Best AI eval tools for CI/CD in 2026
The best AI eval tools for CI/CD catch prompt, model, retrieval, and agent regressions before deploy. Compare Currai, Promptfoo, Braintrust, Langfuse, and Phoenix.
TL;DR: AI evals belong in CI/CD, but the best setup depends on what you are testing. Currai is the best fit when CI quality gates need to stay connected to production traces, prompt versions, sessions, tool calls, cost, and latency. Promptfoo is strong for code-first evals and red teaming. Braintrust is strong for experiment-centric eval workflows. Langfuse fits teams that want open-source observability plus evals. Phoenix fits teams standardizing on OpenTelemetry and Python-first evaluation workflows.
The goal is not to add one more flaky CI job. The goal is to know whether a prompt change, model swap, retrieval change, tool update, or agent scaffold change made the product better before users find the regression.
What AI evals in CI/CD should do
Traditional CI answers questions like "do the tests pass?" and "does the app build?" AI CI has to answer a fuzzier question: "did the behavior get worse?"
That means an AI eval pipeline should be able to:
- Run a stable set of eval cases on every pull request.
- Score outputs with deterministic checks, LLM judges, or human-calibrated rubrics.
- Compare new results against a baseline.
- Fail the build when quality drops below a threshold.
- Preserve enough context to debug the failure.
- Track latency, token usage, cost, model, prompt version, and experiment metadata.
- Turn production failures into future regression tests.
The last two requirements are where many eval setups fall short. A pass/fail number in CI is useful, but it is not enough. When an eval fails, the team needs the trace, input, output, retrieval context, tool calls, and prompt version that created the failure.
What to look for in an AI eval tool for CI/CD
Local and CI execution matters. Engineers should be able to run the same eval suite before pushing and inside GitHub Actions, GitLab CI, CircleCI, Jenkins, Buildkite, or whatever pipeline the team already uses.
Quality gates should be explicit. A good tool lets you fail a build on regressions, low pass rate, policy failures, invalid format, bad judge scores, cost spikes, or latency budgets.
Evaluator flexibility is mandatory. Keyword checks are not enough. Production LLM apps need code-based checks, LLM-as-judge rubrics, semantic similarity, retrieval metrics, tool-call validation, human review, and custom scorers.
Debuggability is the difference between a useful gate and a noisy one. When a run fails, you should see the exact input, output, model, prompt version, retrieval result, tool span, token usage, latency, and error.
Production trace reuse keeps evals from going stale. Static test sets catch known risks. Production traces catch the inputs your users actually create.
Experiment comparison helps teams decide whether a change improved behavior, not just whether it passed a threshold. You want to compare prompt versions, model choices, routing policies, or retrieval settings side by side.
Security and privacy controls matter because eval data often contains user inputs, internal documents, tool outputs, or PII. Look for redaction, environment separation, self-hosting where required, retention controls, and clear access boundaries.
1. Currai: best for CI/CD evals connected to production traces
Currai is built around the trace as the unit of AI quality. A trace can contain the full request: generations, spans, tool calls, retrieval steps, prompt versions, model parameters, usage, cost, latency, session, user, tags, and final output.
That makes Currai a strong fit when CI/CD evals need more than a local dataset. You can run checks on curated regression cases, then connect those results to the same trace model you use in production. Failed production traces can become new eval cases, and failed CI evals can be debugged with the same structure your team uses after launch.
Currai is especially useful for teams that ship prompt and agent changes often:
- Prompt versions can be attached to generations.
- A/B test metadata can travel with traced outputs.
- Evals can run on real production traces for a prompt, model, feature, or experiment.
- Tool calls, retrieval, MCP actions, and custom workflow steps can be recorded as spans.
- Token usage, latency, and cost roll up to the trace, so quality gates can include operational metrics.
The practical workflow is simple: trace the AI feature, promote failures into an eval suite, run the suite in CI, and use production traces to keep the suite fresh.
Best for
Teams that want evals, prompt experiments, agent traces, cost, latency, sessions, and production debugging in one workflow.
Pros
- Production traces become eval datasets without rebuilding context by hand.
- Prompt versions and experiment arms stay attached to actual generations.
- Trace-level cost and latency make CI gates more realistic than output-only scoring.
- Works for single prompts, RAG, multi-turn chat, tool-using agents, and stateful agent runs.
- The same trace view helps debug failed CI checks and failed production runs.
Trade-off
Currai is strongest when you instrument the app with traces, generations, and spans. Teams looking only for a standalone YAML test runner may prefer a code-first tool.
2. Promptfoo: best for code-first evals and security testing
Promptfoo is a strong choice for teams that want evals defined close to the repository. It supports CI/CD workflows for prompt quality testing and security scanning, including quality gates, JSON/HTML/JUnit output, GitHub Actions, GitLab CI, Jenkins, and other pipeline environments.
Promptfoo works well when your team wants eval cases in config files and wants to run them locally or in CI without adopting a larger platform first. It is also well known for red teaming workflows: prompt injection, jailbreaks, PII leaks, policy bypasses, and other AI security failures.
Best for
Engineering-led teams that want open, code-first prompt/model tests and security checks in CI.
Pros
- Evals can live in version-controlled config.
- Works well for PR checks and quality gates.
- Supports multiple output formats for CI artifacts and test reports.
- Strong fit for AI red teaming and vulnerability scanning.
- Easy to start with
npxand provider API keys.
Trade-off
Promptfoo is excellent for repository-centered evals, but teams still need an observability layer if they want production traces, sessions, cost rollups, and long-term trace debugging to drive the eval loop.
3. Braintrust: best for experiment-centric AI eval workflows
Braintrust focuses on evals, experiments, datasets, and comparisons. It is a strong fit when teams want every eval run to become an experiment that can be compared over time, including changes in score, examples, and output behavior.
This model works well for teams that want AI quality checks to feel like a development workflow rather than a pile of scripts. PR comments, experiment tracking, and side-by-side comparison make it easier to understand whether a change helped or hurt.
Best for
Teams that want experiment tracking and pull-request-centered eval comparisons.
Pros
- Good fit for comparing prompt, model, and code changes across eval runs.
- Strong experiment tracking mental model.
- Useful when teams need detailed score breakdowns rather than only pass/fail.
- Supports custom scorers and LLM-as-judge style evaluation.
Trade-off
Experiment tracking is valuable, but teams should still evaluate how production trace data, retention, self-hosting needs, and instrumentation model fit their application and compliance requirements.
4. Langfuse: best for open-source observability plus evals
Langfuse combines tracing, prompt management, datasets, experiments, and evaluation. Its evaluation docs frame evals across the AI engineering loop: score live traces, turn examples into datasets, run experiments, and judge results with manual or automated evaluators.
Langfuse is a good fit for teams that want an open-source LLM engineering platform with observability and evaluation in the same ecosystem. It can support online evaluation on traces and offline evaluation before shipping changes.
Best for
Teams that want self-hostable LLM observability with evaluation, prompt management, datasets, and experiments.
Pros
- Open-source option with broad LLM observability coverage.
- Supports trace scoring, datasets, experiments, and LLM-as-judge workflows.
- Useful for teams that want both online and offline evaluation.
- Strong fit when self-hosting is a hard requirement.
Trade-off
CI/CD setup may require more custom orchestration than a purpose-built CI action or a lightweight config runner. Teams should budget time for workflow scripts, secrets, baseline comparisons, and result reporting.
5. Phoenix: best for OpenTelemetry-oriented evaluation workflows
Phoenix, from Arize, is a good fit for teams that want open-source observability and evaluation built around OpenTelemetry and OpenInference concepts. It is most attractive when the team already thinks in spans, traces, embeddings, retrieval quality, and Python-based analysis.
Phoenix can be a strong choice for RAG and LLM observability workflows where teams want to inspect traces, evaluate retrieval behavior, and build custom analysis around open telemetry data.
Best for
Teams standardizing on OpenTelemetry/OpenInference and Python-first LLM observability workflows.
Pros
- Open-source orientation.
- Natural fit for trace and span analysis.
- Useful for RAG evaluation and retrieval debugging.
- Good match for teams already using Arize or OpenTelemetry-style instrumentation.
Trade-off
For CI/CD, expect to assemble more of the pipeline yourself: dataset selection, experiment execution, threshold checks, artifacts, and PR reporting.
Summary table
| Tool | Best for | CI/CD fit | Main trade-off |
|---|---|---|---|
| Currai | Trace-driven evals on prompts, agents, sessions, cost, and production behavior | Best when CI gates should connect to production traces and debugging | Requires instrumentation to get the full value |
| Promptfoo | Code-first evals and AI security testing | Strong config-driven CI workflows and quality gates | Needs a separate observability loop for production traces |
| Braintrust | Experiment-centric eval comparisons | Strong PR and experiment workflow | Fit depends on platform, trace, and hosting requirements |
| Langfuse | Open-source observability plus evals | Good evaluation concepts, often more custom CI wiring | More orchestration work for CI/CD gates |
| Phoenix | OpenTelemetry-oriented trace and RAG evaluation | Flexible for custom Python workflows | CI reporting and gating are more DIY |
How to choose
Choose Currai if your team wants AI CI/CD to connect directly to production traces, prompt versions, agent spans, sessions, user segments, latency, token usage, and cost. This is the best fit when failed evals need to be debugged in the same place as failed production runs.
Choose Promptfoo if your team wants a lightweight, code-first eval suite in the repo, especially for prompt tests, model comparisons, and red teaming.
Choose Braintrust if your team wants experiment tracking and pull request comparisons to be the center of the eval workflow.
Choose Langfuse if your team wants an open-source platform that combines observability, prompts, datasets, experiments, and evaluation.
Choose Phoenix if your team is already building around OpenTelemetry, OpenInference, Python notebooks, and trace-level analysis.
A practical CI/CD eval workflow
The tool matters less than the loop. A useful AI eval pipeline should look like this:
- Start with 20 to 50 high-impact cases from manual QA, support tickets, and known production failures.
- Add deterministic checks for things that should never vary: JSON shape, required citations, tool permissions, policy constraints, and backend state.
- Add LLM-as-judge rubrics for quality dimensions such as relevance, groundedness, faithfulness, tone, and completeness.
- Run the suite locally before opening a PR.
- Run the suite in CI on prompt, model, retrieval, and agent changes.
- Fail the build only on thresholds the team trusts.
- Attach trace IDs, prompt versions, model names, cost, latency, and git SHA to each run.
- Read failed examples before changing the gate.
- Promote real production failures into regression cases.
Do not start by trying to test every possible behavior. Start with the behaviors that would make you roll back a release.
FAQs
What is an AI eval tool for CI/CD?
An AI eval tool for CI/CD runs automated checks on LLM application behavior during development and deployment. It can test prompts, models, RAG pipelines, agents, tool calls, output format, safety behavior, latency, and cost before a change reaches production.
Should AI evals fail the build?
Yes, but only for trusted gates. Deterministic failures, security violations, format breaks, severe regressions, and calibrated judge scores are good candidates. New or noisy rubrics should report results first, then become gates after the team trusts them.
Are production traces better than static eval datasets?
Use both. Static datasets protect known cases. Production traces reveal the messy, changing distribution users actually create. The strongest workflow turns bad production traces into future regression tests.
What should I measure in CI for an AI agent?
Measure final outcome, tool choice, tool arguments, faithfulness to tool output, step count, latency, token usage, cost, safety constraints, and whether the agent stopped or escalated at the right time. For multi-turn agents, also evaluate the session, not just one response.
Why use Currai for AI evals in CI/CD?
Currai keeps evals close to the evidence. The same trace can show the input, prompt version, model call, retrieval span, tool call, output, latency, cost, session, and eval result. That makes failed CI checks and failed production runs easier to understand, compare, and turn into regressions.