{"slug": "promptfoo-vs-deepeval-which-llm-eval-framework-to-use-in-2026", "title": "Promptfoo vs DeepEval: which LLM eval framework to use in 2026", "summary": "Promptfoo and DeepEval remain the two most-used open-source LLM evaluation frameworks as of November 2026, with Promptfoo at version 0.125.1 under the MIT license and owned by OpenAI since its announced March 9, 2026 acquisition, and DeepEval at version 4.3.4 under Apache-2.0 from Confident AI. The comparison finds Promptfoo better suited to side-by-side prompt and model comparison, code-based assertions, built-in red teaming (157 plugins, 34 strategies) and non-Python teams, while DeepEval fits pytest-based workflows needing ready-made RAG, agent and conversation metrics at the cost of a judge model call for nearly every metric on every case. Both frameworks collect usage telemetry by default, and the author discloses building EvalShift, a separate eval tool, with all claims drawn from each project's docs, repository or pricing page read on November 13, 2026.", "body_md": "[← all posts](https://www.evalshift.dev/blog)\n\n# Promptfoo vs DeepEval: which LLM eval framework to use in 2026\n\nPromptfoo vs DeepEval compared on test format, judge costs, repeated runs, agent checks, red teaming, CI, pricing and telemetry, as of November 2026.\n\nPromptfoo and DeepEval are the two most-used open-source LLM eval frameworks, and they are built for different jobs. Choose Promptfoo when you are comparing prompts or models side by side, when most of your checks can be written as code, when you need red teaming, or when your team is not on Python. Choose DeepEval when your tests live in pytest and you want a ready-made metric for RAG, agents or conversations, and you accept a judge model call for almost every metric on every case.\n\nAs of November 2026, Promptfoo is at 0.125.1 under the MIT license and has been part of OpenAI since March. DeepEval is at 4.3.4 under Apache-2.0 and is built by Confident AI, which sells the hosted platform around it. Both collect usage telemetry by default.\n\nThis post compares them on how a test is written, what a run costs, how each compares two models, what each does with repeated runs, agent checks, red teaming, CI, pricing and what leaves your machine.\n\nA disclosure: I build EvalShift, an eval tool for a narrower job than either of these, and it appears only in the section on what both leave to you. Every claim about Promptfoo or DeepEval comes from that project's docs, repository or pricing page, read on November 13, 2026.\n\n## Promptfoo vs DeepEval at a glance\n\n|  | Promptfoo | DeepEval | \n|---|---|---|\n| License | MIT | Apache-2.0 | \n| Owner | OpenAI, since the acquisition announced March 9, 2026 | Confident AI | \n| Runs on | Node 22.22+, through npm, npx or Homebrew | Python 3.10+, through pip; a TypeScript package on npm | \n| Latest release | 0.125.1, October 25, 2026 | 4.3.4, November 11, 2026 | \n| GitHub stars | 27.1k | 19.5k | \n| Downloads | 1.2M a week on npm | 3.3M a month on PyPI | \n| A test is | A YAML row of variables and assertions, run against every prompt and provider | A pytest function with a test case and metrics | \n| Checks | About 40 deterministic assertion types and 15 model-graded | 50+ metrics, almost all graded by an LLM judge | \n| Comparing two models | Built in: a matrix and a local web viewer | A judge picks a winner; run comparison is on Confident AI | \n| Repeated runs | `--repeat` , with a Wilson 95% interval per repeated test | `--repeat` , and`flaky=True` to stop a test blocking | \n| Agent checks | Deterministic trajectory assertions on traces | Judged trajectory metrics, plus a deterministic tool check | \n| Red teaming | Built in: 157 plugins, 34 strategies | A separate package, DeepTeam | \n| CI | Exit code, pass-rate threshold, official GitHub Action | pytest exit code | \n| Hosted side | Enterprise, custom price | Confident AI: free, $200 or $2,000 a month | \n| Usage telemetry | On by default | On by default | \n\nSources: [Promptfoo on GitHub ↗](https://github.com/promptfoo/promptfoo),\n[Promptfoo on npm ↗](https://www.npmjs.com/package/promptfoo),\n[DeepEval on GitHub ↗](https://github.com/confident-ai/deepeval),\n[DeepEval on PyPI ↗](https://pypi.org/project/deepeval/), and the docs pages linked in each section\nbelow.\n\n## The same test in each\n\nOne test, written twice: a support reply must repeat the order number and must not promise a delivery date.\n\nIn Promptfoo it is a config file. `promptfoo eval` runs every prompt against every provider and\nevery test case:\n\n```\n# promptfooconfig.yaml\nprompts:\n  - 'Reply to this support message in one sentence: {{message}}'\nproviders:\n  - openai:gpt-6-luna\n  - anthropic:messages:claude-sonnet-5-5\ntests:\n  - vars:\n      message: Where is my order 4417?\n    assert:\n      - type: icontains\n        value: '4417'\n      - type: llm-rubric\n        value: Does not promise a delivery date\nnpx promptfoo@latest eval\n```\n\nIn DeepEval it is a pytest file. You call your own code, build a test case from the result and attach metrics:\n\n``` python\n# test_support.py\nimport pytest\nfrom deepeval import assert_test\nfrom deepeval.metrics import GEval\nfrom deepeval.test_case import LLMTestCase, SingleTurnParams\n\nfrom app import reply  # your function that calls the model\n\nno_promise = GEval(\n    name=\"No delivery promise\",\n    criteria=\"The actual output does not promise a delivery date.\",\n    evaluation_params=[SingleTurnParams.ACTUAL_OUTPUT],\n    threshold=0.5,\n)\n\n@pytest.mark.parametrize(\"message, order_id\", [(\"Where is my order 4417?\", \"4417\")])\ndef test_reply(message, order_id):\n    output = reply(message)\n    assert order_id in output\n    assert_test(LLMTestCase(input=message, actual_output=output), [no_promise])\ndeepeval test run test_support.py\n```\n\nThree differences show up even in a test this small:\n\n- +**Who calls the model.** Promptfoo calls it for you from the`providers` list, or calls your\napplication through a script or HTTP provider. A DeepEval test calls your code directly, like\nany other pytest test.\n- +**The matrix.** Adding a second model in Promptfoo is one line, and every test runs against\nboth. In DeepEval you parametrize the test over models yourself.\n- +**The deterministic check.** In Promptfoo it is an assertion type. In DeepEval it is a plain\nPython`assert` , because the metric library is for the judged part.\n\nIf you copy DeepEval code from an older post, note one rename: `LLMTestCaseParams` is deprecated in\nfavour of `SingleTurnParams`. The old name still imports, with a warning.\n\n## Checks: assertions against metrics, and what a run costs\n\nThis is the largest practical difference between the two.\n\nPromptfoo's [assertion list ↗](https://www.promptfoo.dev/docs/configuration/expected-outputs/) is\nmostly code. About 40 types run without a model: `contains`, `regex`, `is-json`, `is-sql`,\n`javascript`, `python`, text-overlap scores such as ROUGE and BLEU, `latency`, `cost`, and checks\non tool calls. About 15 more are model-graded, including `llm-rubric`, `g-eval`, `factuality` and\nthe RAG checks `context-faithfulness`, `context-recall` and `answer-relevance`. A suite can be\nentirely deterministic, and then the only model you pay for is the one under test.\n\nDeepEval ships [more than 50 metrics ↗](https://deepeval.com/docs/metrics-introduction), and its\nown metrics page says almost all of them are LLM-as-a-judge. The deterministic ones are Exact\nMatch, Pattern Match, JSON Correctness and\n[Tool Correctness ↗](https://deepeval.com/docs/metrics-tool-correctness), which compares the tools\ncalled with the tools expected and brings in a model only if you pass a list of available tools.\nEvery metric has a default threshold of 0.5 and returns a score from 0 to 1 with a reason.\n\nSo the arithmetic for a DeepEval suite is simple. 200 cases with three judged metrics is at least\n600 judge calls per run, on top of the 200 calls to your own application. Run each case three\ntimes and it is 1,800. The same checks in Promptfoo cost the same if you write them as\n`llm-rubric`, and nothing if they can be a `regex`, a JSON schema or a few lines of Python.\n\nTwo things about the judge are worth knowing:\n\n- +**The default judge.** Both use OpenAI when you set nothing else. DeepEval defaults to an\nOpenAI model and can be pointed at another provider or at Ollama. Promptfoo picks its grader\nfrom whichever credentials are in the environment, and OpenAI wins when both an OpenAI and an\nAnthropic key are set.\n- +**Jev.** In September 2026 DeepEval added[eval modes ↗](https://deepeval.com/docs/evaluation-eval-modes) . In`system_one` mode a metric is\ndecided by Jev, a model from TypeSafe AI that the docs call System One, with no LLM call. It\nstill needs a TypeSafe API key, so your test cases go to another vendor in place of the judge\nprovider.\n\nA ready-made metric saves you writing a rubric. It does not save you validating it: a judged\nmetric with a 0.5 threshold is a classifier you have not measured yet.\n[When to trust an LLM judge](https://www.evalshift.dev/blog/when-to-trust-an-llm-judge) covers how to check one against\nhand labels.\n\n## Comparing two prompts or models\n\nPromptfoo was built for this. List two providers, or two prompts, and one eval runs the full\nmatrix. `promptfoo view` opens a [local web viewer ↗](https://www.promptfoo.dev/docs/usage/web-ui/)\nwith the outputs side by side, a filter for rows where they differ, a comparison against an\nearlier eval and charts of pass rate per column. The `select-best` and `max-score` assertions pick\na winner per test case. All of it is in the open-source CLI.\n\nDeepEval's open-source answer is\n[Arena G-Eval ↗](https://deepeval.com/docs/metrics-arena-g-eval): you give it the outputs of several\ncontestants and a blinded LLM judge picks one. Test runs are saved locally as JSON, which the docs\nsuggest diffing in Git. A side-by-side comparison of two runs, with regressions marked, is a\nConfident AI feature and needs an API key.\n\nNeither tool tests whether one model is better than the other by more than chance. Promptfoo\nscores each column on its own. Confident AI's\n[changelog ↗](https://www.confident-ai.com/docs/changelog/2026/7/3) says run comparisons have\nincluded statistical significance since July 2026, and its docs do not name the test.\n\n## Repeated runs and flaky tests\n\nLLM outputs vary between runs, so a case that passes once has not been shown to pass. The two tools handle this differently, and Promptfoo moved ahead here in October 2026.\n\n**Promptfoo.** `--repeat 3` runs every test three times. Since 0.125.0 the summary\n[flags tests that both passed and failed ↗](https://www.promptfoo.dev/docs/guides/evaluate-llm-temperature/)\nacross repeats and reports a Wilson 95% confidence interval on each repeated test's pass rate. The\ncatch is the cache: responses are\n[cached on disk by default ↗](https://www.promptfoo.dev/docs/configuration/caching/) for 14 days,\nand each repeat has its own cache entry. Run the same eval twice and the second run replays the\nfirst, so use `--no-cache` when you are measuring variation. The interval is only reported for\nuncached results.\n\n**DeepEval.** `deepeval test run -r 3` repeats each test case. There is no interval in its docs.\nWhat it added, in July 2026, is `flaky=True` on a test case or a metric: the result is still\nrecorded, and a failure prints a warning and no longer fails the run.\n\nKeep the size of the interval in mind before reading much into three repeats. Three passes out of three gives a Wilson interval of 44% to 100%. Ten out of ten gives 72% to 100%. A repeat count you can afford on every pull request tells you which cases flip often, and little more.\n\n## Agents and tool calls\n\nBoth check more than the final reply, from opposite directions.\n\n**Promptfoo** checks tool calls with code. On the output: `is-valid-openai-tools-call` and\n`tool-call-f1`. On a trace: `trajectory:tool-used`, `trajectory:tool-args-match`,\n`trajectory:tool-sequence` and `trajectory:step-count`, with `trajectory:goal-success` as the one\njudged check. The trajectory assertions read\n[OpenTelemetry traces ↗](https://www.promptfoo.dev/docs/tracing/), which Promptfoo receives on a\nbuilt-in OTLP endpoint, so your agent has to be instrumented. For multi-turn tests there is a\nsimulated-user provider. By default that user is generated by Promptfoo's hosted models.\n\n**DeepEval** instruments your code with an `@observe` decorator and\n[scores the trace ↗](https://deepeval.com/docs/evaluation-trajectory-based-llm-evals) with Task\nCompletion, Step Efficiency, Plan Adherence and Plan Quality. All four are judged. Tool\nCorrectness is the deterministic one; Argument Correctness is judged and takes no reference. A\n`ConversationSimulator` plays the user in multi-turn tests with your own judge model, and there\nare metrics for MCP use.\n\nIf the question is \"did the agent call `refund` after `lookup_order`, with this order id\",\nPromptfoo's deterministic assertions answer it without a judge. If the question is \"was the plan\nsensible\", DeepEval has a metric ready. [Evaluating agent tool\ncalls](https://www.evalshift.dev/blog/evaluating-agent-tool-calls) goes through which of these checks catch which failures.\n\n## Red teaming: built in, or a second package\n\n**Promptfoo** has red teaming in the same CLI. Its docs list\n[157 plugins ↗](https://www.promptfoo.dev/docs/red-team/plugins/) that generate adversarial inputs\nand [34 strategies ↗](https://www.promptfoo.dev/docs/red-team/strategies/) that deliver them. The\nfree Community edition allows 10,000 probes a month.\n\nRead the [data-handling page ↗](https://www.promptfoo.dev/docs/red-team/troubleshooting/data-handling/)\nbefore you run it against anything sensitive. Your target is always called from your machine.\nGenerating and grading the attacks is another matter:\n\n- +Without an OpenAI key, generation and grading go to Promptfoo's API, along with your application's stated purpose, the prompts sent to your target, its responses and your email address.\n- +With your own key, most of it runs through your account. But plugins and strategies marked as remote in the docs always use Promptfoo's hosted inference. That includes 11 of the 34 strategies, among them the multi-turn GOAT and Hydra attacks.\n- +`PROMPTFOO_DISABLE_REDTEAM_REMOTE_GENERATION=true` turns remote generation off. The[docs ↗](https://www.promptfoo.dev/docs/red-team/configuration/) warn that local generation\nquality \"is generally low for most models\".\n\n**DeepEval's** red teaming moved to a separate package,\n[DeepTeam ↗](https://github.com/confident-ai/deepteam): Apache-2.0, version 1.0.10 from October 16,\n2026. Its README claims more than 50 vulnerabilities and more than 20 attack methods, and it runs\nthe attacks with your own model, with no hosted generation step. It releases less often than\nDeepEval, four times since July 2026.\n\nPromptfoo's red teaming is the larger and more developed of the two. DeepTeam is the choice when the attack traffic must not leave your own model accounts.\n\n## CI\n\n**Promptfoo** exits with\n[code 100 ↗](https://www.promptfoo.dev/docs/usage/command-line/) when a test fails or the pass rate\nis below `PROMPTFOO_PASS_RATE_THRESHOLD`, and writes JUnit XML, JSON, CSV or HTML. The official\n[`promptfoo-action` ↗](https://github.com/promptfoo/promptfoo-action) posts a summary comment with\npass and fail counts on a pull request, and has inputs for a pass threshold, a repeat count and a\nminimum number of passes per repeated test. Its README is explicit that it evaluates only the\ncurrent checkout. It does not run the base branch for you.\n\n**DeepEval** in CI is pytest. A failed `assert_test` fails the test and the job, and the\n[docs ↗](https://deepeval.com/docs/evaluation-unit-testing-in-ci-cd) show a plain workflow that runs\n`deepeval test run`. There is no pass-rate threshold flag. Gating on a policy, with checks posted\nto the pull request, runs through Confident AI.\n\nEither way, a gate that requires every judged case to pass on every run will fail on noise.\n[LLM regression testing in CI](https://www.evalshift.dev/blog/llm-regression-testing-in-ci) covers what to gate on instead.\n\n## What is open, what is paid, and who owns each\n\n**Promptfoo.** The CLI, assertions, red teaming and local viewer are MIT. OpenAI\n[announced the acquisition ↗](https://openai.com/index/openai-to-acquire-promptfoo/) on March 9,\n2026, and Promptfoo's [own post ↗](https://www.promptfoo.dev/blog/promptfoo-joining-openai/) said it\nwould remain open source and keep supporting a range of providers. Eight months later the README\nsays the same and names the MIT license, the license file is unchanged, and releases\ncontinue, eight of them in the last three months. The\n[paid side ↗](https://www.promptfoo.dev/pricing/) is Enterprise and On-Premise, both priced on\nrequest, adding SSO, role-based access, dashboards and continuous monitoring. The open-source\nserver can be self-hosted, but the\n[docs ↗](https://www.promptfoo.dev/docs/usage/self-hosting/) say that is not recommended for\nproduction: it stores results in SQLite and has no built-in authentication.\n\n**DeepEval.** The metric library and test runner are Apache-2.0. Run history, comparison,\nproduction tracing and dataset management are [Confident AI ↗](https://www.confident-ai.com/pricing),\nwhich is proprietary and bills per organization: a free plan with 2 seats, one project and 5 test\nruns a week; Starter at $200 a month with unlimited seats; Team at $2,000 a month. Self-hosting\nis on Enterprise. Uploads start only after `deepeval login`.\n\nThe practical difference: Promptfoo gives you a usable local viewer for free and puts team features behind a sales call. DeepEval gives you terminal output for free and a published price for the dashboard.\n\n## What each sends by default\n\n|  | Promptfoo | DeepEval | \n|---|---|---|\n| Usage telemetry | On by default | On by default, to PostHog | \n| What it records | Commands and assertion types, version, CI flag; user ID and email if you are logged in | Event and metric names, an anonymous ID, public IP address | \n| Turn it off | `PROMPTFOO_DISABLE_TELEMETRY=1` | `DEEPEVAL_TELEMETRY_OPT_OUT=1` | \n| Judged checks | Output and rubric go to the grader provider | Test case fields go to the judge provider | \n| Results upload | After `promptfoo share` | After `deepeval login` | \n\nBoth projects say telemetry excludes prompts and outputs:\n[Promptfoo telemetry ↗](https://www.promptfoo.dev/docs/configuration/telemetry/),\n[DeepEval data privacy ↗](https://deepeval.com/docs/data-privacy). The larger flow is the judge. If\nyour test cases come from production, every judged check sends customer data to a model provider,\nin both tools.\n\n## What users complain about\n\nThese are reports from each project's issue tracker, not my findings, and some may be fixed by the time you read this.\n\nPromptfoo:\n\n- +An [open issue ↗](https://github.com/promptfoo/promptfoo/issues/9968) reports one request still\ngoing out with telemetry disabled.\n- +A [red-team scan in CI ↗](https://github.com/promptfoo/promptfoo/issues/8276) stopped at an email\nverification prompt, and a strategy refused to run with remote generation off.\n- +A [request open since October 2025 ↗](https://github.com/promptfoo/promptfoo/issues/5947) asks for\na consistency metric over repeats: the share of cases that pass on all N attempts.\n\nDeepEval:\n\n- +An [issue open since March 2025 ↗](https://github.com/confident-ai/deepeval/issues/1417) , with 16\nupvotes, asks for a way to stop the console output promoting Confident AI.\n- +Smaller and local judge models return\n[JSON the metrics cannot parse ↗](https://github.com/confident-ai/deepeval/issues/929) .\n- +The pytest plugin [loads for every pytest run ↗](https://github.com/confident-ai/deepeval/issues/1419) in the environment, which makes telemetry harder to disable from code.\n\n## Using both\n\nPlenty of teams do, and the split is natural: DeepEval metrics in the pytest suite that runs on every pull request, and Promptfoo for red teaming before a release and for lining up candidate models when one is being chosen. They share nothing but your provider keys.\n\nThe cost of running both is two config formats and two sets of results that do not know about each other. If you only want one, pick by where most of your checks fall: deterministic and comparative, Promptfoo; judged and Python-native, DeepEval.\n\n## What both leave to you\n\nPromptfoo and DeepEval answer \"does this output pass my checks?\" The question a model upgrade raises is different: on the same cases, does the new model behave differently from the old one, and is the difference real?\n\nPromptfoo gets closest. Its matrix puts the two models side by side, and since October it tells you how stable each repeated test is. What it does not do is pair the two columns: take each case, compute the difference between the old model and the new one, and test whether those differences add up to more than noise. DeepEval scores one run at a time and leaves comparison to Confident AI.\n\nThat paired comparison is the job [EvalShift](https://www.evalshift.dev/docs) does. It runs your recorded production cases\nthrough both models, scores tool selection, arguments and trace rules on both sides, and tests\neach per-example difference with a paired t-test or Wilcoxon, an effect size with a confidence\ninterval and a false-discovery-rate correction across the run. A\n[migration policy](https://www.evalshift.dev/docs/migration-policy) turns the result into a verdict that passes or fails\nthe pull request.\n\nIn [one published run](https://www.evalshift.dev/blog/cheaper-faster-and-still-a-fail), the candidate model was 58.8%\ncheaper and 75.2% faster with no failed calls, and the verdict was FAIL: it picked a different\ntool on 25% of comparisons against a 10% limit.\n\nWhat EvalShift does not do: red teaming, a metric library the size of DeepEval's, or a matrix of\nfive models. The CLI is free and open source, and it runs beside either tool.\n[EvalShift vs Promptfoo](https://www.evalshift.dev/compare/evalshift-vs-promptfoo) and\n[EvalShift vs DeepEval](https://www.evalshift.dev/compare/evalshift-vs-deepeval) go dimension by dimension.\n\n## How to choose\n\n| If you | Use | \n|---|---|\n| Are picking between models or prompts | Promptfoo | \n| Can write most checks as code | Promptfoo | \n| Need red teaming with the widest attack coverage | Promptfoo | \n| Need red teaming that stays inside your own model accounts | DeepTeam | \n| Have no Python in the stack | Promptfoo, or DeepEval's TypeScript package if you want its metrics | \n| Keep tests in pytest and want them to stay there | DeepEval | \n| Want RAG, conversation or agent metrics without writing rubrics | DeepEval | \n| Want a hosted dashboard with a published price | DeepEval with Confident AI | \n| Need an eval tool not owned by a model provider | DeepEval | \n| Are swapping the model under a production agent | EvalShift, beside either | \n\nThen write the same ten test cases in both. It takes an afternoon, and the one whose failures you find easier to read is the one your team will keep running.\n\n## FAQ\n\n**Is Promptfoo or DeepEval better?** Neither is better overall. Promptfoo is better for comparing\nprompts and models side by side, for deterministic checks and for red teaming. DeepEval is better\nfor teams that keep tests in pytest and want ready-made judged metrics for RAG, agents and\nconversations. Many teams run both.\n\n**What is the difference between Promptfoo and DeepEval?** Promptfoo is a Node command-line tool\nconfigured in YAML that runs every prompt against every model and checks outputs with about 40\ndeterministic and 15 model-graded assertion types. DeepEval is a Python library that runs evals as\npytest tests with more than 50 metrics, almost all of them graded by an LLM judge.\n\n**Can you use Promptfoo and DeepEval together?** Yes. A common split is DeepEval metrics in the\npytest suite on every pull request, and Promptfoo for red teaming and for comparing candidate\nmodels. They share nothing but provider keys and run as separate CI jobs.\n\n**Which is better for red teaming, Promptfoo or DeepEval?** Promptfoo has the larger feature: 157\nplugins and 34 strategies in the same CLI, with 10,000 probes a month free. Many of them use\nPromptfoo's hosted inference. DeepEval's red teaming is a separate Apache-2.0 package, DeepTeam,\nwhich runs attacks with your own model.\n\n**Are Promptfoo and DeepEval free?** Both frameworks are free and open source: Promptfoo under\nMIT, DeepEval under Apache-2.0. You pay the model provider for every call, including judge calls.\nPromptfoo Enterprise is priced on request. Confident AI, the platform for DeepEval, has a free\nplan, then $200 and $2,000 a month per organization.\n\n**Does DeepEval support TypeScript?** Yes. There is a `deepeval` package on npm, built from the\nsame repository, that runs on Vitest. It is younger than the Python library, at version 0.9 as of\nNovember 2026. Promptfoo is a Node tool and works with any language through its config file.\n\n**Is Promptfoo still open source now that OpenAI owns it?** Yes. OpenAI announced the acquisition\non March 9, 2026. As of November 2026 the repository is MIT licensed, the license file is\nunchanged, and the project is still releasing, with 0.125.1 on October 25, 2026.\n\n**Do Promptfoo and DeepEval test statistical significance?** Not between two models. Since version\n0.125.0 Promptfoo reports a Wilson 95% confidence interval on the pass rate of each repeated test.\nDeepEval's docs describe no interval or test. Confident AI's changelog says its run comparisons\ninclude statistical significance, without naming the test.\n\n## Keep reading\n\n- +[The best open-source LLM evaluation frameworks in 2026](https://www.evalshift.dev/blog/open-source-llm-evaluation-frameworks) :\nPromptfoo and DeepEval next to Inspect AI, Pydantic Evals, Ragas and five more.\n- +[Promptfoo alternatives](https://www.evalshift.dev/blog/promptfoo-alternatives) : what to use for evals, red teaming and\nrun history if Promptfoo is not the fit.\n- +[LLM testing](https://www.evalshift.dev/blog/llm-testing) : the six steps of testing an LLM application, whichever\nframework runs them.\n- +[When to trust an LLM judge](https://www.evalshift.dev/blog/when-to-trust-an-llm-judge) : validating the judged metrics\nboth tools rely on.", "url": "https://wpnews.pro/news/promptfoo-vs-deepeval-which-llm-eval-framework-to-use-in-2026", "canonical_source": "https://www.evalshift.dev/blog/promptfoo-vs-deepeval", "published_at": "2026-11-13 18:53:02.070218+00:00", "updated_at": "2026-11-13 18:53:04.838368+00:00", "lang": "en", "topics": ["ai-tools", "large-language-models", "ai-agents", "developer-tools", "mlops"], "entities": ["Promptfoo", "DeepEval", "OpenAI", "Confident AI", "EvalShift", "GitHub", "PyPI"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/promptfoo-vs-deepeval-which-llm-eval-framework-to-use-in-2026", "markdown": "https://wpnews.pro/news/promptfoo-vs-deepeval-which-llm-eval-framework-to-use-in-2026.md", "text": "https://wpnews.pro/news/promptfoo-vs-deepeval-which-llm-eval-framework-to-use-in-2026.txt", "jsonld": "https://wpnews.pro/news/promptfoo-vs-deepeval-which-llm-eval-framework-to-use-in-2026.jsonld"}}