Best open-source LLM eval framework

Combined ranking

1. 1
   DeepEval—13 pts

   GPT #3Claude #1Gemini #1

   Pytest-native testing ergonomics make LLM evals feel like unit tests, with the broadest metric library (G-Eval, hallucination, RAG triad, agentic/tool-use metrics), synthetic dataset generation, and painless CI/CD integration — the default choice for engineering teams.

   To stay #1 Decouple further from the Confident AI cloud upsell — a first-class, fully local experience for dashboards and regression tracking would remove the main adoption objection.

2. 2
   Promptfoo—12 pts

   GPT #1Claude #2Gemini #3

   Best all-around developer experience for local, provider-agnostic prompt, model, agent, RAG, regression, CI/CD, and red-team testing, with declarative configs and strong reporting

   To rank higher Add first-class support for reproducible sandboxed agent evaluations

3. 3
   RRagas—8 pts

   GPT #5Claude #3Gemini #2

   Serves as the industry benchmark for Retrieval-Augmented Generation evaluation, utilizing specialized metrics like faithfulness and context recall.

   To rank higher Standardize native evaluation metrics for agentic workflows and multi-step tool calls.

4. 4
   IInspect AI—6 pts

   GPT #2Claude #4Gemini —

   Most capable framework for rigorous frontier-model and agent evaluations, with composable tasks, tools, scorers, sandboxing, multi-agent support, rich transcripts, and 200-plus ready-made evaluations

   To rank higher Simplify setup and authoring for ordinary application teams

5. 5
   EEleutherAI LM Evaluation Harness—2 pts

   GPT #4Claude —Gemini —

   The research standard for reproducible model benchmarking, with a vast task catalog, configurable few-shot evaluation, broad local-model support, and strong community adoption

   To rank higher Modernize application and tool-using-agent evaluation beyond benchmark-centric workflows

6. 6
   PPhoenix—2 pts

   GPT —Claude —Gemini #4

   Combines OpenTelemetry-compliant tracing with evaluation, allowing developers to run evaluators on specific sub-spans of complex traces.

   To rank higher Simplify the installation footprint and local UI server startup time for lightweight development environments.

7. 7
   Arize Phoenix—1 pts

   GPT —Claude #5Gemini —

   Best fusion of OpenTelemetry-based tracing and evals — you debug the exact production traces you score, with solid LLM-as-judge templates and a genuinely self-hostable, notebook-friendly workflow.

   To rank higher Sharpen the standalone eval story — its eval library lives in the shadow of its observability platform, so teams wanting evals-only often look elsewhere first.

8. 8
   Llm-evaluation-harness—1 pts

   GPT —Claude —Gemini #5

   The de facto standard for evaluating raw foundation models on academic datasets with robust community-verified benchmarks.

   To rank higher Modernize the Python API to support runtime evaluation of live application endpoints rather than static model checkpoints.

By model

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled continuously