides built-in metrics powered by LLMs:

  • Answer relevancy (does the answer address the question?)
  • Faithfulness (does the answer stay within the provided context?)
  • Hallucination detection
  • Contextual precision and recall
  • Bias and toxicity detection
  • Custom metrics via Python functions

Promptfoo supports assertion-based evaluation:

  • String matching (contains, equals, regex)
  • LLM-as-judge rubric evaluation
  • Similarity scoring (cosine, Levenshtein)
  • JSON schema validation
  • Custom JavaScript/Python assertion functions
  • Model-graded evaluations

Comparison: DeepEval’s built-in metrics are more sophisticated out of the box, especially for RAG evaluation (faithfulness and hallucination). Promptfoo is more flexible for custom assertions and supports a wider variety of simple checks without requiring LLM calls.

CI Integration

DeepEval integrates naturally with CI because it runs as pytest. Any CI system that runs pytest can run DeepEval tests. Results are standard pytest output. DeepEval also offers a cloud platform (Confident AI) for tracking results over time.

# GitHub Actions with DeepEval
- run: pytest tests/eval/ -v
  env:
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

Promptfoo runs as a CLI command and outputs results in multiple formats (table, JSON, CSV, HTML). CI integration requires calling promptfoo eval and checking the exit code.

# GitHub Actions with Promptfoo
- run: npx promptfoo eval --no-cache -o results.json
- run: npx promptfoo eval --output-format json | jq '.results.stats.failures == 0'

Comparison: DeepEval integrates more smoothly with Python CI workflows. Promptfoo works better in Node.js/TypeScript environments and for teams that prefer YAML configuration over Python code.

Multi-Provider Comparison

Promptfoo excels at comparing outputs across multiple models. Define several providers and see results side by side.

providers:
  - openai:gpt-4o
  - openai:gpt-4o-mini
  - anthropic:claude-sonnet-4-20250514

This is Promptfoo’s standout feature: quickly comparing model quality, cost, and latency across providers for the same test set.

DeepEval evaluates one output at a time. Comparing models requires writing separate test functions or parameterizing tests, which is possible but less ergonomic.

Winner: Promptfoo for model comparison workflows.

Prompt Development Workflow

Promptfoo includes a web UI for reviewing results visually, making it useful during prompt development iterations. You can see outputs from different prompts and models side by side, identify patterns, and refine prompts interactively.

DeepEval focuses on CI-time evaluation rather than interactive prompt development. It is better suited for automated quality gates than for exploratory prompt engineering.

Winner: Promptfoo for prompt development. DeepEval for automated quality gates.

Cost

Both are open source and free for local use. DeepEval’s LLM-powered metrics require API calls to evaluate (typically using GPT-4o or similar), adding cost per evaluation. Promptfoo’s string-based assertions are free; its LLM-rubric assertions also require API calls.

DeepEval offers Confident AI, a paid cloud platform for tracking evaluation results over time. Promptfoo offers a paid cloud option for team collaboration and result storage.

Recommendation

Choose DeepEval if your team works in Python, you need sophisticated RAG evaluation metrics (faithfulness, hallucination), and you want evaluation integrated into your pytest workflow as first-class test cases.

Choose Promptfoo if you need to compare multiple models or prompts, prefer YAML configuration over Python code, want a visual UI for reviewing results, or your team works primarily in JavaScript/TypeScript.

Use both if your workflow involves prompt development (Promptfoo for iteration and comparison) followed by CI quality gates (DeepEval for automated regression testing). The tools address different stages of the development lifecycle and complement each other well.