LLM evaluation tools compared for CI (2026)

There is no single best option among LLM evaluation tools; they're built for different jobs. Pick Ragas for RAG metrics, DeepEval for pytest-style tests with a large metric library, promptfoo for config-driven evals plus red teaming, LangSmith, Langfuse or Braintrust if you want evals inside an observability platform, and Drift-CI if you want a CI gate that compares every pull request against baselines committed to your repo.

Disclosure: Drift-CI is our product. We've tried to describe every tool from its own documentation, and to say plainly where the others are stronger.

How we compared these LLM evaluation tools

We checked each tool against its own README and documentation, as of November 2026. Each tool name below links to the docs we used. The columns:

• Best for: the job the tool's own docs emphasise.
• Open source: the license of the main repository, and whether that covers the platform or only an SDK.
• CI gating: whether the docs show a way to fail a CI job on eval results.
• Baseline comparison: whether results are compared with an earlier run or an approved baseline, rather than only with fixed thresholds.
• Hosted: whether there's a managed service.

Where we couldn't confirm something from the tool's own docs, the cell says "—". That means "not verified", not "doesn't exist".

The tools

promptfoo

promptfoo describes itself as "a CLI and library for evaluating and red-teaming LLM-based apps". Tests are declared in YAML, it compares models side by side across many providers, and its README says evals run locally. Its GitHub Action runs a before-and-after evaluation of edited prompts on each pull request and links to a web viewer, and its CI/CD guide shows quality gates through --fail-on-error and pass-rate thresholds. Promptfoo Enterprise is offered as hosted SaaS or on-premises. The README notes that promptfoo is now part of OpenAI and remains MIT licensed.

Choose it when you want a mature, general-purpose eval CLI, or when red teaming and security scanning matter. Its red-teaming coverage is far broader than anything Drift-CI does.

DeepEval

DeepEval calls itself "similar to Pytest but specialized for unit testing LLM apps". Its strength is the metric library: G-Eval and DAG judge metrics, agent metrics such as tool correctness and task completion, multi-turn conversation metrics, and RAG metrics including faithfulness and contextual recall. Tests run with deepeval test run, and the README says it integrates with any CI/CD environment. Confident AI, from the same team, is the hosted platform.

Choose it when your stack is Python and you want ready-made metrics for agents, conversations or RAG without writing your own judges.

Ragas

Ragas focuses on objective metrics for LLM applications, with a strong RAG lineage, and on generating test datasets when you don't have one. It's a Python library under Apache-2.0, now maintained at vibrantlabsai/ragas. We didn't find CI gating, baseline comparison or a hosted offering in its README, so those cells are "—".

Choose it when you're evaluating retrieval-augmented generation and need well-known RAG metrics or synthetic test data. For that job it's more specialised than Drift-CI, which has no retrieval-specific metrics.

LangSmith

LangSmith is LangChain's platform for tracing and evaluating LLM applications. Its pytest and Vitest/Jest integrations let you define evaluations as test cases and "raise assertion errors locally (e.g. in CI pipelines)". A comparison view highlights runs that regressed or improved against a source experiment. The SDKs are MIT licensed; the platform runs as a managed cloud service, with self-hosted deployment as an add-on to the Enterprise plan.

Choose it when you already trace production in LangSmith and want offline evals and experiments alongside those traces.

Braintrust

Braintrust is a hosted platform for evaluating and shipping AI products. You write evals in code with its SDKs (TypeScript, Python, Go) and scorers from the open-source autoevals library, then compare experiments in the platform. Its eval GitHub Action runs evals on pull requests and posts a summary comment showing each score's change, with counts of improvements and regressions. The SDKs are Apache-2.0 and autoevals is MIT.

Choose it when you want a polished hosted workflow for experiments and scoring, and you're happy for eval results to live in that platform.

Langfuse

Langfuse is an open-source platform for observability and evals: tracing, prompt management, LLM-as-a-judge and code evaluators, datasets, and cost and latency dashboards. Its experiments CI/CD integration runs experiments in GitHub Actions and fails the job when a score misses your threshold, and the guide shows how to compare against an approved baseline kept in your repository. It's MIT licensed outside its enterprise directories, available as Langfuse Cloud or self-hosted.

Choose it when you want open-source observability and evaluation in one place, especially if you plan to self-host. For production tracing it does far more than Drift-CI, which doesn't trace production traffic at all.

Drift-CI

Drift-CI is a CI gate for LLM behaviour. Cases live in .drift/suite.yaml, and each case's baseline is a JSON file committed to .drift/baseline/. On every pull request the GitHub Action scores each case, compares it with its baseline, posts one PR comment with the deltas, and fails the check when a case drops past your threshold. Accepting a change means committing the new baseline in the same PR, so it's reviewed like code. It supports seven providers and seven evaluators, from exact-match and JSON schema to LLM judges and rubric checklists. The CLI, the action and a self-hostable dashboard are MIT licensed; drift-ci.com adds a hosted dashboard with run history, plus issues and analytics on the Team plan.

Choose it when the main question is "did this pull request make our LLM feature worse?" and you want the answer as a failing check, with baselines in git rather than in a platform.

Where Drift-CI fits

Drift-CI is narrower than most tools here, by design. It isn't an observability platform, a prompt manager or a red-teaming suite. It does one job: decide on every pull request whether behaviour regressed, using baselines your team reviewed and committed. Two choices set it apart:

• Baselines are files in your repository, so a behaviour change and its approval land in the same diff.
• Provider errors are never regressions. Rate limits and timeouts get their own statuses, and a run with too many of them aborts instead of failing your PR with false regressions.

Drift-CI complements the other tools rather than replacing them: tracing in Langfuse or LangSmith, RAG metrics from Ragas, red teaming in promptfoo, and Drift-CI as the merge gate. The LLM regression testing guide explains the approach in detail, the GitHub Actions tutorial sets it up step by step, and prompt regression testing shows what the pull-request check looks like.