AI Agent Eval Tools Compared: Braintrust vs Promptfoo vs DeepEval

Last updated: August 22, 2026

Every team shipping an AI agent hits the same wall: the demo works, but you have no idea if the last prompt change made things better or worse. Evals are the fix, but the tooling landscape splits along a few real fault lines - offline vs online, hosted vs self-hosted, and how much you trust an LLM to grade another LLM. This is a fair look at where each tool actually fits.

Offline vs online evals#

Offline evals run against a fixed dataset before you ship, the same idea as a unit test suite. Online evals score live production traffic after you ship, since agents behave differently once real users start typing things you never scripted.

• Promptfoo is built offline-first: you define test cases in YAML, run them against candidate prompts/models, and get a pass/fail matrix. Its docs frame this explicitly as a CI-style test runner. Promptfoo is now part of OpenAI, and its README says it remains open source and MIT licensed; see who owns which tracing and eval tool now.
• DeepEval is also offline-first and pytest-native - you write assert_test() calls the way you'd write any other test, per its getting started guide.
• Braintrust and Langfuse cover both ends: offline eval runs against a dataset (Braintrust Evals docs, Langfuse Datasets docs), plus online scoring of live traces once the agent is deployed (Langfuse Evals docs).
• Ragas is dataset-driven and RAG-specific, scoring retrieval and generation quality against ground-truth or reference-free metrics per the Ragas metrics docs.
• OpenAI Evals is primarily offline (define a dataset and grading logic, run it against a model) but the hosted Evals API also supports continuous eval runs tied to stored completions.

LLM-as-judge#

Most of these tools use an LLM to grade outputs where exact-match scoring doesn't work - correctness, helpfulness, tone, faithfulness to a source document.

• Braintrust ships built-in "autoevaluators" (LLM-graded scorers) alongside code-based scorers, documented in its autoevals library.
• Langfuse lets you configure an LLM-as-judge evaluator directly in the UI or via SDK, per the model-based evaluations docs.
• DeepEval's metrics (answer relevancy, faithfulness, hallucination, etc.) are LLM-judge based by default, described in its metrics documentation.
• Ragas metrics like faithfulness and answer relevancy also rely on an LLM judge, per the Ragas metrics docs.
• Promptfoo supports LLM-graded assertions (llm-rubric) as one assertion type among many, documented in its llm-rubric assertion reference.
• OpenAI Evals supports model-graded evals as a first-class eval type, per the Evals API docs.

LLM-as-judge is convenient but non-deterministic - the same input can score differently across runs, which is why agent-evals-need-baseline-receipts argues for comparing against a stable baseline rather than trusting an absolute score in isolation.

CI integration#

• Promptfoo is designed to run as a CLI step in CI, with a documented GitHub Actions integration that comments pass/fail results on PRs.
• DeepEval runs as pytest, so it drops into any existing CI pipeline that already runs a Python test suite.
• Braintrust supports running evals from CI via its SDK and CLI, pushing results to its dashboard for diffing against previous runs, per the Braintrust CI guide.
• Langfuse's evals are typically triggered via SDK calls inside a pipeline or dataset run, per the Langfuse datasets guide; it does not ship a dedicated CI action the way Promptfoo does.
• Ragas evaluations are Python functions you call from a script or notebook, so CI integration means wiring them into your own pytest/CI job.
• OpenAI Evals runs are triggered via CLI or API and can be scripted into CI the same way, per the Evals API docs.

Dataset management#

• Braintrust and Langfuse both provide first-class dataset objects you can version, curate from production logs, and reuse across eval runs (Braintrust datasets, Langfuse datasets).
• Promptfoo defines test cases directly in its YAML config or CSV, which is lightweight but less suited to large, evolving datasets pulled from production.
• DeepEval and Ragas expect you to bring your own dataset (a list of dicts or a Hugging Face-style dataset) and don't include a managed dataset store.
• OpenAI Evals stores eval runs and can reference files uploaded via the Files API, per the Evals API docs.

Pick by workload#

• Already invested in Langfuse for tracing - use its built-in evals rather than adding a second tool; see langfuse-vs-braintrust-vs-helicone for the full observability-stack comparison.
• Need a fast CLI check in CI with minimal setup - Promptfoo.
• Team already writes pytest and wants evals as tests - DeepEval.
• Building or evaluating a RAG pipeline specifically - Ragas.
• Want a dedicated eval-first dashboard for regression tracking across releases - Braintrust.
• Already on the OpenAI stack and want evals tied directly to the API - OpenAI Evals.

None of these are mutually exclusive - it's common to run DeepEval or Promptfoo in CI as a fast gate, then use Braintrust or Langfuse for the longer-lived dataset and dashboard layer once the agent is live.

FAQ#

What's the difference between offline and online evals?#

Offline evals run a fixed test dataset against a candidate prompt or model before deployment, similar to unit tests. Online evals score live production traffic after deployment, catching regressions or edge cases real users hit that were never in the test dataset. Langfuse and Braintrust support both; Promptfoo and DeepEval are built primarily for the offline, pre-deploy case.

Is LLM-as-judge reliable enough to trust?#

It's useful for catching directional regressions but not deterministic - the same input can score differently across runs since it depends on the judge model's own variance. Most teams pair LLM-as-judge scores with deterministic checks (exact match, schema validation, code-based assertions) and compare against a stable baseline rather than trusting an absolute score alone.

Can I use more than one of these tools together?#

Yes, and it's common. A typical setup runs a lightweight offline framework like Promptfoo or DeepEval as a CI gate on every PR, then uses Braintrust or Langfuse for the ongoing dataset curation, dashboarding, and online eval layer once the agent is in production.

Do any of these tools require a hosted service?#

Promptfoo, DeepEval, Ragas, and OpenAI Evals (via the open-source repo) can all run entirely locally or in your own CI without a hosted dependency. Braintrust and Langfuse offer hosted dashboards, but Langfuse is open-source and can be self-hosted per its self-hosting docs.

Sources#

• Braintrust docs
• Braintrust Evals guide
• Braintrust datasets guide
• braintrustdata/braintrust-sdk on GitHub
• braintrustdata/autoevals on GitHub
• Langfuse model-based evals docs
• Langfuse datasets docs
• Langfuse self-hosting docs
• langfuse/langfuse on GitHub
• Promptfoo docs
• Promptfoo configuration guide
• Promptfoo llm-rubric assertion reference
• Promptfoo GitHub Actions integration
• promptfoo/promptfoo on GitHub
• DeepEval getting started
• DeepEval metrics introduction
• confident-ai/deepeval on GitHub
• Ragas docs
• Ragas metrics docs
• explodinggradients/ragas on GitHub
• openai/evals on GitHub
• OpenAI Evals API docs

Continue Reading#

• Langfuse vs Braintrust vs Helicone: Agent Tracing Tools in 2026 - full observability platforms that include eval capabilities
• Prompt Management Tools Compared - versioning and deploying prompts that get evaluated
• Best AI Agent Memory Providers in 2026 - memory layers that pair with eval workflows