Here are solid alternatives to Ragas for evaluating RAG systems. The best choice depends on whether you want offline dataset-based evaluation, LLM-as-judge metrics, retrieval metrics, or production monitoring.

1) TruLens

Best for: end-to-end LLM/RAG evaluation with feedback functions

• Measures things like groundedness, relevance, context usage, and answer quality.
• Works well for tracing and debugging RAG pipelines.
• Supports custom feedback functions and LLM-based judges.

Pros

• Good observability plus evaluation
• Flexible metrics
• Nice integration for app-level tracing

Cons

• Slightly more setup than a pure metrics library
• You still need to define your evaluation strategy carefully

Website: https://www.trulens.org/

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2) DeepEval

Best for: test-style evaluation of LLM apps and RAG pipelines

• Provides unit-test-like evaluation for LLM outputs.
• Includes RAG-specific metrics such as faithfulness, answer relevance, context precision/recall-style checks.
• Good for CI workflows.

Pros

• Simple developer experience
• Good for regression testing
• Easy to automate in pipelines

Cons

• More “evaluation framework” than full observability platform

Website: https://docs.confident-ai.com/

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3) LangSmith

Best for: tracing, debugging, and evaluating LangChain-based RAG apps

• Offers dataset management, traces, and evaluation workflows.
• Can run LLM-as-judge evaluations on outputs.
• Very useful if you already use LangChain.

Pros

• Excellent observability
• Strong LangChain ecosystem support
• Useful for human review and experiment tracking

Cons

• Best experience is within LangChain ecosystem
• More platform-oriented than library-only

Website: https://docs.smith.langchain.com/

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4) Arize Phoenix

Best for: RAG observability, tracing, and evaluation in production

• Open-source tool for tracing LLM/RAG applications.
• Supports evaluation of retrieval, groundedness, hallucination, and more.
• Good for debugging retrieval quality and monitoring.

Pros

• Strong open-source option
• Good visualizations and trace analysis
• Useful for production diagnostics

Cons

• More observability-focused than offline benchmark focused

Website: https://phoenix.arize.com/

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5) OpenAI Evals

Best for: custom evaluation tasks and benchmark-style testing

• Framework for building your own evals.
• Good if you want to define task-specific metrics or judge prompts.
• Not RAG-specific out of the box, but very flexible.

Pros

• Highly customizable
• Good for internal benchmarks
• Works well for model comparison

Cons

• Requires more effort to build RAG metrics
• Less turnkey than Ragas

Website: https://github.com/openai/evals

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6) Promptfoo

Best for: prompt/model regression testing and evaluation harnesses

• Great for running test suites against prompts, models, and RAG outputs.
• Can compare outputs across models and prompts.
• Useful in CI/CD.

Pros

• Easy to use
• Great for A/B testing
• CI-friendly

Cons

• You may need to define RAG-specific checks yourself

Website: https://www.promptfoo.dev/

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7) Evidently AI

Best for: monitoring and evaluation of ML/LLM systems

• More known for ML monitoring, but can be adapted for LLM/RAG evaluation.
• Useful for drift, quality tracking, and reporting.

Pros

• Good reporting and monitoring
• Useful if you already use it for ML observability

Cons

• Less specialized for RAG than other tools

Website: https://www.evidentlyai.com/

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8) Semantic evaluation using embedding/search metrics

Best for: retrieval-focused assessment

If your main concern is retrieval quality, you can also use classical IR metrics:

• Recall@k
• Precision@k
• MRR
• nDCG
• Hit rate

You can compute these with:

• custom scripts
• sklearn / scipy
• IR libraries
• vector DB logs

Pros

• Transparent and easy to interpret
• No LLM judge required

Cons

• Doesn’t measure answer quality or grounding directly

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Quick recommendations

If you want a close Ragas alternative:

• DeepEval
• TruLens
• Arize Phoenix

If you want observability + evaluation:

• LangSmith
• Arize Phoenix
• TruLens

If you want CI/testing:

• DeepEval
• Promptfoo
• OpenAI Evals

If you only care about retrieval:

• Classical IR metrics like Recall@k and nDCG

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Suggested stack by use case

Small team / startup

• DeepEval for tests
• Promptfoo for regression checks
• LangSmith or Phoenix for tracing if needed

Production RAG app

• Arize Phoenix or LangSmith for observability
• DeepEval for automated evaluation
• Custom retrieval metrics

Research / experimentation

• OpenAI Evals
• TruLens
• Custom benchmark datasets

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If you want, I can also give you:

1. a comparison table of Ragas vs these tools, or
2. a recommendation based on your stack (LangChain, LlamaIndex, custom Python, etc.).