Agentifact assessment — independently scored, not sponsored. Last verified Mar 6, 2026.
Ragas
Open-source evaluation framework purpose-built for Retrieval-Augmented Generation pipelines. Measures context relevance, faithfulness, answer relevancy, and precision/recall without reference labels. Integrates with LangChain, LlamaIndex, and any LLM via Python SDK. Offers automated metrics and experiment tracking to iteratively benchmark RAG quality in CI workflows.
Viable option — review the tradeoffs
You need to systematically evaluate your RAG pipeline's retrieval and generation quality without building ground truth labels from scratch.
Solid, actionable scores (0-1 scale) with low annotation needs; requires OpenAI/Groq API keys for LLM judge; testset generator creates evals but may need tuning for domain-specific data.
You want to integrate RAG quality checks into CI/CD or production monitoring to catch regressions early.
Fast per-eval (~seconds); batch sampling cuts LLM costs; reproducible via weighted metrics but sensitive to judge LLM choice.
LLM Judge Dependency
Metrics rely on an LLM (e.g., GPT-4) as judge, adding cost/latency and potential bias; not fully deterministic.
Ragas excels in RAG-specific reference-free metrics; DeepEval offers broader LLM eval metrics.
Pure RAG pipelines needing context precision/recall without labels.
General LLM apps with hallucination/faithfulness beyond RAG.
API Costs Accumulate
Each eval calls LLM judge multiple times; batch small samples or use cheaper models like gpt-3.5 to avoid surprise bills.
Trust Breakdown
What It Actually Does
Ragas tests the quality of AI search-and-answer systems by automatically measuring whether results are relevant, accurate, and trustworthy without needing labeled test data. It integrates into your development pipeline to track improvements across iterations.
Open-source evaluation framework purpose-built for Retrieval-Augmented Generation pipelines. Measures context relevance, faithfulness, answer relevancy, and precision/recall without reference labels. Integrates with LangChain, LlamaIndex, and any LLM via Python SDK.
Offers automated metrics and experiment tracking to iteratively benchmark RAG quality in CI workflows.
Fit Assessment
Best for
- ✓llm-evaluation
- ✓test-data-generation
Connection Patterns
Blueprints that include this tool: