Skip to content

agenteval vs other tools

Tools for evaluating LLM applications overlap, but each starts from a different question. This is how agenteval relates to the better-known ones; check each project's own docs for current details.

agenteval Ragas DeepEval promptfoo
Starts from "is my agent reliable, and is this version better?" "is this RAG answer good?" "does my LLM app pass these tests?" "which prompt or model is best?"
Runs your agent yes, many times per question, with timeouts and concurrency no: scores records you give it partly, through its test runner yes, prompts against providers
Traces every step yes: model and tool calls, tokens, errors no through its tracing no
Failures labelled by cause yes: tool error, timeout, rate limit, quota... no no no
Metrics deterministic first (exact match, tool use, faithfulness, retrieval recall), LLM judge on top mostly LLM-based mostly LLM-based assertions, some LLM-based
Judge calibrated against your labels yes, with Cohen's kappa no no no
Says whether a change is real yes: paired McNemar test and confidence intervals no no side-by-side scores
Blocks a PR on a regression yes: gate and GitHub Action no through test failures through assertions
Interface CLI, Python, dashboard Python Python, cloud platform CLI, web viewer

When to use which

  • agenteval when an agent calls tools, runs in production, and you need to know why it fails and whether a change helps, not only how good one answer is.
  • Ragas when you want its catalogue of LLM-judged RAG metrics. They work together: agenteval export --format ragas turns a run into Ragas samples, and agenteval import reads Ragas samples back.
  • DeepEval when you want evaluations written as pytest-style unit tests with a hosted platform around them.
  • promptfoo when the job is comparing prompts and models across providers in a matrix.

What agenteval doesn't do

  • Hosting: it runs on your machine or your server; there is no managed service.
  • Hosted storage: results live in SQLite files; a team shares them by pushing to one agenteval server, not through a database server.
  • Frameworks with neither OpenTelemetry instrumentation nor an integration here: those are traced by wrapping their calls with llm_call and tool.