Top RAG Evaluation Tools in 2026

Retrieval-augmented generation is now the default architecture for grounding LLM outputs in real data. But “grounded” does not mean “correct.” RAG systems fail in ways that generic LLM metrics were never designed to catch: retrieved chunks that are relevant but outdated, context windows that capture the right documents but miss critical nuances, and generated answers that are faithful to retrieved text but misleading in practice.

The stakes are measurable. A 2025 study found that Microsoft's Copilot provided medically incorrect or potentially harmful advice 26% of the time on questions about the 50 most-prescribed drugs in the US, including drug interactions and contraindications. A separate study found that even when RAG systems cite accurate sources without hallucinating, they remain “pragmatically misleading”: they decontextualize facts, omit critical sources, and reinforce patient misconceptions. Generic RAG metrics (faithfulness, relevance) would score these outputs as passing. Domain experts would not.

We reviewed seven platforms that evaluate RAG pipelines in 2026. They range from metric-dense frameworks with dedicated RAG scoring triads to general observability platforms with basic retrieval checks. The gap between them comes down to one question: can the tool tell you whether your RAG system retrieved the right content for your specific domain, or only whether it retrieved something relevant?

What makes RAG evaluation different

Standard LLM evaluation checks whether a single response is accurate and relevant. RAG evaluation adds retrieval-specific failure modes:

• Context relevance: did the retriever pull the right documents, or just semantically similar ones?
• Faithfulness: does the generated answer stick to retrieved context, or does the LLM inject unsupported claims?
• Retrieval precision vs. recall: is the system retrieving too many irrelevant chunks (low precision) or missing critical ones (low recall)?
• Domain-specific context quality: generic relevance scores cannot assess whether a legal RAG system retrieved the correct jurisdiction's statutes or whether a medical RAG system surfaced current clinical guidelines rather than outdated ones

Most platforms on this list handle the first three well. The fourth is where evaluation gaps persist, and where domain expertise becomes the bottleneck.

Platform breakdowns

1. Truesight

Truesight lets domain experts define what quality retrieval means for their field and deploys those definitions as automated evaluations, no coding required. Generic RAG metrics tell you whether retrieved context is relevant and whether the answer is faithful, but they cannot tell you whether the retrieved content is the right content for your domain. A medical RAG system might retrieve documents that score high on relevance but miss critical clinical nuances that a physician would catch immediately.

• Expert-grounded evaluation criteria for retrieval and generation quality
• Guided, no-code setup accessible to non-technical domain experts
• Live API endpoints deploy evaluations directly to production RAG pipelines
• Systematic error analysis surfaces retrieval failure patterns from data
• SME review queue for auditing edge cases where retrieval quality is ambiguous

Best for: Teams where retrieved content must meet domain-specific quality standards: healthcare, legal, finance, education.

2. DeepEval by Confident AI

DeepEval's five-metric RAG triad (answer relevancy, faithfulness, contextual relevancy, contextual recall, contextual precision) maps each metric to a specific RAG hyperparameter, so you know whether to fix your embedding model, chunking strategy, reranker, or prompt template. The DAG metric adds deterministic decision-tree scoring that avoids LLM-judge non-determinism. Python-only, and most metrics require LLM API calls.

• 5 dedicated RAG metrics, each tied to a specific pipeline component
• DAG metric: deterministic scoring without LLM non-determinism
• 50+ total metrics including hallucination, bias, toxicity, and multimodal
• Native Pytest integration for CI/CD pipeline evaluation
• Synthetic dataset generation for RAG testing without production data

Best for: Python teams that need actionable, component-level RAG diagnostics.

3. Braintrust

Eight RAG-specific scorers out of the box: context precision, context relevancy, context recall, context entity recall, faithfulness, answer relevancy, answer similarity, and answer correctness. The platform supports component-level evaluation, letting you test retrieval and generation independently by examining individual trace spans. GitHub Actions integration posts eval results on every PR.

• 8 dedicated RAG scorers covering retrieval and generation
• Component-level eval: test retriever and generator as separate spans
• Loop AI agent automates prompt and scorer optimization
• CI/CD integration with per-test-case regression tracking
• Per-organization pricing with unlimited users on all tiers

Best for: Teams that want the most RAG scorers out of the box with CI/CD quality gates.

4. Arize Phoenix

OpenTelemetry-native from the ground up, with retrieval evaluation built on the open-source OpenInference specification. Phoenix stands out for its UMAP-based embedding visualization, which lets you visually cluster retrieval results to spot semantic gaps and drift. Fully self-hostable with zero feature gates on the open-source tier.

• UMAP embedding visualization for semantic clustering of retrieval results
• Retrieval relevance evaluators benchmarked against HaluEval
• Precision@K and NDCG metrics for retrieval ranking quality
• Integrations with Ragas, DeepEval, and Cleanlab evaluation libraries
• Free self-hosting on Docker, Kubernetes, or AWS CloudFormation

Best for: Teams that need visual retrieval debugging with vendor-neutral, OTel-native instrumentation.

5. LangSmith

Four RAG evaluator types (answer relevance, answer accuracy, groundedness, retrieval relevance) plus integration with the Ragas framework for additional metrics like context recall. Datasets can include expected documents to fetch, enabling direct retrieval quality testing. The deepest integration remains with LangChain and LangGraph pipelines. Per-seat pricing at $39/month.

• 4 built-in RAG evaluator types covering retrieval and generation
• Ragas framework integration for context recall and precision metrics
• Datasets with expected-document fields for retrieval correctness testing
• Annotation queues for structured human review of RAG outputs
• Insights Agent clusters production traces to surface retrieval failure patterns

Best for: Teams already invested in the LangChain/LangGraph ecosystem for RAG.

6. Comet Opik

Built-in answer relevance and context precision metrics, plus Ragas library integration for broader RAG coverage. Opik's differentiator here is the Agent Optimizer, which can automatically refine RAG prompts and retrieval configurations using six optimization algorithms. Apache 2.0 licensed, and at $19/month the cheapest paid tier on this list.

• Answer relevance and context precision metrics built in
• Ragas integration for additional retrieval and generation metrics
• Agent Optimizer: 6 algorithms for automated RAG prompt refinement
• Online Evaluation Rules with LLM-as-judge for production RAG monitoring
• 40+ framework integrations including Dify and Flowise for low-code RAG

Best for: Teams focused on automated RAG optimization at the lowest price point.

7. Weights & Biases Weave

RAG evaluation through general-purpose scorers rather than a dedicated module. Local SLM scorers for hallucination detection and context relevance run without API calls, reducing evaluation cost. A RAG tutorial demonstrates LLM-as-judge approaches for context precision. Functional for RAG observability, but less specialized than the platforms ranked above.

• Local SLM scorers for hallucination and context relevance (no API cost)
• Custom scorers via @weave.op() decorator or class-based inheritance
• Online Evaluations for real-time production RAG monitoring
• Code, dataset, and scorer versioning for experiment reproducibility
• Integrations: LlamaIndex, LangChain, Haystack, DSPy, and 30+ providers

Best for: Teams that need RAG observability within an existing W&B experiment tracking workflow.

How to choose

The right tool depends on where your RAG pipeline is failing and who needs to be involved in fixing it.

• Choose Truesight when domain experts (physicians, attorneys, educators, analysts) need to define what quality retrieval looks like and output correctness matters more than retrieval mechanics.
• Choose DeepEval when you need to diagnose exactly which RAG component is underperforming (embedding model, chunking, reranker, or prompt) and Pytest is your testing framework.
• Choose Braintrust when you want the most RAG scorers out of the box and eval results need to gate your CI/CD pipeline.
• Choose Arize Phoenix when you need to visually debug retrieval quality with embedding visualizations and want free, vendor-neutral self-hosting.
• Choose LangSmith when your RAG pipeline is built on LangChain or LangGraph and you need annotation queues for human review.
• Choose Comet Opik when automated RAG prompt optimization is the priority and you need the lowest entry price.
• Choose W&B Weave when RAG evaluation is one part of a broader ML experiment tracking workflow and you want local scorers that avoid API costs.

Disclosure: Truesight is built by Goodeye Labs, the publisher of this article. We've aimed to provide a fair and accurate comparison based on each platform's documented capabilities as of February 2026.