The 4 best AI evals tools for running evaluations in your CI/CD pipeline in 2025

The best LLM applications aren't built through endless manual testing sessions. They're built through systematic, automated evaluation that runs with every code change. As AI engineering teams mature, they're discovering what software teams learned decades ago: continuous testing catches problems early, saves time, and ships better products.

The shift toward CI/CD-integrated evals represents an evolution in how we build with LLMs. Teams are moving beyond one-off evaluations to continuous validation that runs automatically with every deployment, giving them confidence that prompt changes, model swaps, and code updates won't degrade their application's quality. Early adopters are seeing the benefits: faster iteration cycles, fewer production surprises, and the ability to ship AI features with the same confidence they have deploying traditional software.

Recent adoption trends show that organizations implementing automated LLM evals in their CI/CD pipelines catch regressions before users do and maintain higher quality standards across deployments. This approach transforms evaluation from a bottleneck into an accelerator, enabling teams to move fast while maintaining rigorous quality standards.

What are AI evals in CI/CD?

AI evals (evaluations) in CI/CD are automated tests that measure your LLM application's quality, accuracy, and behavior with every code change. Rather than manually checking if your chatbot still gives good answers after updating a prompt, these tools automatically run dozens or hundreds of eval cases, score the outputs, and fail your build if quality drops below your thresholds.

This becomes important when you're not just running assertions but need LLM-as-a-judge evaluators, retrieval quality metrics, and complex multi-step agent evals. A feature might check if outputs contain certain keywords. A platform provides comprehensive evaluation frameworks that integrate with your entire development workflow.

Key trends shaping the space:

• Semantic evaluation: Moving beyond keyword matching to understand meaning through embedding similarity and LLM judges that assess relevance, factuality, and tone
• Agent-specific evals: Evaluating multi-step reasoning, tool usage accuracy, and whether agents converge on correct solutions (going beyond single LLM call validation)
• Production-ready automation: Tools designed for high-volume concurrent evals with rate limiting, caching, and failure reporting that integrates with GitHub Actions, CircleCI, and other modern pipelines

How we chose the best AI evals tools for CI/CD

When evaluating these platforms, we focused on features that matter for production-ready CI/CD integration:

• GitHub Actions integration quality: Does it provide a dedicated action, or do you need to cobble together scripts? How clean is the PR commenting?
• Evaluation capabilities: Breadth and accuracy of built-in evaluators (LLM-as-a-judge, retrieval metrics, custom scorers)
• Developer experience: How straightforward is it to define eval cases, run evals locally, and debug failures?
• Deployment flexibility: Self-hosted versus cloud options, especially for teams with data residency requirements

The 4 best AI evals tools that integrate in CI/CD

1. Braintrust

Quick overview

Braintrust is a complete AI development platform that brings production-grade evaluation directly into your development workflow. Built by engineers who've scaled LLM applications at companies like Google and Stripe, it provides native CI/CD integration through a dedicated GitHub Action that automatically runs experiments and posts results to your pull requests.

Best for

Teams who want eval results that integrate with their development workflow, not just pass/fail metrics. Braintrust excels when you need side-by-side comparisons of prompt changes, detailed experiment tracking, and insights that help you understand why outputs changed, not just that they changed.

Pros

• Dedicated GitHub Action with PR comments: The braintrustdata/eval-action automatically posts detailed experiment comparisons directly on pull requests, showing how your changes affected output quality with score breakdowns
• Experiment tracking: Every eval run creates a full experiment with git metadata, making it straightforward to trace quality changes back to specific code commits and compare results over time
• Cross-language SDK support: Full-featured SDKs for both Python and TypeScript with identical evaluation APIs, making it straightforward to run evals across your stack
• Built-in concurrency management: Automatic rate limiting and concurrency controls prevent hitting API limits during large eval runs, with configurable maxConcurrency settings
• Watch mode for rapid iteration: Run braintrust eval --watch to automatically re-run evals as you edit code, speeding up local development
• Comprehensive evaluation library: Built-in scorers for factuality, relevance, security, and more through the AutoEvals library, plus custom scoring support

Cons

• Self-hosting requires enterprise plan: While the platform offers generous free tiers, running your own instance requires an enterprise agreement, which may not fit teams with strict data residency requirements on a budget

Pricing

• Free: $0/month (1M trace spans, 1GB processed data, 10K scores, 14 days retention, unlimited users)
• Pro: $249/month (Unlimited traces, 5GB processed data, 50K scores, 1 month retention)
• Enterprise: Custom pricing (Self-hosted deployment, premium support, extended retention)

2. Promptfoo

Quick overview

Promptfoo is a developer-first, open-source eval framework. It offers a CI/CD integration through its native GitHub Action, CLI tools for GitLab CI, Jenkins, and other platforms.

Best for

Engineering teams who want full control over their testing infrastructure and prefer open-source tools.

Pros

• Fully open-source with no feature gates: Community version includes all core features for local evals, evaluation, and vulnerability scanning without paid upgrades required
• Native CI/CD support across platforms: GitHub Actions, GitLab CI, Jenkins, CircleCI, and more with built-in caching and quality gate support
• Security-first approach: Built-in red teaming capabilities for prompt injection, PII leaks, jailbreaks, and other vulnerabilities
• Configuration-driven evals: Define eval cases in YAML files that live alongside your code, making eval maintenance straightforward

Cons

• Requires infrastructure management: Unlike cloud platforms, you're responsible for hosting results, managing secrets, and maintaining the eval infrastructure
• Learning curve for advanced features: The YAML configuration can become complex for sophisticated eval scenarios with multiple providers and custom evaluators
• No centralized experiment tracking: Results are stored locally or in your CI artifacts. There's no platform for comparing eval runs over time or analyzing quality trends across deployments

Pricing

• Community: Free and open-source
• Enterprise: Custom pricing for teams needing centralized dashboards, SSO, and priority support

3. Arize Phoenix

Quick overview

Arize Phoenix is an open-source observability and evaluation platform built on OpenTelemetry standards, backed by Arize AI. It integrates with CI/CD pipelines through custom Python scripts and GitHub Actions workflows.

Best for

Teams interested in open source tools or already in the Arize ecosystem.

Pros

• Fully open-source and self-hostable: Deploy with a single Docker command, free with no feature gates or restrictions
• Built on open standards: Based on OpenTelemetry and OpenInference, ensuring your instrumentation work is reusable across platforms

Cons

• CI/CD integration requires writing custom code: No dedicated GitHub Action. You must write your own workflows using the experiments API and Python scripts, significantly increasing setup complexity compared to tools with native actions
• Evaluation features less mature than dedicated tools: While comprehensive, the evaluation library is newer compared to specialized eval platforms
• Limited experiment comparison UI: While you can run experiments, comparing multiple runs side-by-side requires navigating through trace views rather than a dedicated experiment comparison interface

Pricing

• Self-hosted: Free and unlimited
• Cloud (app.phoenix.arize.com): Free with limits
• Arize AX: Contact for enterprise features (HIPAA, custom dashboards, dedicated support)

4. Langfuse

Quick overview

Langfuse is an open-source LLM engineering platform focused on observability, prompt management, and evaluation. While it has some features outside of CI/CD, the CI/CD process is complex to set up.

Best for

Teams who want to self-host and aren't deterred by writing their own CI/CD integration to fetch traces, run evals, and save results.

Pros

• Flexible evaluation approaches: Supports LLM-as-a-judge, human annotations, and custom scoring via APIs/SDKs
• Self-hosting with no limits: Self-host all core features for free without any limitations
• GitHub integration for prompts: Webhook integration that triggers workflows when prompts change

Cons

• No native CI/CD action: Requires building custom evaluation pipelines. Unlike competitors with dedicated actions, you must manually orchestrate the entire workflow: write custom Python scripts to fetch traces, run evaluations, and save results back. This means setting up cron jobs or custom GitHub workflows yourself, making it significantly more complex than tools with out-of-the-box CI/CD support
• Evaluation runs separate from observability: Dataset experiments and production trace evaluations live in different parts of the platform, requiring you to switch contexts rather than having unified experiment tracking

Pricing

• Self-hosted: Free and unlimited for all core features
• Hobby (Cloud): Free (50K units/month, 30 days retention, 2 users)
• Core (Cloud): $29/month (100K units/month, 90 days retention, unlimited users)
• Pro (Cloud): $199/month (Unlimited history, higher rate limits)
• Enterprise: Custom pricing for SSO, advanced security, dedicated support

Summary table

Why Braintrust wins for CI/CD evals

The future of AI development belongs to teams that can move fast with confidence. While all these tools bring value, Braintrust's dedicated focus on CI/CD-native evaluation sets it apart. The platform automatically creates experiments in Braintrust with every eval run and displays comprehensive summaries in your terminal and pull requests, making it straightforward to track quality over time. Braintrust integrates with GitHub, CircleCI, and can be extended to others with custom eval functions.

What truly differentiates Braintrust is its experiment-first approach. Rather than treating evals as pass/fail gates, every eval run becomes a full experiment you can analyze, compare, and learn from. When an eval fails, you don't just know that something broke. You see exactly which eval cases regressed, by how much, and can compare side-by-side with previous runs. This transforms debugging from guesswork into investigation.

For teams serious about building production-grade AI applications, the question isn't whether to automate evaluation. It's how quickly you can get started. Braintrust removes the friction, giving you a dedicated GitHub Action that works out of the box, comprehensive evaluation libraries, and the experiment tracking infrastructure to continuously improve your LLM applications. The competitive advantage goes to teams who can iterate faster while maintaining quality, and that's exactly what Braintrust enables.

FAQs

How do I add AI evals to my CI/CD?

Start by creating a dataset of eval cases that represent your application's key scenarios (inputs paired with expected outputs or quality criteria). Next, define your evaluation metrics (accuracy, relevance, factuality, etc.) and set quality thresholds. Finally, integrate an evaluation tool into your pipeline: with Braintrust, add the braintrustdata/eval-action to your GitHub workflow file, configure your API keys, and the action automatically runs evals on every pull request, posting results as comments.

What AI evals platform offers the best CI/CD integration?

Braintrust provides the most comprehensive CI/CD integration with its dedicated GitHub Action that automatically runs experiments and posts detailed comparisons directly on pull requests. The action shows score breakdowns and experiment links without requiring custom code. Promptfoo offers good GitHub Actions support but requires more manual configuration, while Phoenix and Langfuse require writing custom Python scripts to orchestrate the evaluation workflow (significantly increasing setup complexity).

How do I test if changes in my PR make my AI agents perform better or worse on our evals?

Use an evaluation platform that automatically runs experiments on every pull request and compares results against your baseline. Braintrust excels here. When you open a PR, the GitHub Action runs your eval suite and posts a comment showing exactly which eval cases improved, which regressed, and by how much. You see side-by-side comparisons of outputs, score changes, and can click through to full experiment details to understand why performance changed before merging.