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LLM evaluation · tools · 14 picks

Best LLM evaluation tools

Evaluation and testing frameworks for LLM and RAG applications — scoring, regression testing, and red-teaming.

Curated by @heyclaude-editors Updated 2026-06-19

Evaluation and testing frameworks for LLM and RAG applications — scoring, regression testing, and red-teaming.

Compared at a glance

The top 5 picks side by side on trust, install, platform support, and disclosed notes — full rationale for each below.

FieldDeepEval

Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces.

Open dossier 		Ragas

Open-source evaluation framework for testing RAG systems, prompts, agents, workflows, and other LLM application behavior.

Open dossier 		MLflow

Open-source AI engineering platform for tracing, evaluating, prompt-managing, and deploying agents, LLM applications, and ML models.

Open dossier 		Giskard

AI testing platform for evaluating, scanning, and monitoring machine learning and LLM application quality.

Open dossier 		Agenta

Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety · Privacy · Safety ✓ Privacy ✓
Categorytoolstoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backedsource-backed
AuthorConfident AIVibrant LabsMLflow ProjectGiskardAgenta
Added2026-06-032026-06-032026-06-032026-04-272026-06-03
Platforms
CLI
CLI
CLI
CLI
CLI
Source repo—————
Safety notes✓DeepEval metrics should be treated as regression and review signals, not proof that an LLM application is safe, correct, or production-ready. LLM-as-a-judge metrics can call configured model providers, consume quota, hit rate limits, and produce judge-model errors that need separate handling. Evaluation thresholds should be calibrated on real examples before they block deployments or trigger automated rollback, ranking, billing, or moderation decisions. Tracing instrumentation can wrap live application code, agents, retrievers, tools, and model calls; keep eval and production environments clearly separated.✓Ragas scores should be treated as decision support, not a substitute for domain review of critical outputs. LLM-based metrics can call configured model providers, so evaluation runs should be scoped and budgeted before use on large datasets. Generated test data and evaluator prompts should be reviewed before they influence release, ranking, or regression decisions.✓MLflow evaluations, traces, judges, and dashboards are review signals, not proof that an agent, LLM application, prompt, model, or deployment is correct, safe, fair, or production-ready. Autologging, decorators, OpenTelemetry ingestion, manual spans, and framework integrations can wrap live application code and record intermediate agent steps, retrievals, tool calls, model requests, and model responses. LLM-as-a-judge scorers and prompt optimization workflows can call configured model providers, consume quota, hit rate limits, and produce evaluator-model errors that require separate handling. AI Gateway and serving workflows can centralize model access, routing, rate limits, and credentials; incorrect configuration can route traffic to the wrong provider or expose more access than intended. Production tracing, async logging, tracking servers, registries, artifact stores, and deployment endpoints should be reviewed for authentication, TLS, network exposure, backups, and incident response before production use. Model registry and deployment workflows can influence real production behavior, so promotion, rollback, and approval rules should be separated from exploratory eval results.— missing✓Agenta can manage and deploy prompt or configuration changes, so production updates should go through review and rollback controls. Webhooks and GitHub automations tied to prompt or deployment changes should be scoped to trusted repositories and guarded workflows. Evaluation and online monitoring results should support, not replace, domain review for high-risk application behavior.
Privacy notes✓Test cases, traces, spans, prompts, actual outputs, expected outputs, retrieval context, tool arguments, metadata, and evaluation results may contain sensitive user or business data. LLM-based metrics can send evaluation payloads to the configured model provider unless a reviewed local model path is used. DeepEval documentation says evaluations run locally by default, while Confident AI login and cloud reporting are optional paths for centralized results. The official data privacy docs say DeepEval collects basic PostHog telemetry by default, including event names, metric names, notebook usage, an anonymous UUID, and public IP, with `DEEPEVAL_TELEMETRY_OPT_OUT=1` available for opt-out.✓Evaluation examples may include prompts, retrieved context, generated responses, references, and metadata from the application under test. LLM-based metrics can send evaluation payloads to the configured model provider unless a local model path is used. The upstream README says Ragas collects minimal, anonymized usage analytics; review or disable analytics where policy requires it.✓MLflow traces and evaluations can capture prompts, completions, retrieved context, tool arguments, tool outputs, spans, metadata, latency, token usage, costs, scores, datasets, expectations, and human feedback. Agent traces may contain customer data, private documents, source snippets, proprietary prompts, internal identifiers, secrets accidentally passed to tools, or model outputs that need redaction before storage or sharing. LLM-as-a-judge scorers, prompt optimization, AI Gateway calls, and serving endpoints may send prompts, outputs, context, or traces to configured model providers unless a reviewed local or private provider path is used. Tracking servers, backend databases, artifact stores, evaluation datasets, prompt registries, model registries, and exported reports should follow normal access-control, retention, audit-log, and deletion policies. Public demos, notebooks, and examples should not be copied into production workflows with real API keys, raw customer traces, unreleased prompts, or sensitive evaluation data.— missing✓Prompt records, variants, test sets, traces, model inputs and outputs, feedback, annotations, and evaluation results may be stored in Agenta. Hosted Agenta use sends that data to Agenta Cloud; self-hosted deployments still require retention, access-control, and backup policies. Review Agenta's sensitive-data redaction and retention guidance before sending production, customer, or regulated data.
Prerequisites
  • Python environment for installing and running the `deepeval` package in the project being tested.
  • Representative LLM test cases, expected outputs, retrieval context, traces, datasets, or golden examples for the behavior being evaluated.
  • Model provider credentials for LLM-as-a-judge metrics such as G-Eval, Answer Relevancy, or other configured metrics.
  • CI policy for which evaluation thresholds are advisory, which are blocking, and who reviews failures before release decisions.
  • Python environment for installing and running Ragas.
  • Test data, application outputs, or production-aligned examples for the RAG, prompt, workflow, or agent behavior being evaluated.
  • Model provider credentials when using LLM-based metrics or generated test data.
  • Python environment, package manager, or managed MLflow environment for installing and running MLflow in the project being traced or evaluated.
  • AI agent, LLM application, RAG pipeline, prompt workflow, model pipeline, or production trace source to connect to MLflow.
  • MLflow tracking server, backend store, artifact store, or managed service path sized for traces, datasets, prompts, model artifacts, and evaluation results.
  • Model provider credentials, gateway policy, rate limits, and budget controls for LLM calls, LLM-as-a-judge scorers, prompt optimization, and deployed endpoints.
— none listed
  • LLM application, prompt workflow, or agent workflow whose prompts and configurations need shared management.
  • Access to Agenta Cloud or a reviewed self-hosted Agenta deployment.
  • Provider credentials and a release policy for test sets, traces, prompt versions, and production deployment approvals.
Install—————
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Citations
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  1. 01
    tools

    DeepEval

    Python LLM evaluation tests, metrics, regression checks, and tracing.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    DeepEval is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  2. 02
    tools

    Ragas

    Open-source evaluation framework for RAG and LLM application testing.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    Ragas is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  3. 03
    tools

    MLflow

    Trace, evaluate, monitor, and manage agents, LLM apps, prompts, and models.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    MLflow is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  4. 04
    tools

    Giskard

    AI testing platform for LLM and ML quality.

    Review firstSource-backedReview firstAdded 2mo ago
    Safety · Privacy ·
    Why it made the cut

    Giskard is included because it has source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  5. 05
    tools

    Agenta

    Open-source prompt management, evaluation, and observability for LLM apps.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    Agenta is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  6. 06
    tools

    OpenAI Evals

    Build repeatable LLM and agent evals with OpenAI's open-source framework.

    Review firstSource-backedReview firstAdded 14d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    OpenAI Evals is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  7. 07
    tools

    Arize Phoenix

    Open-source LLM observability and evaluation tooling.

    Review firstSource-backedReview firstAdded 2mo ago
    Safety · Privacy ·
    Why it made the cut

    Arize Phoenix is included because it has source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  8. 08
    tools

    Evidently

    Evaluate, test, and monitor ML models, LLM apps, data quality, and drift.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    Evidently is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  9. 09
    tools

    Browserless

    Headless browser infrastructure for Puppeteer, Playwright, and AI agents.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    Browserless is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  10. 10
    tools

    Hugging Face Evaluate

    Load metrics, comparisons, and measurements for reproducible model and dataset evaluation.

    Review firstSource-backedReview firstAdded 15d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    Hugging Face Evaluate is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  11. 11
    tools

    Label Studio

    Open-source data labeling and human-in-the-loop AI evaluation.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    Label Studio is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  12. 12
    tools

    MCP Inspector

    Visual MCP server testing and debugging.

    Review firstSource-backedReview firstAdded 17d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    MCP Inspector is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  13. 13
    tools

    TruLens

    Evaluate and trace agents, RAG systems, LLM apps, metrics, and regressions.

    Review firstSource-backedReview firstAdded 16d ago
    Safety ✓ Privacy ✓
    Why it made the cut

    TruLens is included because it has safety notes present, privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

  14. 14
    tools

    Braintrust

    Evaluation and logging platform for AI applications.

    Review firstSource-backedReview firstAdded 2mo ago
    Safety · Privacy ✓
    Why it made the cut

    Braintrust is included because it has privacy notes present, source-backed source posture.

    Reach for instead

    If this will touch credentials, local files, or production systems, inspect the upstream source first.

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